Top 7 Things to Watch When Moving from a Legacy Commerce Platform to the Cloud

Top 7 Things to Watch When Moving from a Legacy Commerce Platform to the Cloud

When a company first decides to move their commerce platform and supporting toolsets to the cloud, they typically focus most of their attention on deciding what type of cloud (private/public) and provider (AWS, GCP, Azure) they want, and then picking the supporting technologies (Spring Boot, Node.js, React, Kubernetes, etc.).

While these choices are important, they are actually less impactful to a successful migration than you might suspect. All of the providers and supporting technologies are mature: They provide what is required for a commerce platform to run in the cloud. Quite often, it’s the things that don’t get the attention they deserve — or get overlooked entirely — that can make or break a migration project.

This blog covers seven of these often-overlooked factors. The advice below applies to two common scenarios: Working with cloud-based components you own or manage, or interacting with third-party cloud components.

Whichever way you go, you need to pay close attention to these points:

  1. Strategy and roadmap. It’s imperative to develop a robust plan to coordinate the activities of team leaders, system architects, database administrators, coders and support personnel and have everyone aligned on the overall project strategy and roadmap. Also, understanding the TCO, ROI, and hard and soft savings should be included in the planning.
  2. Skillset management. Cloud components require specific skills and experience. VMware skills don’t always translate into Google Cloud skills. You have to address skill deficiencies as early as possible and manage them carefully before, during and after the cloud transition.
  3. DevOps. To get the most value from cloud e-commerce, you need a nimble development process and rapid software updates. Following DevOps principles and putting the design and architecture in place up-front in the migration helps keep all your moving parts coordinated.
  4. Performance. It is assumed that moving to an elastic, cloud-based environment with modern technology will improve performance. This is not always the case with both cloud platforms and headless architectures for ecommerce systems. After you have made the investment and delivered the project, the customer experience may in fact be worse if specific steps are not taken up front to optimize page performance. In addition, slow-loading pages can wreak havoc with search engine rankings. Designing and optimizing for performance up-front is mandatory.
  5. Monitoring. Because cloud components are beyond your direct control and somewhat abstracted, you need a comprehensive system monitoring setup. This includes monitoring, logging and alerting that will get people moving and enable them to fix problems as soon as possible.
  6. Design patterns. You must decide how to design the components of the platform. Cloud-based computing provides more flexibility and choices in design patterns, so defining and aligning all teams on the overall design patterns to be used will allow you to create a more cohesive, well-designed platform. Left to their own devices, different teams may end up building things in different ways, producing a confusing, convoluted cloud implementation. You need standard, coordinated design patterns to ensure all of your development efforts dovetail. Make sure you understand and assess your existing application landscape and pick a cloud architecture that meets your assessment.
  7. Dependencies. Third-party applications and data sources can gum up the works if you don’t have a coordinated program to deal with them. You must define dependencies and put in processes as a structural component of the migration effort to ensure success.

The DMI Advantage in Moving E-commerce to the Cloud

When your customer experience is on the line, you can’t afford unexpected glitches that chase people to the competition or projects that aren’t delivered on time and on budget. This reality underscores the inherent tradeoff of moving to the cloud: To get massive flexibility you have to deal with complexity and up-front cost.

At DMI, we have experts with direct experience in e-commerce operations across multiple retail sectors. We don’t tell clients what they need to do. We scrutinize their marketplace and current technology environment, and help them formulate a strategic, proactive approach to doing what’s best for their customers and their business.

It’s not easy to figure out. But it’s easier if you choose the right partner — one that is committed to the client’s success and knows how to successful navigate the unexpected pitfalls.

Andrew Powers, senior vice president, solutions delivery, digital commerce

How to Improve Your Call Center Today

How to Improve Your Call Center Today

It happens to every hospital or healthcare provider: A crisis or emergency erupts and suddenly patients are calling in droves with urgent questions — overwhelming your patient-care staff. You know this aggravates your patients and you want it fixed now.

You’ve probably heard about intelligent virtual assistants (IVAs), which use machine learning to automate a full range of activities in the patient support process. And you’re wondering: Can IVAs lighten the load on your call-center crews?

They can, but not soon enough to put today’s fire out. When you need help in time frames measured in days, not months, it’s best to think in terms of a journey to IVA, with small intermediate steps that lay the foundation for advanced automation a few months down the road.

While the initial phases do not put every fire out, they still provide priceless data about users’ habits. This data fuels intent-analysis processes that will help your IVA system make smart decisions down the road. Over time, user-intent signals will help you predict future patient support needs.

Thus, all of your patience and preparation early in the journey pay off all along the route. There’s no wasted effort.

These are three optimal phases for making the transition to IVA capability:

Phase 1: Add One Bot to Your Call Experience

It’s best to start with a single on-ramp in your journey to an automated patient experience. It could be a simple webchat bot that asks a patient one or two basic questions before forwarding the call to a human representative. This simple solution automatically eases the strain on your staff and doesn’t take long to implement.

Remember: Everything your patients do in this webchat bot will generate data that trains the IVA system to make smarter decisions as time passes. You can use any channel your patients prefer. The point is, you want to work the bugs out of the first automated process before moving on to more complex capabilities.

Phase 2. Automate Answers to Patient Questions

With your basic bot up and running, it’s time to add a few more on-ramps to your IVA journey.

That means using bots to answer more difficult questions and automate more of the patient support experience. You might also customize the responses depending on the channel — phones, email and text messaging, for instance.

Now, you have to focus more deeply on thorny issues like authentication: validating the identity of each person and remembering their favorite authentication methods.

Here, it becomes more important to train your support staff properly and confront any shortcomings in your backend IT systems. You need to make sure your legacy systems have enough power to process complex learning algorithms.

Again, you’re feeding data into your intent analysis process to ensure that each user can log in with their favorite methods in the future. Their questions and your automated answers will produce smarter bots, streamline the experience and improve patient satisfaction.

Phase 3. Implement an IVA System

Phase 1 and 2 gave you a firm foundation to implement an intelligent virtual assistant that can respond automatically to calls from a wide range of patients and tailor these responses to their unique preferences.

A full IVA system identifies callers and remembers their habits and behaviors on previous calls. Learning algorithms teach it to take on more complex challenges because it also folds in data from the rest of your patient calls.

The ultimate goal is to free up your patient-support staff to handle jobs that computers aren’t good at — like using their intuition and training to provide a savvy, humane response to a complex patient support issue. That helps improve job satisfaction, potentially reduce turnover costs.

The DMI Advantage in IVAs

DMI has an extensive track record with IVAs in multiple sectors like health care, finance and government. Our vast pool of talent in business consulting, automation, system architecture, machine learning, data science and patient experience ensures we have the right skills. Our focus on Agile methodologies gets the right solution into our clients’ hands in tight time frames. When you have big fires to put out, these are the skills you need.

–Niraj Patel, director, artificial intelligence

 

6 Things to Avoid in Your Headless Commerce Journey

6 Things to Avoid in Your Headless Commerce Journey

You can’t avoid every bump in the road to modernizing your e-commerce infrastructure. But you can anticipate the most common roadblocks, wrong turns and traffic jams.

E-commerce modernization usually means transitioning to a headless IT infrastructure. With headless, you decouple the frontend of your IT infrastructure — typically the presentation tier — from the backend, where most of the business logic resides. Headless lets you rapidly iterate your frontend to finetune your customer experience without having to coordinate everything with the backend, which would bog down development.

Avoiding these six pitfalls can make your journey to headless commerce go much more smoothly:

Strategic oversights. You can’t afford to scrimp on strategy in your headless commerce journey. That means starting with a discovery process and gap analysis that assesses your current IT state — systems, hardware, personnel, skills, partners and methodologies — and lays out a roadmap for moving toward a headless ecosystem that meets your current and future needs. The more planning you build into your strategic roadmap, the better your chances of success in the long haul.

Time-frame miscalculations. It’s easy to underestimate how much time is involved in a headless transformation. Every company has unique IT challenges and marketplace realities. You may have a small IT team that needs lots of help, or a large IT team that needs only highly specialized assistance. It takes a lot of fact-finding, research and meetings to figure everything out. That can take more time than you might expect.

Platform and software stack slipups. Missteps in choosing the right headless e-commerce platform or software stack for the head can cause headaches for years to come. Choose the platform that meets your current and future needs. Every software stack approach for building the head (presentation tier) has pros and cons. The best route is to draw up a list of requirements, assess your current IT skillsets and pick the software stack that gives you the most of what you need with the fewest compromises.

Skill misalignments. Conventional e-commerce platforms need abundant expertise in the backend, while developing the head on a headless commerce platform requires a separate set of frontend skills. Moreover, headless commerce requires a wealth of experience with cloud technologies and microservices. Your headless commerce journey must assess your skillsets across all the new technologies and plug any gaps you discover.

Methodology missteps. Headless commerce allows a rapid release and iteration cycle that requires continuous integration of skills from multiple disciplines beyond IT, such as UI design, digital marketing and product development. Agile and DevOps methodologies provide the framework for keeping everybody aligned during two- or three-week development cycles.

Botched buy-in.  Everybody must pull together. Otherwise, all the benefits of headless commerce collapse. That’s why it’s essential to secure buy-in from everybody involved in the transition — not only the leadership but also the rank-and-file technical teams and support people.

Picking a Partner for Your Commerce Modernization

Headless commerce delivers immense value in the digital marketplace, but you have to pay a cost in complexity to enjoy that value. To make it all work, you need a partner with deep experience in online commerce and broad technical expertise across frontend, backend and cloud software platforms. These are the capabilities that DMI brings to your headless commerce transition. Our consultants know how to merge commerce domain knowledge, IT infrastructure, marketing and user experience design to give our clients a head start in their headless transition.

—Atul Bhammar, senior vice president, solutions architect, digital commerce

How Headless Commerce and Magento Produce Amazing Shopping Experiences

This is the first in a three-part blog series about the use of Magento.

Headless commerce is changing the face of digital commerce because it allows companies to craft experiences that customers can’t get anywhere else.

A headless system decouples the frontend (typically the user interface layer) from the backend, where most of the computing power resides. This allows developers to customize user experiences for phones, PCs and other devices on the frontend while keeping a standard backend for the most demanding computing tasks.

Retailers are warming to headless commerce because it gives them a chance to set themselves apart in an era of relentless competition. Magento is an attractive option for headless commerce because it provides boundless flexibility to e-commerce system designers and managers.

On the front end. Magento allows frontend designers to leverage the powerful features of Adobe Experience Manager. Adobe has been building best-of-breed visual design software for decades. Adobe  Experience Manager has everything website and application designers need to craft seamless shopping experiences across multiple media platforms — smartphones, tablets, websites and more.

Before headless commerce came along, companies often had to choose an e-commerce platform on the strength of its backend computing power, regardless of any weaknesses in the frontend design system. There’s no more grin-and-bear it with headless computing. Designers can pick the best frontend tools for their specific needs. That leads to better experiences and happier customers.

In the middle. Magento has robust frameworks for the APIs (application programming interfaces) and PWAs (progressive web applications) that maximize the impact of headless commerce. APIs let multiple applications share data and communicate with each other (the GraphQL support added in version 2.3.x of Magento makes API communications more efficient). PWAs bring the functionality of a mobile app into a web page, providing app-like services without requiring users to download an app and update it themselves.

As customer experience developers implement more machine learning to customize and streamline online shopping, PWAs and APIs will become even more crucial to headless commerce.

On the backend. Rich consumer experiences powered by machine learning require substantial computing muscle. So, the same Magento flexibility that gives experience designers an edge also ensures that IT teams can implement the most powerful computing resources. Moreover, Magento’s advanced API frameworks ensure that retailers can plug their ecommerce systems into their systems for managing resources and customers.

While monolithic, all-in-one digital commerce systems are fading into history, Magento is leading the charge into the future because it helps developers get next-generation user experiences into the marketplace much faster. Bear in mind, however, that if your legacy backend system still performs to your satisfaction, you can keep it while leveraging all the frontend advantages of headless commerce.

A Quick Reality Check on Headless Commerce

The flexibility of headless commerce accelerates development timelines because you don’t have to align frontend development with your backend e-commerce computing infrastructure. But immense flexibility comes at a price: You have to invest time and energy in creating a well-thought-out roadmap to implement headless commerce.

That means taking extra care to account for variables like search engine optimization, bots masquerading as humans, proper analytics tracing and monitoring for errors that may no longer show up in traditional server application logs.

Magento has a global community of developers providing a galaxy of precise solutions to all manner of e-commerce challenges. With so many options available, a few stray missteps can undermine all your progress. At DMI, we draw on a deep well of experience in both the retail and the system development sides of the equation. We put these skills to use helping our clients craft an effective, efficient roadmap for implementing and managing Magento.

— Jon Wovchko, vice president operations & strategic consulting, digital commerce

Why it Pays to Transform Your Thinking about Data and Privacy

People need control over their data and transparency about how organizations use it. That’s the mindset companies must adopt in the new age of consumer privacy.

On the surface, it seems like a perfectly sensible, consumer-centered outlook. But the rise of anything-goes data gathering gave companies a lot of bad habits — lax security, opaque data policies, nettlesome behavioral tracking and sketchy third-party relationships.

That age is drawing to a close. Taking its place is an era where legislation like Europe’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA) is almost certain to proliferate in the years to come. Thanks to these new privacy rules, customers can discover how organizations use their data, forbid tracking of their online behavior and direct companies to delete or update the data in their servers.

Perhaps the toughest thing to get your mind around is that hundreds of pages of privacy legislation are actually excellent news. Though compliance is usually about as much fun as getting lost in a snowstorm, new privacy rules bring welcome clarity for companies using data to better understand their customers.

What’s more, you can take advantage of privacy rules to zero in on collecting the consumer data that drives the best outcomes. That’s a net gain over conventional data collection and analysis — if done correctly.

What We Missed in the Bad Old Days

The old way was to gather as much data from as many sources as possible, then use data science, analytics platforms, machine learning and other complex, expensive technologies to find the few grains of useful insight buried in a Gobi Desert of data.

It made sense in an era of easy access to consumer data. But it wasn’t exactly a sensible data strategy.

An effective data strategy starts with the information most directly relevant to your business. That is, you collect only the data that drives productive outcomes and advances your goals. This strategy is simpler and more cost-effective than hoovering up in everything, much of which is useless.

Getting Closer to Your Customers

In the new opt-in, permission-based world, you’re working with data from people who are interested in your product or service. Thus, the data they willingly share is inherently relevant to your objectives. Opt-in data is easier to segment for marketing purposes. It also gives you more specific, actionable information about the behavior of people using your applications and website pages.

Moreover, building online forms and folding them into a frontend data strategy is much easier to implement than developing massive back-end systems that require much more technical heavy lifting.

All of this can result in a more welcoming sense of intimacy in your customer experience. You can tailor offers to specific people and build more customization options based on the behavior of people who have agreed to engage with your organization.

Transforming Your Perspective on Privacy Isn’t Easy

Adopting a privacy-first data strategy requires a fundamental shift in perspective. Instead of gathering massive volumes of data on the backend, you’re crafting a robust data strategy on the frontend.

Making the transition successfully requires a convergence of skills in diverse disciplines, from SEO and email marketing to machine learning and system architecture. You also need a framework to persuade teams to adopt new methodologies and a roadmap for implementing the optimum technologies.

At DMI, we’ve developed powerful methodologies to help companies succeed in these kinds of transitions. We’ve learned from engagements with companies across every sector of the economy that managing change effectively is the bedrock of effective business transformation.

We believe that the current wave of privacy regulation represents an opportunity to develop data-rich, customer-centered websites, apps and marketing campaigns. The hitch is knowing how to make it work for your enterprise. That’s where our skills and experience pay off.

 — Michael Deittrick, senior vice president digital strategy, chief digital officer

Cracking the Code on Fan Engagement

In an age where consumers are in control, brands have to work harder than ever to cut through the noise to reach their consumers. Then they must also engage their audience in meaningful ways to earn their trust and loyalty. That means marketers are having to get creative. The brands that are willing to take a leap and do something bold are the ones that will reap the rewards.

When the International Spy Museum, a partner of DMI’s since 2014, was planning to move locations into a new, bigger, better, purpose-built facility across Washington, D.C., we knew we needed to do something big. The museum would be closed for more than four months for relocation, but this time was essential for generating buzz in advance of the grand opening.

Here are the four key principles we followed to build the wildly successful “Mission countdown” activation:

  1. Know Your Audience
    The first step in any marketing effort is to understand your audience. From owned data to surveys and customer interviews, knowing your consumer is essential. If you don’t know what their needs are, you can’t possibly deliver on them. If you don’t speak to them in the right way, or connect with them in the right place, they will quickly overlook you or worse, write you off entirely. Consider if you have an engaged fan base you can start with organically to validate your concept and get it off the ground, or will you need to build an audience from the start?

    For the Spy Museum, we had the benefit of four years of understanding their audience prior to the big move. We knew they had a core set of very loyal and very knowledgeable fans who followed the various Spy Museum social channels. We knew that if we wanted to connect with the fan base, we were going to need to speak their language. We had to create an experience that felt truly ‘insider’ and not like a marketing campaign.

    We decided to send our followers on their own undercover mission in the real world. Over the course of four weeks leading up to the opening of the museum, we released clues to 36 different dead drops hidden in the D.C. area that contained prizes from free museum tickets to access to the opening gala. To participate, fans had to crack clues to unlock the secret location of the dead drop, then go to that place to either collect the physical dead drop container or claim the digital dead drop pinned to that location. The technology we implemented enabled thousands of individuals to play at the same time while giving each their own unique experience, and allowing just 36 skilled agents to claim the prizes. The activation was a major hit with the Spy Museum fans, leading them to tag their friends to participate. It also generated a 28% increase in organic Twitter impressions month-over-month, as well as garnering earned media impressions to further build excitement and awareness for the museum.

  2. Be True to Your Mission
    Consumers are smart. They are skeptical of marketing. They only pay attention to the brands that truly connect with them, and they expect to be rewarded for their engagement and loyalty. So how do you earn the trust and attention of your audience?

    The key is authenticity.

    Don’t do a filmed stunt just because everybody else is doing it. Don’t post about an issue that is taking over social if your brand has no business being in that conversation. Start with your mission, and you can’t go wrong. Your brand is what it is, and if your marketing doesn’t represent that, the brand lift will be short-lived at best and tarnish your reputation at worst.

    For the Spy Museum, we could have done a simple ticket giveaway or social media contest. But none of that felt right for their brand. It took a tremendous amount of research and effort to create an experience that would surprise and delight the most knowledgeable spy enthusiasts. Each of the 36 dead drops was placed at a location where a moment in spy history occurred, like an actual dead-drop site, or a restaurant where clandestine meetings occurred.

    The 36 clues were all different puzzles, challenging our audience’s spy skills, like analyzing intel and cracking codes. This also enabled all players to engage, even after the prizes were claimed. The entire experience was carefully designed from start to finish to really put the audience into the shoes of a spy in the way that the new museum would upon opening. During a time the museum was closed, we actually turned the entire city into a museum where history came to life.

  3. Spread the Word
    It doesn’t matter how great your campaign is if nobody sees it. The rollout strategy is as important as the concept itself in creating a successful connection. You already know your audience from step one, now you need to meet them where they are. That may be your owned social channels, paid media placements or something else entirely.

    For the Spy Museum’s Mission, we released the clues in batches over the course of several weeks, giving more fans more opportunities to engage and win. We launched the game to the Museum’s followers first, giving our most loyal fans the first chance to win. Over the course of several weeks, we released more batches of clues and slowly began to use paid media to drive traffic. As the activation built momentum the earned press began, and as a result the final release of clues had the highest engagement of all the releases.

  4. Take Risks
    Fortune favors the bold. To get attention you need to do something exciting, new, fresh, even a little bit scary. Yes, the first time you do something it won’t be perfect. But consumers would rather you be exciting and not perfect than boring and expected, yet flawlessly executed.

In the case of the Spy Museum activation, we built an experience like nothing that had ever been done before using a technology platform that was new to the market. While we did everything we could to ensure success, there was still an element of uncertainty. But with great risk comes great reward. All 36 prizes were claimed. Most of them were claimed within just a few hours of the clues being released, one prize took just 23 minutes to be claimed. Thousands of existing fans were engaged, and thousands more became fans. Earned, owned and paid media generated more than 2 million impressions. Most importantly, the museum delivered an authentic experience and delivered on its mission to educate, even when their doors weren’t yet opened.

— Elizabeth Van Blargan, associate creative director, DMI’s Brand Marketing & Customer Experience Division

Why Blockchain May Prove Essential to GDPR Compliance

Blockchain may turn out to be the killer app for GDPR compliance in the 2020s.

How so? Well, blockchain creates a secure way to track the provenance of digital transactions. That aligns well with the demands of GDPR, the European Union’s General Data Protection Regulation, which requires companies to develop vigorous data-protection frameworks.

GDPR governs the digital privacy rights of everybody living in the EU. Companies outside the EU have to comply with GDPR if any of their digital transactions collect data about EU residents. That makes pretty much every website publisher and e-commerce site subject to GDPR rules.

GDPR compliance poses three primary challenges to companies:

  • Understanding who has access to people’s data. Within your company, you need to ensure that nobody can misuse personal data. Moreover, you must be compliant in any situation.
  • Staying transparent on your data policies. You have to tell users how their data is used and how they can help keep their data private.
  • Understanding where your data is going. You must document the paths data will take after you’ve collected it. If people’s data goes to third parties, you must be able to track it accurately.

Blockchain’s distributed-ledger architecture helps with all three of these GDPR requirements. It starts by encrypting an initial entry on a digital ledger. That data point cannot be erased or altered. Rather, any changes are added to the ledger and must be approved by everybody sharing it. This makes it extremely difficult to create fraudulent or fake entries.

All data has to be stored, transmitted and kept secure. Blockchain can be used to build an encrypted data trail so you always have a complete understanding of data storage, access and transmission. Moreover, you can increase transparency and build trust because you have a mechanism to keep your promise to protect users’ data.
It’s true that blockchain has generated more hype than results in recent years, but that’s often the case with promising new technologies. Inflated short-term expectations lead to discontent that obscures the long-term potential.

Sure, there’s a lot to work out. Blockchain is a sophisticated technology and GDPR is a complicated regulatory regimen. For all the uncertainties, one thing seems certain: GDPR and similar data-protection rules will grow more pervasive in the 2020s. And organizations will need all the help they can get to avoid fines and adopt transparent data policies.

At DMI, we’ll be looking for ways to converge the data-policy needs of clients, customers and regulators. If blockchain looks like the optimum GDPR solution for our clients, we’ll be putting it to work.

-Varun Ganapathy, director/digital technology office UK/Europe

Digital Twins: Mining the Potential of Real-Time Modeling

The appeal of a digital twin is obvious: You deploy a digital model that mimics real-world behavior — detecting inefficiencies, optimizing performance and learning to improve in real time.

Digital twins are starting to generate buzz in the tech sector because they represent the next phase in the evolution of computer modeling. Conventional digital models attempt to predict future results before building complex systems, anticipating problems in the virtual realm before they cause expensive trouble in real life. Digital twins build on these strengths by providing instant feedback and machine learning.

Fortunately, the sensors, networks, software and computers required to build digital twins are readily available. Indeed, they’re already finding a home in Industry 4.0 applications, where they’re streamlining production and enabling predictive maintenance.

Moreover, there are strong incentives to move these interactive replicas into new realms. After all, if digital twins can optimize machines, why can’t they improve human outcomes?

That’s where things get complicated.

Consider a sophisticated digital twin used by a professional sports team. It’s conceivable that wireless sensors in clothing and equipment combined with digital video cameras, 3D modeling and advanced learning algorithms could mimic an entire game while it’s being played. Sensors and software could detect which players are off their games and recommend improvements in real time.

This futuristic scenario is easy to envision. Embracing the full potential of digital twinning, however, is a lot more challenging.

Where to Start With Digital Twins

Pretty much any behavior in the natural world is a candidate for digital twin development. It’s not just people and machines. It could be weather, the flow of water or the accumulation of pollutants.

Thus, you’re looking at a veritable mountain of distractions and dead ends. To mine the nugget of productive opportunity with digital twins, you have to narrow your focus.

To do that, ask some basic questions: Do you have a process where you’re reasonably certain that a better understanding of customer behavior will improve revenues or profitability? Do you sell a complex product with a steep learning curve that would benefit from insights on user behavior?

Zero in on a few processes you’d like to optimize and build from there. Remember, machine learning is a core component of a productive digital twin. If you build it right, it will teach itself to work better over time.

It’s imperative to avoid bright-shiny-object syndrome. You have to start with a pressing need — not a nice-to-have.

4 Mandatory Skills for Digital Twin Development

After you’ve targeted your best digital-twin opportunity, you have to amass the skills required to make it happen. These skills span four disciplines:

  • Psychology. Like a designer of user interfaces or customer experiences, your digital twin must reflect and respond to cognitive processes that drive human behavior.
  • Physiology. A digital twin must account for the subtle interplay of nerves, muscle and bone during the use of a device or software application.
  • Architecture. System design drives the effectiveness of a digital twin. Your model must optimize the interactions of networks, sensors and learning algorithms.
  • Engineering. Computation combines statistical analysis with the logic of artificial intelligence and machine learning. Savvy engineering supplies the accuracy and relevance that produce success with digital twins.

Your digital twin solution will need equal measures of design creativity, strategic vision and technical acumen. Dovetailing all these skills is a non-trivial task.

Embracing the Future of Digital Twins

Admittedly, digital twin technology is in its infancy. In the years to come, innovations in edge computing and the growth of 5G wireless networks should expand its horizons. Advances in artificial intelligence, machine learning and neural networks will open even more opportunities.

Even if you don’t think digital twins are a good fit for your business today, it’s a good idea to monitor developments — especially in the consumer sector. If a breakout innovation hits the mass market, it could be as big as the smartphone.

We’re not predicting the future at DMI, but we do know promising technology when we see it. As we gather more insight and experience designing and implementing digital twins, we’ll make sure our clients benefit from everything we learn.

–Michael Deittrick, senior vice president digital strategy, chief digital officer

DMI Digital Leader Award – Alex Spinelli, LivePerson

DMI Digital Leader Award - Alex Spinelli, LivePerson

Creating AI-Powered Conversational Interfaces for the World’s Largest Brands

Tell us about the LivePerson mission.

Our mission is to make life easier for people and brands though trusted conversational AI. Leveraging decades of data and a powerful AI-engine, we develop conversational experiences for top companies including HSBC, Home Depot, Orange, and GM Financial, helping to ensure today’s consumers are served with the personalized customer experience they have come to expect.

Explain the “Fourth Industrial Revolution.”

The term “Fourth Industrial Revolution” represents the blurring of boundaries between the physical, digital and biological worlds. We are at the start of a convergence of advances in artificial intelligence (AI), robotics, the Internet of Things (IoT), 3D printing, genetic engineering, quantum computing and other emerging technologies. This convergence is the force behind many products and services that are quickly becoming indispensable to modern life, GPS for example. This is also paving the way for a seismic shift in the way people live day-to-day and the way business is conducted. We at LivePerson are excited to be helping to manage this change on behalf of our customers.

How is the “Fourth Industrial Revolution” affecting consumer behavior?

Differentiation based on brand is rapidly disappearing. As technology has enabled businesses to offer more connected experiences, today’s consumers have many more options and are not afraid to switch brands to find a better experience. In fact, 73 percent of consumers will give up on a brand if they cannot get an answer to their question within 10 minutes. Consumers have come to expect immediacy when it comes to brand engagement.

What percentage of consumer inquiries can be automated?

Nearly 70% of consumer inquires can be automated. What’s more, 90% of consumers begin their shopping journey online but in the U.S., less than 15% of sales are completed online. Consumers need to ask questions, get advice and understand their options before making their purchases. Conversational interfaces make this possible without the shopper ever having to visit a store or place a call. Today, 50 percent of all conversations (tens of millions) on our platform are automated; that’s a jump of almost 200 percent over past year-and-a-half!

How difficult is it for companies to implement conversational interfaces?

Building automations can be slow and fragmented with a heavy reliance on developers. However, our Conversation Builder is an all-in-one platform tailor-made for conversational commerce, giving brands a dramatically faster and non-technical way to implement automation. Additionally, team members, such as customer care managers and even qualified agents, can participate in creating and optimizing chatbots.

What role does messaging play in the future of commerce?

Today 13 million texts are sent every minute in the world. There are 100 million messages sent every day on platforms like WhatsApp and Facebook messenger. That’s astounding. Thus, many of the tasks we now do, such as shopping, will be shifted to messaging and voice platforms such as WhatsApp, Apple Business Chat, and Alexa. Conversation is just a much more natural way to interact with brands. For businesses to scale this, they’ll need the help of really effective conversational AI, which is where we come in.

What has your experience been like working with the DMI team?

We at LivePerson have been extremely pleased with the DMI partnership. The team’s technical expertise is impressive and we share a common vision and passion for digital transformation. Additionally, with DMI’s deep roots in the government contracting sector, we look forward to exploring opportunities to enhance the customer experience for U.S. federal agencies and the Americans they serve.

NRF2020: They Don’t Call it The Big Show for Nothing

Blog_Impressions_of_NRF_2020

My Impressions of the 15th NRF Expo and Show

As a technology professional focused on retail and commerce, this was my 15th NRF Expo and Show (but who is counting?). The venue is, of course, enormous, yet convenient. The content is breathtaking and well …breathtaking. The experience can be a bit like drinking from the proverbial fire hose. At certain times over the years, it was difficult to differentiate between an actual positive technology trend or just hoopla.

I have been here before to hear about everything from RFIDs, a new company named Amazon that was suddenly selling more than just books and CDs (remember those?), self-checkouts and the promise of kiosks, and of course, the “elusive” predictive merchandise and financial planning solution everybody seems to always be looking for. Here’s what REALLY impressed me this time around:

  1. AI is King. There were innumerable presentations of a vast number of “solutions” to predict, discover, deploy and “learn” from omnichannel customer transactions. Everybody was speaking of these as a “turnkey” solution that can produce immediate results. What gives? The best approach here is to match your AI strategy with a solid data cleansing and prioritization strategy. Trust me, you can’t have AI with bad data. The best solutions I saw incorporate AI to cleanse your data and then AI to decipher, understand and suggest actions based on that clean data.
  2. Customer Experience is Queen. So, you have clean data, you say? And a good AI engine to crunch it and learn from it? Well, now you need a frictionless experience for your customers. As some of my CX friends say, the journey is the experience and the customer is the journey. In other words, customers leave when they have a bad experience and they do not come back. However, when EVERY customer interaction is the result of thoughtful analysis, focused market testing, geocentric pricing elasticity, and is supported by a, “we are here for you at any time and in any channel you choose to interact with us,” you are way ahead of the herd.
  3. Game of Thrones. Well, the King and the Queen reign together. Companies that are successful in acquiring customers in current markets either by coming up with new (and more qualified) customer segments and the companies that are growing in new markets do the following: They are using AI and CX to their advantage. What exactly do I mean? Well, let’s say that you know that a high percentage of your customers use a messaging app (let’s say Whatsap), and you also know your customer care experience is not world class, and in fact, is costing you way too much. How would you address both issues? At DMI, we would strongly suggest that you look into Conversational Commerce. Chatbots are smart, fast and adaptable. They can enhance the customer experience by allowing your customer to communicate in the medium of their preference. Instead of calling to check on their order, they can text your customer care organization. Instead of downloading an app, or going to a browser to buy a product, they can instead text your brand (via SMS, Whatsapp, iMessage, etc.), and be in control of the touch point. There is no awkwardness about being put on hold. They can continue the conversation at a later time without losing their place in line. And it is 100% PCI-compliant. What do you gain? Consumers will come back for more convenience. You will get one more channel of commerce and of course, your CSR’s can become more productive with the help of the same chatbots by handling more sessions.

At DMI, we use technology to solve business problems — which creates a winning strategy. Of course, we have a lot of smart technical folks, but our focus is on how we can make your business successful. We care fanatically about how your customers and prospects perceive your brand for example. We make it our business to help you build the right ROI model for your particular project. And because of this, we have an army of very happy customers to back us up.

If you feel overwhelmed by the amount of stories coming out of NRF2020, get in contact with us at DMI. We will help you navigate the technology while we listen to YOUR story.

— Pablo Pazmino, senior client partner, consumer vertical

3 Ways that GDPR Makes Life Better for Everyone

3 Ways that GDPR Makes Life Better for Everyone

These days, it’s clear that the benefits of GDPR (the European Union’s General Data Protection Regulation) far outweigh the costs.

It wasn’t always this way. Remember the mad scramble in the first half of 2018 to comply with more than 250 pages of new privacy regulations? It was all about developing new data policies, assigning new people to compliance duty and adding new online features that helped people control the use and collection of their personal data.

Sure, compliance was drudgery back then. But now that we’ve had time to digest the impact of GDPR, the picture is far more positive in three essential ways.

  1. We have better data on online traffic

The first thing companies started noticing after complying with GDPR was that they knew a lot more about the people using their websites, apps and other online properties.

That makes it easier to segment audiences and deliver specific appeals and content to the people most likely to want them. Before GDPR, organizations had to sift through a lot more online chaff to separate current and likely customers from everybody else. That’s better for marketing and customer service, which strengthens businesses.

  1. Users have more control over their data

Reports of massive data breaches and private companies’ cavalier attitude toward their users’ private data have made the value of GDPR all the more evident. Indeed, the new California Consumer Privacy Act (CCPA) suggests that the future will bring much more scrutiny to the handling of personal data.

The wild frontier of taking anybody’s data and doing anything you want with it is drawing to a close. More people will have the legal right to decline collection of their data and to demand that it be deleted. That’s good for users and the people who serve them.

  1. There’s more trust and transparency

With GDPR, people can find out the kind of data that companies are collecting. They can refuse to allow cookies to track their behavior. They can find out how companies plan to use the data they collect. And they can feel reassured that potential fines for non-compliance have motivated organizations to take data privacy and governance seriously.

Growing concerns about cybercrime and unwanted surveillance undermine users’ trust in online properties. Providing transparent, easily accessible information on data-protection and privacy polices strengthens the bonds of trust between organizations and the people they depend on, whether they are employees, customers or vendors.

Data Protection is Central to a User-Centered Philosophy

DMI is devoting more time and expertise to helping clients in Europe and the UK comply with GDPR. Of course, we want to help organizations avoid fines and other regulatory actions, but there’s more at work here.

User-centered design starts with a deep understanding of how people navigate digital systems and what they hope to achieve. It’s only natural for users to want their data protected and their privacy secured. We see GDPR as a framework to support our philosophy of user-centered system design.

-Varun Ganapathy, director/digital technology office UK/Europe

Predicting Customer Intent: It’s All About Impact

Predicting Customer Intent: It’s All About Impact

Using predictive algorithms in customer care poses a challenge you might not anticipate: Accurate predictions don’t automatically generate better business outcomes.

When you’re automating elements of your customer care system, there’s a strong tendency to prize accuracy over everything else. After all, inaccurate predictions defeat the purpose of deploying artificial intelligence and machine learning.

But zeroing in on predictive accuracy is not the whole story — predictions have to be helping you improve the customer-care experience. Thus, you have to prioritize impact over accuracy.

Let’s take a look at how this works.

Predictive Modeling in AI: A Quick Overview

Machine learning algorithms comb through billions of data points, using pattern matching to distinguish between the outcomes you want and the ones you hope to avoid. With a large enough set of training data, the algorithms learn to create more accurate predictions over time.

All of this is built around complex mathematical formulas that analyze previous patterns of behavior and predict the likelihood of these things happening again. The more accurate these calculations are, the better your predictive capability.

Every time you accurately anticipate a customer’s behavior and help improve their satisfaction with an engagement or transaction, you’re producing better business outcomes. Thus, accuracy matters — as long as it’s generating the impact you want.

Deploying Predictive Models in Customer Care

In the call center, you want customers’ questions answered and complaints resolved as quickly as possible. With predictive modeling, you correlate data from multiple sources to understand the context of customer calls, answering questions such as:

  •  Are they on a landline, cell phone or web interface?
  • What have they purchased recently?
  • How much have they purchased, on average?
  • Where are they?
  • Does local weather affect their buying experience?
  • Have they made social media posts that are relevant to their call?
  • Do you know their age, gender, income and other demographic details?
  • What are the most intuitive traffic patterns through your customer-care interface?

You also have to track customers’ behavior throughout the customer-care journey. Do you force them to scroll through a long menu of choices, or do you use automation to anticipate their needs and reduce their menu options to one or two?

You also can create AI-enabled chatbots to assess exactly what the customer wants. If they want something that the bot can do easily with little risk of failure, then you keep the user within the automated voice interface.

If, however, the customer has a more complex challenge that requires human intervention, then your bot should forward them to the right person. Your customer reps should have all the data they need about the product and the caller to produce a happy outcome.

Why Impact is More Important than Accuracy
With a large enough dataset, a robust statistical model and a well-designed algorithm, you can accurately predict how people will answer the questions listed above. Indeed, you can expend considerable resources producing accurate predictions of their replies.

But you have only so many people, only so much budget, and only so much time to spend on automating your customer care system. Moreover, algorithms can provide uncannily accurate predictions that have no measurable influence on your business.

Thus, you have to think first about impact. Which predictions get customers what they want sooner? Which ones streamline your operations? Those are the kinds of questions your algorithms should answer.

The Smart Way to Automate Your Customer Care Journey

At DMI, we urge our clients to start with the goal of using AI to predict a single useful outcome. You don’t need a moonshot that automates your entire customer care system. You just need a solid foundation to build upon.

Learning to prize impact over accuracy is central to making that happen.

— Niraj Patel, director artificial intelligence

Why Convergence Will be the Make-or-Break Technology Trend of the 2020s

7 Technology Trends Impacting The Human Experience in 2020

They all have to come together — people and data, assets and inventions.

The 2020s will bring technologies like digital twins, swarm intelligence and virtual/augmented reality into the mobile mainstream. Fatter networks, faster computers and ubiquitous sensors will produce vast torrents of real-time data. All these advances, in turn, allow increasingly accurate predictions that reinforce the need for transformative technologies.

It’s tough enough mastering any one of these challenges. The future belongs to those who converge them all. At DMI, we’ve identified four pillars of digital convergence:

  • Human-centric engagement: Building systems that combine visual, touch and auditory signals to create frictionless user experiences.
  • Data-centric enablement. Using the best available data to analyze user behavior, anticipate their intent and personalize their experiences.
  • Leveraged investments: Getting the best performance from existing technology assets to avoid having to replace systems that already work well.
  • Next-gen empowerment. Inventing new services and systems that enable organizations to transform and disrupt.

We find that companies often excel on the human side but lag on the data side. Or they excel with data but need help developing human-centered user interfaces. DMI’s convergence framework acknowledges that companies often have worthwhile systems and might not require a rip-and-replace project. It also encourages clients to invent new, potentially disruptive products and services that strengthen their competitive position.

Any of these efforts is worthwhile on its own. But with technology evolving so quickly in the 2020s, organizations have to succeed at all four. That’s why our convergence framework pulls these forces into alignment.

What Convergence Looks Like

Consider the challenge of developing new pharmaceuticals. It takes years of effort in laboratories and clinical trials to bring effective cures into the marketplace, pushing costs into the stratosphere. Technologies that shrink development times while ensuring safe treatments can save lives and earn substantial fortunes for their inventors.

Technology convergence can help with:

  • Data centricity: Developing advanced, AI-enabled algorithms that accurately predict the efficacy of specific drugs with certain populations.
  • Human centricity: Adapting virtual- and augmented-reality applications and devices that help researchers visualize molecules, tissues, bones and other variables in real time and collaborate quickly to pool their knowledge.
  • Leveraged investments: Optimizing systems for cloud computing and data security to encourage adoption and hold the line on costs.
  • Innovative inventions: Building a SaaS platform that disrupts molecule discovery or drug manufacturing.

Excelling in one or two of these areas would have been a laudable achievement in years past. But in the years to come, there will be no choice but to do all four.

Converging in the 2020s in Every Industry

Complex industries like pharmaceuticals, health care and automotive manufacturing have strong incentives to embrace a converged digital transformation model. With upstart competitors bringing new ideas to market every month, incumbents can’t afford complacency.

But we believe the DMI framework applies to any kind of organization or enterprise. We built flexibility into our framework to ensure that companies of any age or size can find a model that suits their marketplace and business goals.

Just consider the virtual/augmented reality example: Call center representatives wearing 3D AR/VR goggles could envision the products they support from every angle, making it much easier to answer callers’ questions and then move on to the next customer. A human-centric system like this would require a sophisticated interplay of data, invention and leveraged assets.

At the same time, weaknesses in any of the four pillars of convergence would undermine everything.

A Convergence Partner

DMI’s converged framework accounts for the entire digital transformation process. We start with a sound technology strategy built upon the client’s precise business requirements and competitive environment.

From there, we build a strategic framework for organizational change to ensure that everybody who matters to the client — employees, customers, vendors, etc. — contributes to the transformation project. Once we’ve laid that groundwork, we create a roadmap describing how to get all these efforts moving in the same direction at the same time.

That’s where our skills and our clients’ priorities converge.

-Andrew Brockett, senior director/digital technology office

3 Strategic Keys to Driving Speed-to-Value in Agile Product Development

3 Strategic Keys to Driving Speed-to-Value in Agile Product Development

It’s not enough to produce speed and value in Agile product development. You have to provide speed TO value.

Making that happen requires a sound strategic footing. You have to understand why your company needs to use agile methodologies and how they will outperform conventional methodologies like waterfall.

Winning with Agile requires a sound time-to-value strategy. Here are three keys to formulating that strategy, based on DMI’s deep experience with Agile development across multiple industry sectors.

  1. Align Agile With Your Critical Business Goals

Agile cannot happen in a vacuum. It has to work in the context of your company’s primary goals. Moreover, Agile performs best when you can merge speed and value to do the most good.

If you’re getting disrupted by a well-funded startup, for instance, you have no time to waste on rigid waterfall methodologies. You need a fast, flexible route to value — whether you’re adding revenue, reducing risk or building market share. Agile gives you that.

However, waterfall may be the better choice if you expect minimal changes to a project’s scope, schedule and budget. You have to find the best fit for your specific objectives.

  1. Make Value Your Highest Priority

Agile development typically happens with small teams working in sprints of two to four weeks. Teams create a prioritized backlog of tasks to perform, tracking their progress on a burndown chart. While this approach is effective, focusing only on progress and productivity does not paint the full picture when Agile’s primary objective is to produce speed-to-value.

At DMI, we prioritize Agile development like this: Value > quality > progress > productivity. All of these factors are absolutely crucial to Agile success, but we find that progress and productivity don’t mean much unless they serve higher goals of adding value and upholding quality.

Thus, we emphasize delivering value supported by high-quality software with a minimum of bugs. Emphasizing value and quality also eliminates slip-ups that thwart progress and productivity.    

  1. Measure Your Progress

Agile doesn’t have the hard-and-fast structure of waterfall methodologies, so it can be difficult to quantify your success. But it can be done.

DMI developed a proprietary formula to measure the success of Agile teams. We named it APIX (Agile Performance IndeX) and we designed it to address a well-known phenomenon: measuring something influences your outcome. When you track Agile success only with burndown charts, for instance, you can emphasize progress even without enhancing time-to-value.

APIX measures Agile success by prioritizing value and quality over progress and productivity. Measuring Agile this way encourages teams to pursue the most valuable outcomes.

We customize APIX’s parameters to suit the needs of each client. Contact us to find out how to put APIX to work in your next Agile development initiative.

— Brian Andrzejewski, vice president, business transformation services

DMI Digital Leader Award — Pasquale Forletta, General Motors

DMI Digital Leader Award - Pasquale Forletta, General Motors

Leveraging AI to Get Vehicles to Market Quickly and SafelyPasquale

Tell us about your work at General Motors.

I’m proud to have worked at General Motors for 26 years. I recently began a new role leading Product Marketing for our Full-Size Vans and Light Commercial Vehicles. It’s an exciting space, as the number of fleets of corporate EV’s is rapidly growing thanks to the expansion of online shopping and a need for vehicles to deliver packages. Through the years at GM, I’ve also held operations leadership roles helping to build organizational effectiveness and driving continuous process improvements.

 We understand GM’s mobile inspection app is set to be rolled out in early 2020. Why was there a need for such an app?

General Motors sells over 100,000 cars a year in the used car space; many are rental return vehicles. There are numerous intricacies involved in the inspection of these vehicles to ensure they can be turned around quickly and safely to be sold online or at auction. We needed a solution to transition from our legacy digital picture-based inspection tool, which was not uniform across our inspection providers, to a new AI-based tool that could be deployed quickly to any supplier in any location. I’m excited to report that the proof-of-concept we worked on last year was successful and demonstrated significant efficiencies. The app is being rolled out to our 35 suppliers in February.

Describe the inspection apps’ functionalities.

Simply by scanning a vehicle’s VIN number, the app extracts data about condition history of the car. Inspectors have the ability to submit photos and comments via mobile devices. Thus, the technology allows inspectors to easily identify and report possible issues, including open recalls, before any time and money are spent on labor. Instead of waiting for paper forms to cross their desks, administrators are alerted to issues promptly so vehicles can get on the road more quickly.

What’s the most exciting part about seeing this proof-of-concept be deployed into production?

First, the inspection app can continually be improved. Being able to roll out a solution that empowers us to move quickly when we find issues or desire improvements is tremendous. The second most exciting part is the amount of money projected to be saved annually. In the wholesale market, speed and efficiency translate into significant cost savings for OEM’s and rental companies, as well as auctions and dealers. Timeline improvements benefit all segments of the supply chain.

What was your experience like working with the DMI team on development of the inspection app?

Through our work on the inspection app we’ve formed a trusted working partnership and great relationship with DMI. The team is highly-innovative. When we had challenges, it seemed like overnight they’d come back and say, ‘This is what we figured out. What about doing it this way?’ In the two years we’ve worked with DMI, I’ve never felt like we were getting a sales pitch. The team is super collaborative and creative. It’s been fun.

3 Questions to Ask Before Switching from Waterfall to Agile Development

waterfall

You have to be Agile these days. That’s what all the Agile true believers say.

But those are just words if you’ve used waterfall methodologies for years. Perhaps you’ve suffered through a worst-case waterfall project — wasting millions on software that’s obsolete when it hits the market. You know you need a better way, especially with technologies changing so quickly.

Agile methodologies have proven their superiority to waterfall across a vast range of use cases. But where do you start? All those books, articles, videos, blog posts and conferences devoted to Agile are overwhelming.

Before you charge headlong into the Agile ecosystem, it’ll help to answer three fundamental questions:

  1. Are you ready to prioritize time-to-value?

Everything about Agile boils down to producing value quickly. Agile teams of about 8-12 people build a high-quality minimum viable product (MVP) as quickly as they can — typically in less than a month — and then rely on user feedback to fix bugs and add features in future iterations.

This is a sharp departure from waterfall developments, which prioritize scope, schedule and budget. Waterfall works fine if you anticipate little or no change in a project. But change is inevitable with most technology projects, so you have to be able to adjust on the fly. Where should you adjust first? Agile says, “let’s prioritize the changes that drive the most value.”

  1. How will you address resistance to change?

Agile is a mindset, so you have to change how your people think about getting products to market quickly. That change starts in the C-suite and runs all the way through to your managers and technical teams.

A shift this substantial cannot happen overnight. You need training programs and a game plan to implement your changes. At DMI, we’ve helped dozens of companies make the shift to Agile and scale it over time. We’ve learned that even highly regulated companies like medical device manufacturers can thrive with Agile methodologies. Making your culture more Agile is central to making this happen.

  1. Can you measure Agile performance?

Metrics are the one authentic advantage of waterfall — you always have data on budget, scope and schedule. While these suit the needs of CIOs and their C-suite colleagues, these stats don’t measure the right things in the Agile world.

At DMI, we’ve learned you need to quantify value, quality, progress and productivity — prioritized in that order — to measure Agile success. In fact, we have developed a proprietary formula for Agile metrics that rival the clarity of waterfall data. If you’re interested in Agile metrics, ask us about APIX (Agile Performance IndeX).

Measuring the right things in the right way can make all the difference in Agile development.

— Brian Andrzejewski, vice president, business transformation services

Why AI will Drive These 4 Crucial IoT Trends in the 2020s

IoT

The internet of things (IoT) is a complex expression of a simple idea: connecting a sensor to the internet in any time or place where it’s useful.

But there’s one formidable challenge: How do you make sense of all that sensor data? The best answer is to apply artificial intelligence/machine learning (AI/ML) algorithms that automate the collection, storage and analysis of IoT device data.

IoT devices come in countless forms — phones, wearables, smart speakers, video cameras, location beacons and things that haven’t been invented yet. In theory, IoT applications can converge data from all these sensors to generate unique insights and unforgettable customer experiences. In reality, AI/ML is mandatory to make everything work together.

A quick look at three emerging connectivity technologies of the 2020s — 5G networks, geospatial commerce and smart-cities applications — illustrates the crucial role of machine intelligence in IoT.

5G Networks

The next generation of wireless networks will generate a massive boost in mobile bandwidth in the 2020s. That will flood the internet with rivers of new data. IoT applications will inevitably spring up to leverage this new bandwidth.

Mobile devices and remote locations will provide two of the most logical use cases for 5G networks. It won’t just be consumers downloading 4K movies to their iPads. It’ll be medical providers live-streaming patient data to specialists in far-flung locales. Safety inspectors will use video and AI to scan for hazards that still images miss.

These high-bandwidth applications can generate mega-volumes of data that enable predictive AI. Algorithms that accurately forecast future outcomes allow people to be proactive vs. reactive, which has immense value. Now, imagine subtracting the predictive-AI component of 5G and IoT. The value proposition is far less clear.

Geospatial Commerce

Smartphone apps and location beacons enable the creation of geofenced areas that can document the positions of people and products in three dimensions in real time.

Geospatial Applications have profound potential in the commercial realm. Sensors can detect when consumers enter a specific section of a store, and notifications can direct them to the exact location of a product. On a cruise ship or in an amusement park, families can coordinate their activities and use mobile maps to find their favorite attractions.

But think about how much data these applications generate. And consider the ethical implications of tracking people’s behavior on such a broad scale. AI algorithms are essential to crafting consistent, highly personalized user experiences that stay within the bounds of humane privacy guidelines.

Smart Cities

Smart cities represent the pinnacle of geospatial IoT applications. Sensors in traffic lights, bus stops and scooters will be able to work together to optimize traffic patterns and help urbanites travel to jobs, stores and entertainment venues.

In the public safety realm, IoT sensors and geofencing can help law enforcement agencies target their efforts where they can have the most impact. That could reduce the likelihood of unnecessary run-ins with people going about their business. Predictive AI could presumably make this kind of application even more effective.

While commercial IoT raises ethical concerns, smart-cities applications must deal with legal mandates. Again, this is an area where machine learning can help ensure that IoT initiatives conform with local ordinances.

The Power of AI, IoT and Analytics in the 2020s

5G networks and geospatial/smart-cities applications are only the most likely trends to emerge in the 2020s. More beguiling is the possibility that the application that pulls it all together does not even exist today.

Of course, a lot of uncertainties remain. Will 5G become pervasive enough to enable widespread IoT applications? Will public objections stall the progress of real-time behavior analysis? Will cities find the resources to embrace smart technologies?

In Conclusion

We suspect the answers to these questions will emerge from use cases — people and companies trying, failing and trying again to profit from ubiquitous connectivity. The people who master the combination of AI, IoT and analytics cannot be known today, but it’s safe bet that they’ll be household names before the decade ends.

— Niraj Patel, director artificial intelligence

AI: A Look Back And Where It’s Going

AI In 2019

As the decade ends, we are witnessing a tremendous acceleration in the utility and capability of artificial intelligence and machine learning as business tools. This trend will only continue as the calendar changes from 2019 to 2020.

At DMI, our clients and partners are already seeing the benefits of using new technologies. By putting learning algorithms to work to streamline operations and develop predictive capability, using chatbots and other conversational AI technologies businesses are opening up new avenues for improving customer service. While these trends have transformative potential, they also pose thorny ethical questions that cannot be ignored.

These questions will be critical to consider in the next year and decade to come as businesses grapple with the need to improve their processes with the moral imperative to respect their customers. There are a number of areas where these questions will play out.

Retail

Retailers are using AI to automate and streamline back-office applications to improve efficiencies: to control supply chains, to connect customers to purchases, to track and predict behaviors to improve the consumer experience.

Increasingly, we aren’t just using machines as tools – we are communicating with them. We ask and they respond, just as Alexa and Siri answer our questions, customers are able to get assistance from businesses ranging from banks to their doctor from machines, who have intuitively learned how to guide people to good decisions.

As impressive as these applications are, they challenge us to know their limits – to know when a machine’s capabilities are not up to the challenge, to know when a human being must answer the questions being asked, to know the difference between information and knowledge, between data and wisdom. Failing to recognize the limitations of technology can set up both businesses and customers for failure.

In the retail sector, we will continue to use AI to streamline the customer experience. One example of this will be tightening and improving supply chain processes.

In looking at customer service, using AI chatbots can make life easier for agents. Chatbots can process information quickly, handling more requests, while prioritizing particularly challenging questions. Some applications will have customers welcoming the AI, for example, people can be more comfortable talking to a bot about personal issues they’d rather not reveal to a person. In a more common scenario, you can automate simple queries like basic product information that a bot can deliver more efficiently than a human. As our survey revealed, resistance to AI happens on a continuum. You’re much better off embracing the low-resistance end of that range.

Government 

Government agencies face challenges the private sector does not privacy regulations and public accountability chief among them. Those challenges make deploy AI in government processes complicated.

Nevertheless, agencies are launching AI pilot projects and inviting companies to compete for a chance to participate. As these small projects expand, federal agencies will gradually add more AI/ML to their technology portfolios in 2020 and throughout the decade.  DMI’s work in this area can provide a roadmap for these efforts.

Ultimately, the federal government and the private sector reach their destinations via wildly diverging routes, but they share the same desire — using thinking machines to unleash the superior power of human cognition.

Finance 

While not the public sector, the finance world provides many of the same challenges. The paramount importance data protection, the need to protect institutions from legal liabilities and stiff regulations governing transactions and data.

While AI offers potential solutions, it can be taxing to seamlessly integrate these new tools.

Data comes from multiple sources in many formats. We have to assess whether the data is accurate or producing false-positives. If inaccurate data pollutes useful data, we have to scrub out the inaccuracies.

Conclusion 

Across the board, the business environment is becoming more integrated, more connected and more dependent on intelligent machines to think and learn and provide value for companies.

DMI is committed to moving forward with our partners across a variety of sectors in ways that best suit their business models. Helping them to cede a measure of control to machines while leaving ultimate authority in the hands of humans. Our experience with machine intelligence gives us the grounding to help clients find thoughtful, humane ways to strike that balance.

We want to help leaders maximize the potential AI can bring to their businesses. By harnessing the power of machine learning and artificial intelligence, and doing so responsibly, businesses can improve their own processes and the experiences of their customers. 

 

Why the Automotive Sector Must Become More Proactive About Technology Strategy in the 2020s

auto

Automakers face nonstop demands to embrace new technologies. One year, it’s smartphone integration. The next year, it’s big data. Then it’s artificial intelligence and machine learning (AI/ML).

In such a highly competitive industry, GM and Toyota can’t afford to let Ford and Volkswagen gain a technology edge. Thus, it’s only natural that industry players tend to be reactive to demands for new tech. The trouble is, companies across the sector have created patchworks of solutions and silos that are becoming increasingly difficult to manage. Technical debts are piling up amid mounting consumer demands and rapid-fire technology innovation.

In the 2020s, automakers will face a reckoning. They can’t just keep plugging in new technologies as the need arises. They need a more proactive, strategic approach:

Why will this shift be so important in the coming decade? These challenges spring to mind:

Social and Technical Convergence

Consumers expect their vehicles to be computing devices that anticipate their needs and improves their lives. Everything is getting smarter — from the speakers in people’s living rooms to the cities where they work and play. Technical behemoths like Google, Apple and Microsoft are getting into the connected-transportation business.

These and many more social and technical developments will pick up speed in the 2020s. But they won’t happen in isolation. They’ll converge. Automakers need a robust, sophisticated technology strategy to navigate this convergence.

5G Connectivity

Most cars rolling off assembly lines in 2019 still have 3G communication modules. Meanwhile, everybody in the technology sphere is gearing up for 5G. The best news for OEMs is that consumer adoption of 5G is at least two years away.

Sure, some consumers will use tethered 5G smartphones to bring high-bandwidth infotainment services into their cars within a few years. But it’s a safe bet that 5G won’t be a mainstream technology until mid-decade.

Nevertheless, automakers have to start planning for 5G in 2020. Given the long production cycles of the automotive industry, OEMs cannot risk getting outmaneuvered by disruptive startups. They need to be ready when 5G hits the mainstream.

Electrification and Automation 

Though self-driving vehicles pose immense technical challenges that may well delay Level 5 autonomy until the end of the 2020s, the evolution of in-car automation will bring a host of new driver-assist features. At the same time, electric cars and trucks will become increasingly common. Moreover, smaller battery-powered devices like scooters and drones could have a significant impact on mobility and transportation as the decade unfolds.

Advances in AI/ML and robotic process automation will be central to the rise of electrification in the coming decade. Learning algorithms, data science and advanced analytics have the potential to transform transportation, from sourcing to production to marketing to service. These are profoundly complex innovations that must dovetail with a forward-looking technical strategy.

Privacy and Regulation

Everybody in the transportation business needs to think deeply about how data is used, stored and protected. Customers and colleagues must be able to trust that tracking and personalization technologies won’t be misused.

As the risks of automation and connected travel start to rival the rewards, governments will get more deeply involved in creating standards and boundaries to protect people and their data. Complying with these rules will be a constant challenge for OEMs in the years to come.

DMI: A Partner with a Transformation Framework

DMI’s automotive industry experts know how to build delightful consumer experiences, implement automation and ensure compliance. These are tough jobs in their own right. Doing them together brings a quantum leap in difficulty.

That’s why we developed a converged framework that gives OEMs precise guidance on transforming their corporate culture while crafting an in-depth technology strategy. The framework starts with consulting workshops that help companies author a persuasive narrative for change. Next, it shows OEMs how to choose technical solutions that best fit their change narrative.

The technology reckoning of the 2020s will happen. DMI’s convergence framework gives OEMs the tools they need to confront it.

— James Bydalek, automotive and transportation, industry general manager

How Retailers Can Avoid the Pitfalls of Adopting Next-Generation Technologies

Retailers

Artificial intelligence can help retailers craft remarkable customer experiences. Natural language processing can improve customer service and spare employees from boring, repetitive chores.

It’s all good until you crash into reality. Customers and employees aren’t always excited about change. Some are outright resisters. Meanwhile, questions about next-gen technology’s financial impacts and return on investment can cloud the judgment of top executives and key decision-makers.

Retailers used to have a lot more time to overcome the pitfalls of adopting next-gen tools. But with disruptive threats coming at them from every direction these days, retailers have to adapt to change and adopt new technologies as quickly as possible.

Getting to ‘Why’ in Next-Gen Technologies

DMI developed a program called VisionNEXT to help companies rapidly adopt new technologies. VisionNEXT operates as a pair of workshops that help companies explore all of their options and find the most practical ways to embrace next-gen hardware and software.

The point of these workshops is to get you to “why.” That is, we help retail clients explore the crucial forces motivating their need to adopt the next generation of retail tech.

We’ve adapted this process to modern-day realities. The old people-processes-technologies framework that was popular for years worked best with monolithic systems and top-down management. In today’s world of VC-funded platforms and rapid disruption, you need a much more flexible model built around collaboration and consensus.

VisionNEXT helps companies craft a narrative around the imperatives of technology change. The goal is to persuade people that it’s in their best interest to adopt next-gen technologies. We recommend getting the program’s detractors directly involved rather than ignoring them and hoping they go away. Because time is of the essence, you can’t afford to let naysayers slow progress. But you also can’t afford to let their expertise go unused.

This kind of real-world perspective defines the VisionNEXT approach. We sit down with your technology leadership and help them chart a path to disruptive innovation.

Sharpening Your Competitive Edge

Nobody should be innovating for innovation’s sake. There’s too much time and money at risk. Instead, you should be finding the best ways to improve competitiveness and ensure adoption of new technologies.

DMI’s VisionNEXT workshops help you clarify the right and wrong ways to go. It’s not just choosing apps and targeting APIs. For instance, you can use the program to reassure employees that automation won’t take their jobs away.

When you know why you’re changing, it’s that much easier to persuade everybody else — co-workers, customers, vendors, directors — to adopt the right tools to get you there.

— Varun Ganapathy, director, commercial/consumer: digital technology office

DMI Digital Leader Award — Dave Feldman, Takeda Digital Products Lead

DMI Digital Leader Award - Dave Feldman

Partnering to Deliver Better Health and a Brighter Future

Dave Feldman and Chris Kent

Takeda Digital Products Lead Dave Feldman, left, with DMI’s Chris Kent.

What is the Takeda mission?
Takeda is an innovative, values-based global pharmaceutical leader that services the needs of patients and physicians worldwide. We’re proud to be a world-class R&D organization dedicated to delivering transformative therapies to patients. Our R&D efforts are focused in the following four areas: Oncology, Gastroenterology (GI), Rare Diseases and Neuroscience. We also make targeted R&D investments in Plasma-Derived Therapies and Vaccines. Takeda has approximately 50,000 global employees.

Tell us how Takeda first began to partner with DMI on digital transformation.
Our relationship with DMI initially began in Zurich more than two years ago when DMI supported Takeda in developing a Digital Center of Excellence to accelerate change across the enterprise. DMI was Takeda’s strategic partner responsible for all of the frameworks, governance, marketing, everything required to put our Digital Service Line in place. Since then, DMI has served as a trusted delivery partner in certain technology areas across the globe, including projects in Asia, Europe and the U.S.

Tell us about your role at Takeda.
For the past several years I’ve been responsible for all of Takeda’s collaboration tools across Microsoft Office 365. I help determine how Takeda can leverage its investment in Office 365 to drive solutions more quickly, whether they are for a finance group, clinical trial partners, etc., and ultimately, how we can derive business value for the organization. My new role as Digital Products Lead entails scaling services from the digital incubator phase to full enterprise solutions ensuring a maximum level of quality.

What is the Microsoft MVP (Most Valued Professional) Award and how does being a Microsoft MVP help you in your role at Takeda?
The Microsoft MVP Award grew out of the software development community and is given to technology experts passionate about sharing their technical expertise with the community, whether their knowledge is directly or indirectly related to Microsoft. I believe as a Microsoft MVP, one of the unique areas of value I bring to Takeda is knowing the right talent to hire. Being a member of the Microsoft MVP community connects you with an ecosystem of unparalleled technology peers. In fact that is how I met DMI’s Chris Kent, whom I frequently collaborate with in my role at Takeda. Chris is a fellow Microsoft MVP, as is DMI’s Corey Roth.

How is partnering with DMI a Value-Add for Takeda?
DMI brings a dedicated team whose technical expertise is second-to-none. What’s more, DMI team members work seamlessly, spanning business units and geography. We at Takeda can honestly say that from a customer perspective, we find the concept of “One DMI” to be right on target.

3 Ways AI Will Help the U.S. Government in the 2020s

The U.S. government won’t miss out on the wave of artificial intelligence and machine learning (AI/ML) coming in the 2020s.

That’s not to say it will be an easy ride. Federal agencies have to figure out how to accomplish ambitious AI/ML goals within the bounds of budget, personnel and political necessity. If they get it right, they’ll hand some tasks over to AI algorithms while freeing more people to work on solving human problems beyond the ability of machines.

These are three of the most likely ways that AI will help federal agencies in the 2020s and beyond:

Predicting Likely Outcomes

If AI/ML algorithms have enough data across a long enough time span, they can scan for patterns repeatedly and teach themselves to predict likely outcomes. Because the U.S. government has some of the world’s largest repositories of data, it’s well positioned to develop predictive AI. Likely use cases:

  • Vehicle maintenance. From compact cars to aircraft carriers, the federal government maintains massive equipment fleets. Each vehicle has moving parts that wear out. Predictive maintenance can tell fleet managers when to remove worn-out parts in advance, preventing breakdowns that delay critical missions.
  • Safety. Federal inspectors need to detect hazards in aircraft, mines, food-processing plants and many more applications. Computer-vision technology can analyze pixel patterns in still images and video frames to detect patterns that the human eye misses. With enough data, this kind of AI can make inspections much more efficient — targeting the most hazardous sites and predicting likely hazards, which can prevent accidents.

Tightening Security and Compliance

It’s easy to imagine government agencies using AI for the espionage and secret missions that fill so many TV and movie scripts. Some of that will be happening in the 2020s, of course, but with much less pulse-pounding drama. Examples:

  • Border and airport security. As computer-vision AI matures, agencies will get better at identifying and detaining potential bad actors. It may even be able to detect people trying to disguise their appearance. Along the border, computer vision can scan video feeds to detect likely entry points and give guidance on where to deploy personnel.
  • Regulatory compliance. Companies submit millions of PDFs and other documents in regulatory disclosure filings. Machine learning algorithms and Natural Language Processing will get better at scanning these documents to automate processing, streamline approvals, and improve accuracy.

Automating Manual Processes

Robotic process automation (RPA) is making waves across the private sector in factories, distribution centers and other commercial applications. The federal government is also getting into RPA. Examples:

  • Data entry. For decades, the government has collected data in multiple formats like PDFs, images, video, text and spreadsheets. Workflows that used to require manual processes to enter these documents into databases can now be automated, enabling the government to get more work done with limited staffing and budgets.
  • Conversational AI. Virtual assistants and chatbots will help government agencies make data entry faster and more accurate. That’s part of a broad move toward voice-driven automation that boosts the efficiency and effectiveness of public services.

What’s on the Horizon in 2020
Federal agencies face constraints that private businesses rarely endure. For instance, rules requiring data privacy and public accountability give the federal government a small threshold for failure. It’s no small challenge to develop AI systems in this environment.

Nevertheless, agencies are launching AI pilot projects and inviting companies to compete for a chance to participate. As these small projects expand, federal agencies will gradually add more AI/ML to their technology portfolios in 2020 and throughout the decade.

Ultimately, the federal government and the private sector reach their destinations via wildly diverging routes, but they share the same desire — using thinking machines to unleash the superior power of human cognition.

–Varun Dogra, chief technology officer

DMI Digital Leader Award — Rick Griskie, ADESA CIO

DMI Digital Leader Award - Rick Griskie, ADESA CIO

Transforming Auto Auctions for the Digital Age

What is the ADESA mission?
ADESA delivers wholesale vehicle auction solutions to professional car buyers and sellers. From data-driven research tools to comprehensive reconditioning services, ADESA simplifies the entire auto auction experience. ADESA is a business unit of global automotive remarketing and technology solutions provider, KAR Global. KAR Global has made strategic investments in acquiring, developing and improving technology that better fuels its online, digital and physical marketplaces to meet customers where they want to do business, providing them with smart, intuitive technology for a seamless customer experience. ADESA has 74 auctions throughout North America.

What are your strategic priorities as ADESA’s CIO?
As CIO, my biggest area of focus, opportunity and challenge is enabling the digital transformation of ADESA, KAR Global and the automotive remarketing industry. We are transforming the people, processes, tools, technologies and, most importantly, the culture of the KAR product development team. We’re leading an industry-wide transformation by implementing a Dev Ops culture and leveraging a hybrid cloud strategy to deliver industry-leading solutions with user-centric design concepts that enable a robust digital marketplace.

As CIO, how would you describe your management style?
Since joining ADESA nearly four years ago, I’ve implemented the Scaled Agile Framework (SAFe) and instilled a servant leadership management style across the KAR Product Development organization. Each of our scrum teams strives to deliver on our three primary metrics: Value, Velocity and Quality. This same approach was used to transform how our relationships with supporting business areas are managed. We quickly transitioned from a “staff augmentation” vendor management strategy to a partnership model. We have aligned accountability and authority. Now our partners have entire scrums teams working to meet their needs. These teams are held accountable in ways that allow ADESA to effectively measure performance relative to all other scrum teams. With this approach, we successfully track the quality and velocity of what’s being produced by all scrum teams across the enterprise.

Describe how ADESA has transformed its vehicle auction simulcast technology.

ADESA is leading the digital transformation of the in-lane buying experience. Nearly two years ago, we came to the realization that our current “simulcast” platform needed significant improvement and modernization. As a result, we kicked off an aggressive project to completely revolutionize ADESA Simulcast. We needed to leap frog over other industry offerings and provide a solution that offered the flexibility to create additional digital sales channels. This solution was one of the most complex projects undertaken by KAR Global during my tenure. It required complex integrations across multiple disparate legacy systems with multiple cloud-based solutions for the improvements needed to ensure extremely low latency (milliseconds) between in-lane bidders and on-line bidders. It also required a complete rewiring of all auction lanes in the 74 auctions across North America. In the end, the 18-month DMI-supported project was hugely successful. We completed the modernization one month ahead of schedule and under budget. We are extremely proud of our industry-leading technology.

Tell us why ADESA’s recent simulcast sale in Las Vegas was so innovative.
In September, we were thrilled to successfully pilot an ADESA Simulcast sale at Fiat Chrysler Automobile’s (FCA’s) national certified preowned vehicle dealer meeting. Vehicles were launched into auction from four ADESA auction locations — ADESA Golden Gate, ADESA Indianapolis, ADESA Kansas City and ADESA Las Vegas. Essentially, we brought the auction to more than 130 franchise dealers, empowering them to participate in a fast, live-bidding virtual auction. This format exposed dealers to a broader buyer base and also helped buyers cost-effectively access hard-to-find vehicle inventory. It was a win-win for all parties and helped introduce buyers to the next generation of vehicle auctions.

How would you characterize ADESA’s partnership with DMI?
DMI is one of our top providers of talent and an excellent partner in the consultant category. We’ve partnered with DMI at scale for two years and currently have about a half dozen fully-formed scrum teams. We’re pleased with the results and how the relationship is managed day-in and day-out. We have a very transparent relationship that works well. As a large organization, DMI has exceeded our expectations.

3 Key Factors Driving AI Trends in the 2020s

3 Key Factors Driving AI Trends in the 2020s

Artificial intelligence and machine learning (AI/ML) will move out of the shadows and into the mainstream in the 2020s.

AT DMI, we’re already seeing these forces at work in our engagements with enterprise clients. Increasingly, B2B companies are putting learning algorithms to work to streamline operations and develop predictive capability. Chatbots and other conversational AI technologies are getting more sophisticated, opening up new avenues for improving customer service. While these trends have transformative potential, they also pose thorny ethical questions that cannot be ignored.

Here’s a quick look at three of these factors.

Back-Office Adoption
AI/ML is moving into the B2B space in a big way. Retailers are using mobile apps, image recognition and robotic-process automation to streamline transactions and improve efficiencies in the storefront and the distribution center. As more consumers shop online but pick up their products in stores, retailers are turning to back-office AI to upgrade their supply chains and inventory controls to ensure these hybrid transactions don’t leave buyers empty-handed.

Intelligent bots also can track in-store traffic patterns to help retailers optimize their store layout and streamline service and checkout. In the automotive sector, back-office AI can help companies improve the driving experience and help dealers deliver offers to the customers most likely to want them. In the world of finance, AI is making it easier to develop more accurate assessments of credit risk and merger/acquisition potential.

Conversational Intelligence
Siri and Alexa were early chapters in the story of natural language processing. In the new decade, conversational AI will broaden its appeal in multiple sectors.

In healthcare, chatbots will increasingly know how to identify users and help them navigate to information they need right away. That can put patients in the driver’s seat — letting them decide whether to wait a half-hour to talk to a human or to read a few articles or support threads that solve their problems. Government agencies also will be keen to adopt these tools to bolster constituent services.

Companies like banks and consumer brokerages, meanwhile, will use more chatbots to help customers select investment vehicles, balance portfolios and map out their financial futures.

Ethical Quandaries
The accelerating pace of AI technologies creates vast potential but also must confront questions only human can answer.

Consider the possibility of an AI engine predicting the success or failure of a clinical trial. This raises a host of issues in the underlying modeling. How accurate is the data? How trustworthy are the recommendations? It’s no exaggeration to say these are life-or-death challenges.

AI is also enabling sophisticated data-tracking sensors that follow people’s actions and predict their behaviors. Should AI applications secure people’s consent before conducting these scans?

The problems of black-box AI — where users can’t be sure why a bot decides to act — require companies to develop traceability and clarity on the mechanisms driving AI bots. Failing to account for these kinds of challenges could be the one roadblock to the progress of AI adoption.

Moving Ahead with AI/ML
Though advanced automation has raised enthusiasm and unease in equal parts in recent years, inroads in the B2B sector reflect growing acceptance of these next-gen technologies.

At DMI, we’re seeing these trends across the major sectors we serve — automotive, financial, healthcare, government and retail. And we’re helping companies find the best way to adopt transformative algorithms while staying true to their values and their customers.

The coming decade will test everybody’s ability to cede a measure of control to machines while leaving ultimate authority in the hands of humans. Our experience with machine intelligence gives us the grounding to help clients find thoughtful, humane ways to strike that balance.

— Venkat Swaminathan, director, data analytics and artificial intelligence
— Niraj Patel, senior vice president, artificial intelligence

Safeguarding Covered Defense Information

The Department of Defense has made a requirement for all commercial vendors that are doing business to become Cybersecurity Maturity Model compliant, or CMMC. That means all DoD contractors will need to become CMMC certified by passing a CMMC audit to verify they have met the appropriate level of cybersecurity for their business. This will be a requirement for any organization who wants to hold contracts with the Department of Defense. 

Read on for the answers to some common questions about CMMC:

What are CDI and CUI? 

Covered defense information is used to describe information that requires protection under DFARS. Clause 252.204-7012. It is defined as unclassified controlled technical information (CTI) or other. 

Controlled Unclassified Information (CUI) is information that requires safeguarding or dissemination controls pursuant to and consistent with applicable law, regulations, and government-wide policies but is not classified under Executive Order 13526 or the Atomic Energy Act, as amended.

When do the certifications take effect?

The DoD has built upon existing DFARS 252.204-7012 regulation and developed the CMMC as a “verification component” with respect to cybersecurity requirements. The DoD has entrusted DoD contractors to achieve compliance, and with continued pressure to ensure 100% adoption of cybersecurity controls, the DoD is updating its policies.

Now is the time for contractors to get an assessment to determine where they stand regarding NIST 800-171 controls and the CMMC level they want to achieve to be certified by the second quarter of 2020.

In the fourth quarter of 2019, the DoD will release the CMMC Levels and their associated NIST 800-171A controls. The DoD will also announce the nonprofit that will be in charge of the certification process and will start training 3rd party certifiers.

What are the 5 CMMC levels and their respective requirements?

Level 1 – “Basic Cyber Hygiene” – In order to pass an audit for this level, the DoD contractor will need to implement 17 controls of NIST 800-171 rev1.

Level 2 – “Intermediate Cyber Hygiene” – In order to pass an audit for this level, the DoD contractor will need to implement another 46 controls of NIST 800-171 rev1.

Level 3 – “Good Cyber Hygiene” – In order to pass an audit for this level, the DoD contractor will need to implement the final 47 controls of NIST 800-171 rev1.

Level 4 – “Proactive” – In order to pass an audit for this level, the DoD contractor will need to implement 26 controls of NIST 800-171 RevB (still in the Public Comments stage)

Level 5 – “Advanced / Progressive” – In order to pass an audit for this level, the DoD contractor will need to implement the final 4 controls in NIST 800-171 RevB.

Buck Pierce, director solutions engineering, national security and defense

REthinking Disruption in the Retail Sector

We don’t have to let Amazon write the story of retail disruption anymore.

Of course, Amazon’s Prime Day and delivery deals require retailers to adapt. But all retailers now have the potential to disrupt. The tools and tactics of retail disruption are well understood. Indeed, legacy retailers can be just as disruptive as VC-funded start-ups.

Disruption typically happens via three avenues:

  • Platforms: Digital destinations where people buy, sell and collaborate. Examples: Amazon’s Marketplace and Apple’s App Store.
  • Crowds: Masses of people who buy products or services that solve a problem in their lives. Examples: Airbnb and the ride-hailing companies.
  • Machines: Technologies that improve people’s life experiences or make businesses more efficient. Examples: Smartphones and cloud services.

To shake up their industries, retailers can take any or all of these routes. That will require a mindset shift that might not be so easy for retailers who have spent years focusing on other priorities.

Consider one of the most popular retail metrics: ROCI, or return on capital invested. Retailers tend to obsess over ROCI because it helps them assess the value of investing in new stores. But in the era of disruption, you might not need any new stores. Instead, you might invest in digital technologies that are not capital in the conventional sense.

Remember omni-channel?  It was hot a couple years ago but already seems ready for retirement. It’s not that retailers won’t keep investing capital and selecting marketing channels. But they will have to adapt to an era where disruption is inevitable.

Getting the Right Perspective on Retail Disruption

Though platforms, crowds and machines are vehicles of disruption, they are not in the driver’s seat. As the author Thales S. Teixeira explained in a recent Harvard Business review article, customer dissatisfaction is the prime cause of disruption. Innovators identify gaps in customer satisfaction and launch services to close those gaps.

This concept gives retailers a straightforward question to ask: What are you doing across your entire value chain that has a positive impact on your customers? Keeping today’s customers happy is a short-term challenge — you also have to think about future customers.

You’ll need a framework to explore and adopt new retail technologies. And you’ll have to measure how these and many more factors affect your customers’ bond with your brand.

Retailers hoping to become disruptors have to remember the big picture. Disruption cannot be a quick fix. It has to be an engine of long-term growth, competitiveness and customer satisfaction.

At DMI, we’ve developed frameworks to help retailers evolve in an era of perpetual disruption. Our expert consultants and technologists can help you simplify your application portfolio and adapt your company culture to do more than keep the lights on.

With our help, you can take the lead and stay there.

 — Varun Ganapathy, director, commercial/consumer: digital technology office

Shaken, Not Stirred: How Geospatial Commerce Can Shake Up the Hospitality Industry

Picture yourself relaxing on the deck of a cruise ship, sipping your drink and enjoying the ocean breezes. When you need another drink, you call up the cruise line’s mobile app and order a refill. In a couple of minutes, a crew member brings a fresh drink right to you.

It can happen if the ship is wired for geospatial commerce to deliver customized services to the cruiser’s precise location. On a ship, geospatial commerce can leverage a wealth of crucial technologies:

  • A branded mobile app that delivers location-specific services.
  • Edge computing devices like locator beacons to deliver services rapidly to people’s phones.
  • Wireless networks connecting all the cruisers, plus the ship’s crew.
  • CRM software to collect and optimize information on each traveler.
  • Mobile commerce software to ensure safe, secure transactions.
  • Analytics to make sense of all the data flowing from these apps and devices.

Machine learning and other advanced algorithms to streamline operations and develop predictive capability.
Now, imagine you’re traveling on that cruise ship with your whole family: Location tracking could help you find where everybody is and send them a reminder to gather for dinner. The cruise line’s app could direct them to the deck and section where they need to be and tell how long the walk will take.

At dinner, you hear somebody at a table nearby singing the praises of the ziplining at the next port of call. Without leaving your table, you use your smartphone to sign your family up for the ziplining tour.
Multiply these activities by the thousands of people on the cruise ship and think of the benefits of collecting and analyzing the data passengers generate. Over time, services could be personalized to a degree that’s difficult to imagine today.

And it’s not just cruise lines. These next-generation technologies hold the potential to drive immense value across the hospitality industry, where individualized pampering can help companies and attractions sharpen their competitive edge. Theme parks, spas, resorts and family entertainment centers all can benefit from being able to track guests’ locations and deliver personalized mobile services.

At DMI, we’ve developed sophisticated technology strategies for these kinds of advanced retail services. Our consulting teams and technical experts can apply our Application Optimization Framework to simplify IT operations and encourage top-line growth. Our Innovation Hub for Retail helps companies strengthen their competitive standing and improve their company culture to embrace innovation and find ways to disrupt their industries.

Fortunately, these innovations do not require travelers to master intrusive, frustrating technologies. All they have to do is pick up their smartphone, tap on an app and start enjoying the fruits of geospatial commerce.

— Varun Ganapathy, director, commercial/consumer: digital technology office

DMI Digital Leader Award — Brian Zempel, Credit First National Association

DMI Digital Leader Award - Brian Zempel

Digitally Transforming to Build the Bridgestone Brand

Describe the Credit First National Association (CFNA) mission.

CFNA is proud to be a leader in customer-first payment solutions that earn loyalty and drive business in the communities we serve. We’re one of less than ten remaining special-purpose nationally chartered credit card banks in America. As the consumer credit division and payments arm of Bridgestone Americas, the CFNA payment program for tire services is offered at all 2,200 Bridgestone Retail Operations stores in the U.S. More than 5,500 Bridgestone affiliated dealers also accept the CFNA payment program.

How do you describe the disruption within the financial services world?

Today, everything in financial services is omnichannel. This includes how payment programs are presented, serviced and handled. If you’re not digital, you’re not anywhere. When I joined CFNA three years ago, we were just embarking on our digital transformation journey. We recognized that we needed to invest in enhancing our systems to grow our business and connect with customers where they are. DMI has been instrumental in enabling this effort.

How do you meet the changing needs of your customers?

Our vision is to create a customer experience that is on-demand and self-service. Customers today don’t want to pick up the phone and talk to a customer service agent to resolve an issue or obtain their statement. They want to do that online, and with ease. We’re also exploring developing an app to connect consumers to other products and services within the Bridgestone digital offering. We aspire to offer digital wallet services to securely store customer payment information so customers can purchase items online or with their phone.

As CFNA’s President, what are your business goals?

Our goal is to ensure we’re supporting Bridgestone’s vision to extend the brand. We’re doing this by working to make our payments program part of a loyalty program designed to grow our customer base and get consumers to shop preferentially with us because they know we can effectively help them take care of their vehicle. Consumers need payment options, and we want to be part of their wallet.

How is DMI delivering value to your organization?

As CFNA makes progress on its digital transformation journey, DMI is a strategic partner that has enabled us to transform our service offerings to better service our existing customers and grow our business. We are excited to continue working with DMI to further enhance our offerings and improve the customer service experience.

How to Measure Agile Performance — If You Set the Right Priorities

Agile can’t be measured. We hear that a lot from fans of waterfall development.

We respectfully disagree. It’s true that waterfall provides excellent metrics for benchmarks like spending against budget and progress along a timeline. But more often than not, waterfall development is too rigid to thrive amid today’s perpetual changes in markets and technologies.

That’s why DMI usually recommends an Agile approach to software development, which provides speed-to-value in ways that waterfall typically cannot deliver. We have to admit, however, that we have shared the frustrations of developers who craved useful data on Agile performance.

That’s why we developed APIX (Agile Performance IndeX), which offers the ability to quantify and score multiple benchmarks to help managers track the success of Agile projects. The numbers are a bit softer than the ones you get with waterfall projects. But they can be extremely effective when you apply them to a prioritized list of the things you want to measure the most.

We prioritized what we measure with the Agile index like this: value > quality > progress > productivity

Understanding these priorities — and putting them to work — requires a fundamental shift in perspective. Here’s a quick look at our thinking on these priorities:

Why value ranks first: Agile methodologies were developed to enable speed-to-value. You build a minimum viable product (MVP) as quickly as possible to start producing value — whether it’s revenue, market share, other business priorities or a combination of those things.  Then you use short development iterations and frequent customer feedback to keep making the product better. Thus, any attempt to measure the performance of Agile projects must place the highest priority on building value.

Why quality ranks second: Quality represents your ability to deliver an MVP with the most critical functionality and the fewest bugs. Quality has to rank below value because you have to deliver an MVP within a reasonable time frame at a sensible cost. You also have to reduce the risks of failure or delays. High costs, long time frames and overlooked risks all erode value and the total cost of delay grows exponentially over time. Thus, when you quantify value first and measure quality in the context of your value metrics, these numbers help drive success in the next two categories — progress and productivity.

Why progress ranks third: Progress is among the easiest Agile metrics to quantify. Burndown charts, for example, provide an excellent illustration of how far along an Agile team is. Because Agile projects tend to be difficult to quantify otherwise, Agile team leaders tend to emphasize progress. You measure what you can see, after all. But troubles can arise if teams make excellent progress on projects that are not delivering value and full of bugs and hence unlikely to deliver speed-to-value. Thus, progress must support value and quality.     

Why productivity ranks fourth: Productivity is among the most crucial measures of performance in any activity. The challenge in Agile projects is that measuring productivity can have unintended consequences. For instance, if you try to quantify the productivity of individual team members, you may miss their ability to help the entire team succeed. Productivity and progress can be interchangeable, depending on the needs of your organization and the skills of your team members. Regardless of your approach to these two metrics, you need to make sure they reflect their contributions to quality and value.

When DMI developed APIX, we acknowledged that metrics drive behaviors. People automatically adjust their priorities to meet numerical goals. Thus, we established our Agile index to prioritize the behaviors required for Agile success. We also built flexibility into our APIX parameters — we tweak them according to each client’s business requirements.

As we see it, progress and productivity are empty data points until they support quality and speed-to-value. In our experience, folding these numbers together in order of priority can deliver metrics that meet or exceed anything a waterfall project can deliver.

  — Brian Andrzejewski, director of business transformation services

AI in the Supply Chain: 7 Factors that Affect Success

The evolution of artificial intelligence (AI) and machine learning (ML) is redefining the supply chain.

Until recently, digital technologies reduced repetitive tasks and eliminated waste at each link in the supply chain — clearing an obvious path to ROI. But supply chains aren’t so straightforward anymore. Yes, supply chains still move parts to factories and goods to consumers. But they also use data and advanced algorithms to anticipate future demand, correlate with supply challenges and adapt to emerging competitive threats.

That’s a whole new kind of supply chain.

AI/ML projects have to take these new realities into account to drive value and avoid wasteful missteps. Keeping these seven factors in mind will help you do that:

  1. Next-level complexity. The quantity and direction of moving parts in supply chains is exploding. Automation in the form of learning algorithms provides the only hope of corralling everything. Our next six AI/ML issues illustrate the sources of supply chain complexity.
  2. Platforms vs. pipelines. Platform companies like Apple and Airbnb create many-to-many supply chains where buyers and sellers switch roles constantly. That’s a profound shift from the conventional, one-to-one supply chain that fed resources from mines and ports to production lines and distribution centers.
  3. Regulation and taxation. We can’t automate governments out of existence. But AI/ML can feed regulations, statutes and tax laws into algorithms and help companies ensure all the rules get followed and everybody pays the taxes they owe. Overlooking compliance issues in AI projects can produce expensive headaches that ruin ROI.
  4. Robotic process automation (RPA). We still need conventional supply chains to move products from producers to consumers. Learning algorithms can drive innovations in robotic processes that reduce errors and improve throughput in factories and distribution n centers.
  5. Natural language processing (NLP). Voice technologies can help produce better chatbots that streamline and personalize customer service environments. In the contact center, NLP also can scan audio files from every customer service rep to assess their success and recommend improvements. That can help boost compliance and elevate the customer experience.
  6. Recommendation engines. Algorithms can help consumers find what they’re looking for by scanning previous purchases and correlating them with their current circumstances. AI can recommend the product or service most likely to please the customer. This kind of technology used to be the sole province of giants like Amazon, but it’s rapidly becoming available to the rest of the retail sector.
  7. Implementation. With all these complications, AI projects live or die on the strength of their implementation. Missteps at the beginning of an AI/ML initiative can pollute everything that follows. To succeed, you have to tie everything into your business, industry, marketplace and customer expectations. That’s too much for any organization to figure out on their own.

DMI: A Consulting Partner for AI in Supply Chains

Just knowing where to start an AI project can be confusing and frustrating. At DMI, we recommend small pilot projects you can scale and evolve quickly once you figure out what succeeds.

But how can you tell where to start in a large enterprise? The best route is to join forces with a consulting partner who has a proven track record and a strong reputation for success in your industry.

That’s how DMI drives ROI with AI/ML in the supply chain.

Our Agile methodologies deliver fast prototypes and accelerated time to value. Our deep focus on areas like edge computing, 5G, data science and AI/ML helps our clients deploy the right mix of talent and tools for their unique needs.

With supply chains moving in new and unexpected directions, you need that kind of expertise to succeed with AI/ML.

— Varun Ganapathy, director, commercial/consumer: digital technology office

DMI Digital Leader Award – Mark Greer, Altec

DMI Digital Leader Award - Mark Greer

Partnering to Build a Next-Gen Utility Truck Fleet

Tell us about the Altec mission.

Altec has a 90-year history of providing truck-mounted equipment, such as aerial lifts, cranes, chippers, digger derricks, and cable handling equipment to the utility industry. Today, Altec provides products and services to more than 100 countries throughout the world. Since 1929, Altec has been a company committed to excellence. Our products are the industry leaders and consistently raise the bar through innovative design and integrated safety features.

How was Altec initially referred to DMI?

Initially, we were impressed by DMI’s status as a Gartner Magic Quadrant Leader in Managed Mobility Services, Global – four times running.  Additionally, I learned later that DMI has been recognized by Gartner six times. We find that when industry analysts provide their stamp of approval, it goes a long way toward building credibility with a new partner.

Tell me about your work with DMI on Digital Transformation.

We wanted to develop a digital platform to deliver the next generation of products and services that would connect our customers to their equipment. Since DMI leads the market in mobility solutions and connected systems, DMI has been integral to realizing our objective. Today’s world is increasingly data-driven. Our customers are seeking metrics surrounding equipment health, safety and productivity. Together, Altec and DMI have implemented a platform that empowers us to respond to our customers’ requests and provide meaningful insights to inform sound business decision-making for our customers.

What is the next step in your transformation?

The ultimate vision is to create and maintain a digital twin of our physical products; resulting in a higher level of support, optimization and automation as Altec equipment progresses through its operating life. The pairing of the physical machine and virtual machine serves multiple purposes; from systems monitoring to technical support to data analytics to improving and planning future products using digital simulation. Altec had the vision and desire to create the foundation for connected products and services that can be further developed and scaled to meet the expanding needs of our customers. DMI understands how a digital twin can help drive innovation and performance.

What has the relationship with DMI been like?

The DMI team had exactly the sort of experience that we were looking for in a partner. This allowed us to jumpstart the program as we developed and expanded our own capabilities. DMI provided the right people with the right skills at the right time. Any project of this magnitude will encounter some technical issues and DMI has worked side-by-side with our team to overcome challenges while continuing to move toward the objective. Working together, the team is consistently bringing creative ideas to the table and delivering high-quality results – and this makes our working relationship truly enjoyable.

DMI and Crayon Partner to Enhance Customer Experience

DMI is excited to announce our strategic partnership with Crayon Software to become a Microsoft Cloud Solution Provider (CSP) partner. From implementations and migrations to enhancing technology you already own, DMI is known for our strength in modernizing, developing and integrating enterprise strength applications and platforms. Now as a CSP partner, DMI will not only continue to support our clients with the best in class cloud solutions and services but also offer a more holistic experience that includes licensing, support and managed services all through one vendor.

As Microsoft continues to phase out the traditional Enterprise Agreements, Microsoft Cloud Agreements (MCA’s), and Server Cloud Enrollments (SCE’s), organizations are being challenged with how to most strategically and cost-effectively procure and manage their licensing. Through the CSP program, DMI can now offer our clients an agile, scalable buying model where they can scale up or down (not just up) based on their usage, pay monthly instead of being locked into long term commitments, and receive built in support, consolidated invoices and deep discounts.

As a Microsoft Gold partner for 15+ years, DMI is excited to expand our capabilities into in software licensing and additional services. A full suite of our ongoing Microsoft solutions and capabilities is featured on the DMI website.

— Michael Deittrick, senior vice president, strategy, chief digital officer

Why Engaging Your Detractors is Pivotal to Transforming Your Business

They’re inevitable. The lone holdout. The eye-rolling cynic.

They think they’ve seen it all before. They have to be dragged kicking and screaming into projects they consider questionable.

They are your detractors — and they are absolutely central to achieving your digital transformation goals. Why? Because they include some of your most valuable people. But you can’t afford to let their objections stall your progress.

Understanding the Crucial Role of Detractors

At DMI, we’ve made detractor outreach one of the pillars of VisionNEXT, our framework for helping organizations realize their dreams of digital transformation. We don’t underestimate the magnitude of a transformation initiative. Converting your company from a pipeline to a platform business model or using technology to disrupt your industry is a massive undertaking.

To succeed, you have to change the culture of your organization. And that requires changing the minds of your people while formulating a strategy to implement that change. VisionNEXT embraces this challenge, helping companies write a narrative explaining what they hope to achieve, why they hope to achieve it and how they plan to get it done.

The framework leans heavily on persuasion — using the language of logic, reason, ethics and emotion to create a unity of purpose among masses of people. Our reasoning: If your narrative wins over the detractors, you stand a much better chance of success.

We developed VisionNEXT to address one of today’s biggest challenges: Innovation happens so quickly that you must be able to adjust your programs on the fly. Otherwise, you can spend 18 months developing a program, only to have technologies or disruptive competitors make all your efforts obsolete.

You have to move at a rapid clip, using agile methodologies that rely on consensus and iteration to stay current. In an environment where three-year time frames have shrunk to six months, it makes no sense to let critics and naysayers spread doubts and gum up the works.

But it’s also useless to ignore their potential to be powerful contributors. Detractors usually have the know-how; you just have to shift their mindset. Outreach helps you do that.

Why Detractor Outreach is a Must

Detractors are one of three key audiences in DMI’s VisionNEXT narrative.

The other two audiences are the beneficiaries of the change you propose (customers, employees, etc.) and the natural advocates of change (executives, investors, etc.). It’s comparatively easy to frame a narrative that appeals to customers, who always want better products and services, and advocates, who want their company to be on the leading edge of economic trends.

Detractors are another matter. They are independent thinkers who don’t play get-along, go-along. They might be some of your smartest, most skilled employees. Their knowledge and abilities can make them incredibly influential. Their doubts can infect your whole program.

The conventional wisdom is to ignore the naysayers and move ahead with your program. If they don’t like it, that’s their problem.

The trouble is, rapid-fire change makes their problem your problem. You need all the skill you can muster. If some of your best people can’t get with the program, ignoring them is out of the question.

You have to engage them.

What Detractor Engagement Looks Like

In practice, VisionNEXT is a series of workshops that help companies develop an in-depth action plan for digital transformation projects. The first half of the framework builds a narrative for change, while the second half formulates a strategy to implement it.

In one recent VisionNEXT workshop, we had a person who did not want to be there. The cynic wasn’t some grumbling underling — but a corporate VP who had orders from on high to see what we had to say. The expectations were low.

Four hours later, the skeptic had completely changed. This nay-sayer suddenly realized the need for transformation – in this case from manufacturing into a service business. That’s the potential of detractor engagement.

Addressing detractors is baked into VisionNEXT. Our framework encourages you to engage with detractors, seeking their input and giving them critical assignments that make full use of their critical-thinking skills.

You Need Everybody On Board

Most detractors won’t do a 180-degree turn like the VP in our recent workshop. Grudging acceptance might be the best you can hope for in some cases.

Nevertheless, you can persuade detractors to see the logic and reasoning driving your transformation initiative. If you involve them in the process and demonstrate that they are crucial to its success, you stand a far greater chance of them getting with the program. And there’s less risk of them raising objections or conducting whisper campaigns that grind progress to a halt.

Detractors have plenty to offer. You just have to position yourself to encourage their contributions.

–Michael Deittrick, senior vice president, digital strategy, chief digital officer

3 Keys to Understanding the Power of Edge Computing

We need edge computing because faster processors and fatter bandwidth can’t fix everything.

Edge technologies run the gamut from smartphones to IoT sensors, location beacons and hyper-converged infrastructures. They’re appealing because they can squeeze the power of the cloud into microprocessors at the network edge. Edge devices enable real-time processing that overcomes the challenges of network latency. Edge networks also let devices talk to each other without connecting to a central server, which is crucial in areas that lack broadband connectivity.

Edge computing is not a shiny object to dive into because everybody else is talking about it. Rather, it’s a new generation of distributed IT architecture that outperforms the cloud in specific scenarios. These three points will help you separate the signal from the noise about edge computing:

1. Edge computing elevates the human experience

Imagine taking your family — kids, parents and a couple of cousins — on a two-week ocean cruise. Everybody has a smartphone that keeps real-time data on their location. If the ship has a network of location beacons, then you can track everybody’s locale, muster them for dinner and get all the kids back to their cabin by bedtime.

Cruise ships can use satellite links to connect to the internet backbone, but the latency in satellite data streams makes conventional cloud applications impractical. A remote site like a coalmine faces similar challenges: Edge devices improve safety for everybody without a real-time internet connection.

Edge technologies can help you deploy sophisticated real-time services using AI, data science and analytics software in domains like industrial IoT projects or smart-cities projects. No matter how you build an edge network, the common thread is to give more computing power to people regardless of their distance from the data center.

2. Edge projects can start small — at low cost

Edge computing doesn’t require you to bust your budget on servers, switches and cabling. Indeed, some edge devices like smartphones and wearables use microprocessors people have already purchased.

The edge is the latest generation of distributed computing, with one crucial innovation: System developers don’t have to draw up the entire network architecture in advance and provision a data center, networking gear and hundreds (or thousands) of PCs. Edge projects can be launched in small pilots to prove what works and rule out unworkable ideas. Edge networks can grow organically over time, adding and subtracting technologies as needed.

This goes back to the human experience that’s so pivotal to edge networks. It’s much easier to adapt your technology to people’s changing tastes and habits. And you’re less likely to get stuck with equipment that has become obsolete.

3. Business strategy drives success at the edge 

It’s natural to be a bit skeptical about edge computing. Aren’t we just trying to sell you more technology?

Not exactly. Edge networks respond organically to people’s ever-changing needs and desires. That means the marketplace creates demand for edge services. Traditionally, IT directors have been operational — focusing mainly on finding and managing IT people and devices. In the era of edge computing, IT directors will have to be much more strategic, aligning their teams’ capabilities with demands emerging from the business side.

In Conclusion

These three points underscore why we’re so enthusiastic about edge computing at DMI. First, we build human-centered design philosophies into everything we do. Second, our vast experience in cloud and network development helps us craft small, economical pilot projects that are easy to adapt and scale over time. And, finally, our consulting approach applies all of our clients’ business realities to the solutions we develop. Everything flows from a client’s strategic business needs.

 -Michael Deittrick, senior vice president digital strategy, chief digital officer

DMI Digital Leader Award – John Heveran, Liberty Mutual Insurance

DMI Digital Leader Award - John Heveran

Creating Digital Personas to Keep that Personal Touch

Tell me about your role as Digital Leader.
I’m John Heveran, the Senior Vice President and Chief Information Officer of Global Risk Solutions for Liberty Mutual Insurance, responsible for serving our large enterprise customers. Since 1912, we at Liberty Mutual Insurance have provided competitively-priced insurance products and services to meet our customers’ ever-changing needs. We operate in 30 countries and economies around the world and have more than 50,000 employees in more than 800 offices.

How is digital transformation benefiting Liberty Mutual’s customers?
One example is the way that the insurance claims process is being rapidly transformed from analog to digital. Industry economics and customer expectations demand such a change, and technology enables it. Consumers are increasingly “always connected” and seek a mobile-friendly, personalized and self-serve approach to their quote-to-buy, claims and policy-renewal journey. They increasingly demand convenience, easy-to-understand benefits and price transparency.

Thus, we continue to reimagine how we curate, consume and integrate an ecosystem of information for the benefit of our customers. As a result of our focus on digital transformation and the introduction of data-driven practices around customer intelligence and personalization, we’re able to succeed in meeting the needs of today’s savvy customers.

How is Liberty Mutual leveraging digital personas?
Customers today expect a seamless customer experience – whether it’s on their mobile devices, the web or directly over the phone. Digital personas create a framework on which we can deliver personalized experiences by leveraging data. Designing for each customer individually is not practical. Digital personas are a powerful tool for us to continue serving our customers with the personal touch that’s been a defining hallmark of the Liberty Mutual customer experience for generations.

To that end, DMI helped us to create digital personas, which are fictional characters representing the various segments of the Liberty Mutual customer base, that have helped us to gain important insights into our customers’ online and mobile behaviors, motivations, needs and technical skills so we can deliver an optimal customer journey.

How has Liberty Mutual been able to remain competitive with so many new players entering the property and casualty insurance marketplace?
We remain competitive by optimizing our portfolio and through the operational efficiencies that digital transformation and automation bring. Additionally, deeper customer relationships and a continuous delivery of value go a long way toward helping us maintain our status as the third largest property and casualty insurer, according to the National Association of Insurance Commissioners, and simultaneously, helps us ward off competitive threats.

At the enterprise level, what is it like to collaborate with DMI?
The DMI team is fantastic! Our team has worked with DMI for the past decade. As a result of the partnership, my team has been able to widely expand our digital design and persona-mapping capabilities and skill sets. The DMI team combines state-of-the-art tools with a progressive mindset to deliver maximum value for Liberty Mutual’s investment.

Next-Gen Technology is Key For Healthcare To Move Forward

In a recent press release, the consortium of Amazon, Berkshire Hathaway and JP Morgan Chase, announced that they had formed a new healthcare entity called Haven. The consortium aims to solve the rising healthcare costs and streamline the process of getting comprehensive and affordable care for all of its employees. This recent announcement should be a wake-up call for all healthcare payers and providers.

Companies like Haven and Oscar Health are digital native companies that are seeking to disrupt the healthcare industry and make waves by simplifying what has long been a complicated process. With these companies emerging, we are going to discuss why it matters, how you should react and what the next steps should be.

Why it matters

The healthcare industry as a whole has seen very little disruption over the years. This has not been lost on big time investors such as Warren Buffet and Jamie Dimon. They see an opportunity to bet on two things:

  1. There is a thirst for new healthcare options.
  2. Healthcare costs will continue to rise thus affecting their businesses directly.

The current inability for the healthcare industry to stabilize its costs has led to much frustration across the board from end consumers to business leaders. With the backing of two major funds along with the wide reach of Amazon, this consortium seeks to eliminate a lot of the problems that the end consumers of the healthcare industry experience. The addition of Amazon also brings in a strong technology and cloud background, which will end up saving Haven a lot of money. A lot of the payers currently in the market are still using monolithic systems that:

  • Cost a lot of money.
  • Require constant upkeep.
  • Slow down many essential processes.

If they are not moving nonessential and essential functions to the cloud, they are behind the 8-ball. Amazon will also be using cutting-edge AI and ML to help consumers make smarter decisions about their healthcare, something that current payers are still trying to figure out how to do.

Although these are serious reasons as to why the consortium matters, it is not the most important one. The most important reason as to why the consortium matters is because of who is backing it. If Haven really aspires to transform healthcare, they have the means and the backing to do so. If they want to buy up space to build facilities for patients to get care, they have the nation’s second-largest brokerage firm backing it up and giving them the flexibility to buy real estate as needed. If they needed capital on hand, they are associated with one of the largest financial institutions in the world, JP Morgan Chase. That allows for them to gather funding necessary to scale and grow their lines of business.

Speaking of scaling, they have the backing of Amazon, by proxy AWS as well. The ability to scale up and down using the cloud can help Haven expand their business and expand their capabilities into more focused, data-driven solutions. These three companies are the main drivers that will help Haven become successful.

How to react to Haven

While this is not the time for rash judgement, there are certain measures that healthcare companies can take to position themselves for upcoming competitions. The following are what I consider to be big-ticket items that should be addressed first:

  1. Ensure that all applications are starting to move to the cloud: This is one of the most important points. While there are still some security concerns involved with moving sensitive information to the cloud, that should not be a reason that most, if not all, applications are moving toward a cloud solution. Whether it is AWS or Azure, leveraging the cloud as an information repository can help streamline a lot of the business operations such as claims processing.
  2. Utilize Artificial Intelligence and Machine Learning to help make decisions: By implementing rules on what claims get approved or declined, you are able to automate the process, thus freeing up capital to use in whatever way you deem necessary. You can also utilize AI and ML to help make informed decisions on what kind of offerings to put out into the market, what are the recent drivers in terms of cost, and ultimately, help create a healthier population by gathering insight on the consumers
  3. Creating a better customer experience: By creating a better customer experience through connecting via mobile devices and simplifying main functions of healthcare, you are then able to build a solid customer base. When new entrants come into the market, especially ones that have the notoriety of Haven, then we tend to see some of the fringe customers head over to try the new entrant. In order to prevent this, and satisfy current/future customer trends, it is imperative to create a better customer experience.

These should be the first three steps that are taken to in order to start competing and start looking into the future to help grow your business. These are the steps that successful companies will take to ensure that they are geared up for the upcoming competition.

Varun Ganapathy, Director – Digital Technology Office; Industry Solutions

The Next Big Automotive Revolution is Here

Learn how to Navigate the Evolving Connected Car Ecosystem

While the connectivity space is bursting with ideas and new players are entering the automotive ecosystem at record speeds, the concepts developed over two years ago are beginning to shift quicker than anticipated. As a result, today’s mobility ecosystem is evolving faster than we can keep up with.

The connected and autonomous spaces are saturated by both providers of key technologies, partners in service delivery, innovations of in-dash technology and niche content providers. But today’s roadmap to the future is being paved by one key component in the ecosystem – the evolution of connectivity. Specifically, how it will be done.

Introducing the Connected Car Ecosystem 2.0Ⓡ

With the advent of 5G technology on the horizon, networked roads showing up and a growth in smart cities, there’s tremendous momentum in the automotive ecosystem. While the current model is exploding, no one has successfully captured all the wants and needs of consumers.

About the Ecosystem

In order to create an optimal experience in the ecosystem, what we need is a future-proof platform where we can take the best components of the current ecosystem services and integrate them into a software system. To achieve this model, it’s crucial that we stop thinking in the hardware sense and transition our thinking in such a way to include software solutions. This creates the opportunity to design open APIs that allow you to connect to the vehicle. By connecting the vehicle via cloud ecosystem backends, it allows one to exchange information across a multitude of providers – creating the ultimate connected car ecosystem.

Navigating the Complex Ecosystem

Integrating various pieces from the array of content providers into the vehicle roadmap is a challenging task. It forces manufacturers to make difficult decisions that often force them down a path that doesn’t serve their customers or the public in the best way. By creating an open platform model, manufacturers are able to tactfully integrate the individual desired services to create the unique customizable platform that consumers are demanding.

Think about it this way; why should we limit customers to what we think they want?

There’s an abundance of choices in the market, but the current state of the ecosystem is eliminating the choices and influencing what consumers use. There is little to no ubiquity in the design and delivery of the current car services. The car is essentially a mobile device that has to connect to everything that is not mobile, but by putting the car at the core of the ecosystem you fail to create the optimal customer experience. Consumers buy into certain software-based entertainment or travel ecosystems, if you fail to provide an interlink back to any and all of these services in the marketplace, you are in essence telling the consumer the choices they made are wrong. Or that in order to connect with this specific vehicle they must change who they work with. With the current model of limited service offerings in the existing ecosystem, manufacturers run the risk of losing brand loyalty.

Examples of Content Provider Ecosystems Influencing the Connected Car Ecosystem:

  • Music Providers: Choosing the music streaming service they want to subscribe to or the news channels they prefer, rather than the one the manufacturer picks.
  • Travel Agents: Integrating the navigation and/or HD maps that each individual customer pairs their mobile phone with.
  • Digital Assistants: There’s a rise of virtual assistants connecting all aspects of one’s life- connecting the phone to the house and even to the car. Providing drivers an assortment of assistants to choose from, integrating the car with their personal life.

Even with so many players jumping into the connected car space, consumers are still not getting an optimal experience. The answer lies within creating an explosion of choices, bridging the gap between the car and the exponential growth of content providers that will outpace the current capabilities of in-vehicle inside the vehicles today.

*And yes, it goes without saying that security is implied in this thinking, but that’s a whole blog in itself.

— Michael Deittrick, SVP, Digital Strategy & Chief Digital Officer

DMI Digital Leader Award – Captain Michael Dickey, U.S. Coast Guard

DMI Digital Leader Award - Captain Michael Dickey

Ensuring a Modern and Secure IT Infrastructure
for the U.S. Coast Guard

Tell me about your role as a Digital Leader:

I am Captain Michael Dickey, Commander, C4IT Service Center, U.S. Coast Guard. I lead almost 900 employees who are primarily located in Alexandria, Virginia; Portsmouth, Virginia; and Martinsburg, West Virginia, but are also at several other locations around the country.

My role is to ensure that the members of the C4IT Service Center who support the Coast Guard’s IT infrastructure, computers, applications, command and control, and navigation systems have the resources and processes needed to deliver the critical services we provide.

What is your mission?

The U.S. Coast Guard has a number of missions including Search and Rescue, defending America’s maritime borders, and protecting the maritime environment. Our missions can only be executed successfully by having the right people in the right place, informed by accurate, real-time data.

My mission specifically is to ensure the infrastructure, software, and enterprise applications that power the U.S. Coast Guard deliver precise, reliable, mission-critical data to our globally-deployed, internal stakeholders to inform prudent agency decision-making, and to our external stakeholders to ensure they can effectively execute their missions.

What is your biggest challenge as Commander of the Coast Guard’s Data Center?

I have a few! The most significant is keeping up with rising expectations and demand for robust and relevant IT services. We are also facing an increasingly high bar when it comes to cyber security. It can be challenging passing Command Cyber Readiness inspections, which are a series of reviews to ensure we have implemented all of the standards the Department of Defense has established. These inspections are particularly onerous within an environment that includes extremely complex legacy applications.

When it comes to federal agency modernization, what else is necessary?

Our Deputy CIO has made it a priority to markedly increase the rigor in our planning and budgeting processes. To ensure taxpayer dollars are leveraged wisely, we must keep accountability constantly at the forefront of everything we do. Rigorous controls and governance over software maintenance and investment prioritization are an imperative.

I’m committed to facilitating a reporting structure that empowers us to communicate results and performance to all necessary stakeholders. We are implementing an industry standard service management process framework, which I think any IT professional today understands is foundational to successful service delivery. DMI has helped a great deal on this front.

Tell me about your experience in partnering with DMI.

DMI’s leadership and expertise has been instrumental in helping us achieve our strategic objectives over the past five years. The work initially consisted of designing a management structure to enable accurate cost accounting and reporting to stakeholders. We are currently focused on restructuring our organization, which along with implementation of standardized processes across the entire organization, will enable us to markedly improve service delivery. DMI’s support at our data center in Martinsburg, West Virginia really helped us get this whole effort going.

We are working on incorporating Cyber and Intelligence support into our organization to ultimately become the “C5I Service Center”. C5I is a relatively new acronym that stands for Command and Control, Communications, Computers, Cyber, and Intelligence and much better defines the entire spectrum of services we provide to the Coast Guard. I think what we have learned through our partnership with DMI has really set us up to make huge improvements.

What’s next for you on your journey as a Digital Leader?

In addition to an increased focus on the performance of critical IT infrastructure, mobility now is part of the conversation at the most senior levels of the Coast Guard. DMI prompted some of our earlier thinking about developing mobile applications, but it has taken us a while to get our arms around the necessary security framework to deploy applications that allow mobile users to access sensitive enterprise data. However, mobility is now at the forefront of our operational capability planning and I have no doubt there will be opportunity for DMI to help us in this area.

DMI Consumer Survey: Amazon Teaches Retailers How to Elevate Customer Experiences

Amazon has spent much of the past two decades teaching us how to shop. Their website, mobile apps, product content, shipping deals and customer service have created high expectations in shoppers’ minds that everybody has to live up to.

That reality hits home in What Buyers Want — a DMI Consumer Survey, which polled more than 1,500 U.S. adults to get to the roots of shopping behaviors in an era of massive retail disruption. It’s become popular to implicate Amazon in the “retail apocalypse” that’s shutting down thousands of legacy retail stores. But when we asked people to name all the places where they purchased something in the past month, 88% of their responses named physical stores, versus 59% for websites and 38% for retailers’ mobile apps.

Moreover, while Amazon nets about half of all online sales in the U.S., according to Chain Store Age, they get only about 5% of total retail sales. Thus, it’s fair to say there’s nothing to fear from Amazon. Indeed, retailers are better off thinking about what they can learn from Amazon’s success. Can they take advantage of the behaviors that Amazon encourages in online shoppers? And could those experiences ever bleed to in-store experiences?

Survey Reveals Amazon’s Impact

Amazon’s influence left footprints throughout DMI’s report. For instance:

  • The Prime effect. When we sifted the survey data, we found that Prime members were 44% more likely to shop in-store at least weekly and 86% more likely to use a mobile device in-store. Thus, Prime membership signals a more active shopper profile in both digital and in-store, though their high digital engagement is most significant.
  • Blending online and in-store. 58% of our survey respondents had used a smartphone in a physical store to help find a product, learn more about it or make the purchase. Also, 51% of survey responses cited buying or reserving an item online and picking it up in-store or curbside. Amazon’s pervasive online presence is a crucial enabler of these behaviors.
  • Sunset for the desktop? Our survey responses suggested that desktop computers are becoming less relevant to online commerce. The survey’s rankings for best shopping experience when making a purchase look like this: 47% in-store, 28% desktop, 24% mobile. Combining the in-store and mobile votes (71%) reveals that people prefer them by a margin of 2.5-to-1 over desktop computers. Amazon’s influence on in-store mobile device usage is undoubtedly reinforcing these preferences.

What Amazon Does Best

Making Shopping Easy

Mobile is the preferred channel for browsing, over in-store and desktop. However, mobile doesn’t get the conversion of other channels because it’s often a clumsy experience. Among Amazon Prime shoppers, 44% have made purchases via mobile applications vs. 30% among non-Amazon Prime Members — that’s a jump  of 46%, suggesting that when consumers have a frictionless mobile channel, they convert at higher rates.

And it’s not just mobile applications. Responsive web can also be improved for easier, faster retail experiences, especially at checkout. When asked what retailers could do to secure more of their purchases, 14% of all survey respondents selected “faster checkout,” over other options like more loyalty points or exclusive discounts, indicating faster checkout has real nominal value to them.

Encouraging User Accounts

A common reason why shoppers continue to return to Amazon: all of their information is there already, from addresses to payment methods. This serves as a lesson to retailers to improve the process of account creation and use. In our survey, only one in four shoppers say they always use or create an account when making a purchase online, while one in five say they never create accounts. The biggest reasons for not authenticating: 32% don’t because they don’t want to receive marketing emails and 22% because it takes too long. If retailers better explain marketing practices and make the account creation process faster, they can secure more than half of lost purchases in the critical moment of authentication.

Turning Free Shipping into a Loyalty Program

We wanted to understand what retailers could do to earn more transactions from consumers. The answer, again, led us back to Amazon. Free shipping (33%) outperformed earning rewards (23%), and when coupled with guaranteed delivery and hassle-free returns, shipping represented nearly half of loyalty driving preference. When retailers consider all the effort and resources invested in costly loyalty programs, they should ask if those programs are truly driving loyalty and subsequent purchase, or if that investment would be better spent in shipping and delivery.

All retailers want to know what they should do about Amazon. You don’t have to fear them. In fact, Amazon may be on your side in encouraging more frequent shoppers in both digital and in-store. Instead, embrace the expectations Amazon has created with customers. If you can be at parity where it matters most — specifically around ease and delivery — you may not only protect but grow your market share against Amazon.

Taking these lessons to heart just might be the best way to keep your customers out of Amazon’s clutches.

— Paula Moniz, Director of Strategy & Customer Experience

Building a Future-Proof System Architecture for Connected Vehicles

Developing connected vehicles requires a two-pronged technology strategy. First, you have to master the integration of apps, devices, data and APIs to suit the connective services of the next few years. Second, you have to clear a path for the evolution of automation.

To make this happen, OEMs must develop user-centered connected-vehicle architectures that orchestrate:

  • People — employees, customers and vendors
  • Technologies — devices, applications and services
  • Content — infotainment, navigation and commerce

You can’t just wave a baton and bring all these elements into harmony, of course. But the right kind of system architecture can provide the framework to meet the challenge

Addressing Near-Term Demands

In the short term, you have to integrate internal, in-vehicle and external technologies and services to meet the demands of today’s connected drivers. This table is a snapshot of all the sources that must share data effectively to guarantee safe and enjoyable connected driving experiences:

Internal In-vehicle External
manufacturing, R&D, marketing, sales, warranty, dealers infotainment, telematics, user authentication, commerce development partners, service providers, mobile apps, devices

A robust system architecture is like an ecosystem that keeps everything in balance. Information from the vehicle flows into the enterprise, enabling services like predictive maintenance and sales projections. Data and services from third parties flow into the car, keeping the driver current on changing traffic patterns, parking access and nearby hospitality services.

Eventually, advances in data science and artificial intelligence will bring more automation to these technologies. Even if we never see fully autonomous vehicles on our streets and highways, connected vehicles will be increasingly complex mobile computing platforms.

Eliminating the Point-to-Point Integration

Today, the bane of connected-vehicle development is the point-to-point integration. It works like this: A telematics or infotainment provider connects directly into an in-vehicle network. In an ideal world, the provider’s open API stack connects to a multitude of connected-vehicle services without a hitch.

In the real world, OEMs manage a tangle of technologies, some of which may be decades old. These kinds of apps are not always well-suited to the API-powered age we live in today.

Moreover, the point-to-point integration represents a chokepoint in the system architecture. Any failure in the third-party provider’s API could shut down a host of connected services.

To avoid these chokepoints and keep using legacy technologies, you need an orchestration layer in your system architecture. You also need to maintain ownership of your APIs rather than farm them out to third parties.

The goal: Building an architecture that makes it easy to provision new services that arrive on the marketplace and deprovision services that suffer system crashes or become obsolete.

Keeping an Eye on the Long Horizon

A year ago, DMI developed CATE (Connected Autonomous Transportation Ecosystem) to provide a framework for the future of connected-vehicle development. Since then, the heady optimism about the potential of self-driving vehicles has given way to sobering questions, among them:

  • Who is liable when an autonomous vehicle crashes?
  • Will fully autonomous vehicles encourage overuse of highways and streets?
  • Can computers, sensors and networks ever get smart enough to automate all the cognitive processes people use while driving?

No matter how our society addresses these kinds of questions, the march of automation will go on. New tools and services will enable ever-richer driving experiences. An open system architecture with a well-thought-out orchestration layer will give OEMs the best chance of adapting to this evolving ecosystem.

— Brian Drury, director connected-vehicle practice

Strategic Tips for Optimizing the Path to Purchase

Retailers today have a lot to untangle. Technologies, devices, media platforms and shifting consumer motivations seem to create chaos at every turn. To clarify things, it helps to imagine customers traveling a four-stage path: awareness, browsing, purchase and support.

Customers enter these stages whenever the urge arises, using any kind of connected device. Thus, retail strategy has to align with consumer motivations within each of the four stages. DMI grappled with these challenges in What Buyers Want — a DMI Consumer Survey, an in-depth report exploring the roots of consumer behavior in today’s hyperconnected retail space.

The survey, which polled more than 1,500 U.S. adults, shed light on shoppers’ preferences throughout the path to purchase. Here’s a quick overview of the findings.

Awareness

In the awareness phase, people learn about products they aren’t necessarily interested in buying. DMI’s survey asked people to name all the places where they found out about products that captured their interest in the past three months. The top four responses:

  • 49% Amazon
  • 49% retailers’ websites
  • 45% social media
  • 38% search engines

These results underscore the fact that Amazon isn’t just a place to buy products — it’s a place to find out about them as well. Moreover, social media must be central to any product-awareness strategy. People trust friends, family and influencers to provide credible guidance on products. Finally, search-optimization strategy must include Amazon, on-site search and conventional search engines.

Browsing

In the browsing phase, people research products and gravitate toward a purchase decision. Mobile devices (42%) are most popular in the browsing phase, topping in-store visits (30%) and desktop computers (28%). When we asked people to name their favorite places to browse for products, the top five responses were:

  • 53% search engines
  • 43% store visits
  • 33% single-brand websites
  • 27% multi-brand websites
  • 19% Facebook

The browsing phase is a great place to ensure your website answers all your customers’ questions and gives easy access to crucial documents like user manuals. If customers need information about your products, they’ll find it wherever they can. Everything you do to improve access to product information pays off in the browsing phase.

Purchase

Our survey revealed a wealth of insights about purchase behavior. For instance, when we asked people to name their single favorite purchase location in the past year, the results were:

  • 46% in-store
  • 19% Amazon’s website
  • 15% retailers’ websites

We also noted marked differences between impulse buys and purchases that were more well-thought-out. Overall, people making impulse purchases placed the highest priority on convenience, while those buying products that required substantial research were most concerned about security.

The key takeaway from the first three phases of the path to purchase is you have to meet shoppers where they are and help them accomplish their goals. Because awareness and browsing clear the way to buying decisions, retailers cannot afford to overlook these phases in the quest to close more sales.

Support

Our survey explored the role of AI in customer service and collected consumers’ thoughts on how well brands communicate via channels like social media and email. When asked if they had encountered artificial intelligence during a customer service experience, 34% agreed they had, while the remainder either had not or weren’t sure. Of those who claimed AI encounters, 54% were either satisfied or very satisfied with the experience.

Thus, significant proportions of shoppers acknowledge the value of advanced learning algorithms in customer service. However, they also voiced strong preferences for dealing with employees versus automated chatbots. Brands and retailers have to take a strategic approach to AI, deploying it where resistance is lowest and likelihood of adoption is highest.

We also asked how consumers prefer to communicate with brands. The top five responses:

  • 76% in-store visits
  • 59% email
  • 56% phone calls
  • 45% online contact forms
  • 44% live chats on website or mobile

These responses reveal the deep complexity of communicating with customers. Brands need to make every venue available on multiple devices to ensure customer frustrations do not create roadblocks along the path to purchase.

It all matters

Our survey provided welcome reassurance that people enjoy the experience of shopping in stores. But it also sounded a note of caution: People have so many choices of devices, products, and information sources that brands must align their strategies with shoppers’ preferences. That starts with optimizing the entire path to purchase.

Whether they complete the transaction today or not, helping people with awareness, browsing and support improves the likelihood of winning the purchase phase when they’re ready.

-Elisabeth Bradley, vice president connected commerce solutions

5 Critical Tips for Implementing AI in Retail Customer Service

It’s all about the implementation. Retailers and consumers are increasingly open to AI and machine learning. But you have to get it right, which is essential because there are so many ways to get it wrong.

DMI’s research on consumer behaviors backs this up. Our recent special report (What Buyers Want — a DMI Consumer Survey) found that one-third of respondents had encountered AI, while the remainder either had not encountered it or could not say for sure. Of those who were aware of AI, just over one-half were either satisfied or very satisfied with the experience.

So, a significant minority of consumers know they’re dealing with AI and a small majority are fine with it. But they don’t prefer dealing with an AI chatbot over talking to a human. We posed eight customer service scenarios to survey participants and asked whether they’d prefer dealing with a human or an AI chatbot.

Even in the most readily automated scenario, ensuring a product is in stock in the customer’s size before purchase, survey respondents preferred human assistance over a chatbot by a factor of 4-to-1. In the most complex scenario, where the customer was mischarged and needed a refund, the human-to-AI preference leapfrogged to 19-to-1.

5 AI Implementation Tips

If you’re getting ready to ramp up AI in your retail customer service operations, keep these tips in mind:

  1. Optimize the agent side before the customer side. AI chatbots can make life easier for customer service agents, speeding customer and product information to them so they can handle more service requests. This strategy lets you work the kinks out of your AI engines behind the scenes, where they are less likely to annoy consumers.
  2. Seek the path of least resistance. Humans don’t always resist AI in chatbots. For example, people can be more comfortable talking to a bot about personal issues (health, finance, etc.) they’d rather not reveal to a person. In a more common scenario, you can automate simple queries like basic product information that a bot can deliver more efficiently than a human. As our survey revealed, resistance to AI happens on a continuum. You’re much better off embracing the low-resistance end of that range.
  3. Start small. It’s tempting to take on ambitious AI projects that transform large, complex processes. That’s usually a bad idea because you’re dealing with so many variables. AI is a learning process that optimizes positive outcomes and discourages negative ones. You start out solving easy problems and let your AI engines evolve to handle increasingly complex challenges.
  4. Optimize for satisfaction. Frustrations rise when an AI chatbot cannot handle the customer’s request. You have to build your AI engine to recognize when the bot is in over its digital head and needs to hand things off to a human agent. Resistance usually happens because somebody botched this implementation.
  5. Deploy trust mechanisms. Fingerprints and facial recognition are mechanisms to establish user trust. If you’re creating an automated interface that has to pull in sensitive customer information like a credit card number or a Social Security number, you need to add these kinds of mechanisms to reassure people that their data is secure.

Keeping it Human While Encouraging Automation

To a large degree, AI in customer service is a process of adoption rising as people become more acclimated to automation. Think about when gas stations started allowing people to pay for gas at the pump. Initially, we all asked for receipts because it felt necessary. Then we started seeing receipts pile up in our cars and realized we didn’t necessarily need one every time.

In years to come, many more components of AI will become automated and only the most complex issues will require human intervention. The human part of AI will be more about overseeing, measuring and monitoring increasingly complex AI systems.

We’ll always need humans in the loop of customer service because commerce is about people getting what they want. A bot can’t understand why that matters.

-Niraj Patel, managing director, artificial intelligence

DMI Digital Leader Award – Paul Bowman, Wexer

DMI Digital Leader Award - Paul Bowman

Personalizing the Fitness Experience at Gyms Around the Globe

Tell me about your role as a digital leader.
I’m the CEO of Wexer, a digital fitness platform that delivers personalized gym services that are disrupting the fitness industry worldwide. Our mission is to make virtual exercise more accessible through the use of technology. We’ve grown significantly since our 2008 start in Denmark and currently partner with fitness chains on five continents and in 58 countries.

How is the fitness industry benefiting from digital transformation?
It is said that “health is the new wealth” and the fitness industry has exploded in the past decade, experiencing nearly four percent growth annually according to Forbes. Still, the conventional brick-and-mortar gym industry has lingering issues related to overall member retention. Many members are intimidated by the fitness equipment and/or do not see fast enough results from their investment in their gym experience so motivation declines and they stop attending. Wexer empowers fitness chains to meet customers where they always are these days– on their phones!

Specifically, what services does the app provide gym members?
While typical fitness apps provide basic functions, like heart-rate monitoring and step-counting, our app delivers a complete set of services unique to the fitness chains Wexer partners with, including live-streamed and on-demand fitness classes. From ballet barre to meditation to boxing, cycling and high-intensity interval training, our platform delivers a comprehensive set of workout offerings for fitness enthusiasts of all levels. Members can access gym services from wherever they happen to physically be at that moment. Folks can also upgrade their membership, book an upcoming class and purchase a protein shake. Experience it for yourself by participating in one of our on-demand fitness classes here!

How are you augmenting the value of the typical brick-and-mortar gym experience?
We like to think of our mobile app as “a gym in your pocket.” The app provides a direct line of communication between fitness clubs and their members who may not be able to get to the gym in person often enough due to scheduling, lack of confidence, traffic jams, childcare issues or any of the myriad of other reasons gym patrons skip workouts. Conversely, the app is also a value-add for fitness chains, as it sells gym services and promotes overall sales retention. We’re extremely proud of the direct correlation that exists between member use of our app and the impressive member satisfaction scores for the fitness chain we serve.

What’s next for Wexer?
We’ve set our sights on expanding beyond traditional health clubs into a broader range of fitness venues. For example, we’ve been contacted by a major hotel group in Europe interested in implementing our platform across the continent. Additionally, as corporations of all types begin to truly understand the business value of a healthy workforce, expect to see many global offices start to leverage our services. We’re extremely excited about the prospects of expanding into commercial industry.

Describe your collaboration with DMI.
We’ve had a tremendous experience partnering with DMI since 2015. DMI was originally recruited to help develop the app into a multipurpose tool, and now manages our core technologies within the app. The partnership is currently accomplishing more than ever in terms of managing content outside of the four walls of fitness facilities, as Wexer explores a commercially expansive future. We’re excited to see where the collaboration with DMI takes us!

DMI Digital Leader Award – Paul Whitfield, Federal Railroad Administration

DMI Digital Leader Award - Paul W. Whitefield Jr.

Modernizing Railroad Inspections

Tell me about your role as a digital leader.
I’m Paul Whitfield, IT Project Manager at the Federal Railroad Administration (FRA) which is an operating administration within the Department of Transportation. Our nationwide team enforces and oversees the U.S. railroad safety regulations.

Why did the way railroad inspectors perform their jobs need to be modernized?
Our team of approximately 350 federal inspectors plays a critical role in ensuring railroad tracks, bridges, switches, ties, ballasts and crossing gates operate safely. Our track inspectors had previously been leveraging outdated palm devices and notebooks to gather data on possible defects. To perform their jobs more quickly and efficiently, inspectors needed both modernized hardware and software, including an app-based inspection platform, and rugged computer devices capable of synching with FRA’s legacy back-end database.

Describe the transformation process from concept to solution.
In creating the prototype for the Portable Inspection Reporting Tool (PIRT), it was initially necessary to literally walk the tracks with FRA inspectors in the field to understand how they do their jobs on a daily basis. After understanding and capturing the inspection requirements data, the team got to work and developed the PIRT prototype, a mobile inspection platform that uses a tablet with AI-powered speech recognition, a Bluetooth headset and a mobile app harness to empower inspectors to perform their inspections with voice commands.

What feedback about PIRT did you receive from inspectors in the field?
We rolled out PIRT initially to a pilot group of 30 track inspectors who called the solution a “homerun.” Inspectors reported features like the touch screens tablets, drop-down menus and data auto-population literally cut the time it took to perform their inspections in half. Thus, we are rolling out the next iteration of PIRT to all inspectors across the FRA enterprise writ large and incorporating additional requirements to support inspectors from multiple disciplines.

What’s next for PIRT?
We’re exploring how PIRT can be leveraged as a training device for new inspectors. We’re also at the beginning stages of automating some processes. We’re becoming more sophisticated in the ways we take in data with sensors, drones, video and social media along this nation’s more than 140,000 miles of railroad infrastructure. My vision is to start using machine learning as a “co-inspector for our inspectors,” eventually leveraging data predictively.

How would you describe the DMI team?
From the very beginning, the DMI team got it. We were particularly impressed with how quickly and effectively the team captured inspectors’ requirements during the pilot phase and came up with a PIRT prototype platform that was clean and intuitive in a matter of weeks. The team is super friendly, super flexible and definitely forward-thinking!

3 Excellent Use Cases for AI in the Financial Sector

Money changes everything, especially when it comes to artificial intelligence. Consider the typical financial professional: whether they’re a banker, broker or insurance agent, they have a strong vested interest in protecting customers’ financial well-being and finding productive investments.

If AI algorithms make their jobs easier, they’ll invest in them.

Stories on the financial press often mention “black box” algorithms designed to drive investment outcomes in stocks, debt and other financial instruments. While that’s a crucial use case for AI in finance, there’s a lot more happening in this space beyond Wall Street.

For instance, AI and machine-learning algorithms can search for patterns in financial transactions to help us avoid fraud, optimize our investments and streamline B2B operations. A quick review of these use cases can be helpful if you’re new to the idea of implementing AI in your company.

Anti-Fraud

When you buy coffee and a blueberry muffin with your debit card, your bank needs to know if that’s really you making the purchase. A PIN offers one layer of protection that a determined crook can easily overcome, so your bank needs more data. Most likely, it’ll run your transaction through an approval algorithm that can scan recent purchases and make an educated guess about your identity.

Today’s anti-fraud algorithms use simple rules, such as scanning only for previous purchases made with a single debit or credit card. In the years to come, AI will deploy complex rules that pull in data from increasing numbers of variables — essentially creating a behavioral signature that’s extremely difficult for a criminal to fake.

Soon, AI will be able to analyze data accurately enough to predict not only what we would do, but also what we wouldn’t do. That insight will throw up more roadblocks for would-be fraudsters.

Consumer Finance

We’re already seeing a burst of financial-planning apps. Robo-advisors use AI algorithms to help consumers pick profitable investments. Websites like Intel’s Mint.com allow users to import data from their financial accounts to track their wealth and manage spending.

Ideally, the data from these apps could be correlated to analyze your financial health the same way a fitness tracker extrapolates your physical health from your heartbeats, step counts and other real-time data. Today’s financial apps supply many pieces of the holistic puzzle of financial health, but it’s incomplete.

Every AI project eventually bumps up against limitations in its data sources, and personal finance is no exception. For AI to reach its full potential, financial companies will have to find secure, private methods to share data and remove silos.

Business to Business

AI and ML algorithms can scan enough patterns over enough time to predict future outcomes. While marketers in the consumer sector crave this kind of data to project sales and product demand, there’s also broad B2B potential for predictive AI.

Managing cash-flow challenges such as payroll, receivables and vendor payments boils down to optimizing revenues and expenses. Predictive algorithms can help companies do a better job of making sure they always have enough cash on hand for day-to-day operations while setting aside more retained earnings for reinvestment when they have excess capital.

Digital applications already produce substantial volumes of data to feed into AI algorithms. Sophisticated data-science projects can pull in external data on factors such as weather, demographic shifts and commodities prices to better predict future financial risks and opportunities.

Expect More AI innovations in Finance

Finance is an especially tricky arena for AI because of privacy regulations and companies’ natural desire to control their legal liabilities. And while it’s easy to extol the potential of learning algorithms, it’s difficult to make them work well. Data comes from multiple sources in many formats. We have to assess whether the data is accurate or producing false-positives. If inaccurate data pollutes useful data, we have to scrub out the inaccuracies.

For all these challenges, the financial industry and its customers have strong incentives to fight fraud, build wealth and optimize cash flow. These incentives will keep finance on the front lines of AI development.

– Niraj Patel, SVP IoT

Clinical Trials and Insurance: Two Windows on AI’s Impact on Healthcare

Healthcare is one of the frontiers of artificial intelligence breakthroughs. In years to come, advanced learning algorithms will help researchers unravel more of life’s mysteries from strands of DNA. It will also help doctors scout for trouble in people’s genetic profiles and take a proactive approach to disease prevention.

But looking to the future should not obscure the reality of the progress we’re making today. Two use cases from DMI’s client portfolio illustrate these gains. In one, our AI development partner helped predict the outcomes of clinical trials for cancer drugs. In the other, we helped a health insurance giant improve customer service in its call center.

Prediction: Avoiding Adverse Outcomes in Clinical Trials

Establishing the safety and efficacy of new medications can take over a decade and cost billions of dollars. Speeding up this process while reducing costs drives value across a vast health care ecosystem of doctors, patients, providers and insurers.

A recent DMI project conducted with our AI partner revealed cause for optimism that AI can play a role in driving this kind of value. Our AI-powered project for clinical studies on cancer medications:

  • Accurately predicted clinical trial outcomes at the clinical ARM/trial and molecular levels, reducing time frames, shrinking costs and improving treatment efficiency.
  • Enabled researchers to predict therapeutic failures, helping patients avoid wasting time on ineffective treatments.
  • Produced useful results with platform-agnostic methodologies that can work with any system.

The research project developed a predictive AI model based on a massive database of colorectal cancer cases. The project combined the colorectal research with the AI model and replicated the results in pancreatic cancer patients. Now the model can be applied to other tumor types.

These results point to the potential of predictive AI to help clinicians get more proactive and less reactive — avoiding the well-worn path of treating people only after they become sick. Preventive tactics like these may prove crucial to controlling the escalating costs of health care.

Conversational Interfaces: Improving Customer Service for Insurers

Another DMI client, a large health insurance provider, needed help transforming its call center. The company was sitting on seven years of unstructured data such as audio files from its government and commercial lines of business. They knew this data held a treasure trove of information about how people use the call center, so they put us to work on an AI-driven strategy to capitalize on it.

DMI’s AI solution:

  • Used call log analytics to streamline call center operations. That produced higher customer satisfaction and loyalty while improving operational efficiency and boosting agent performance.
  • Crafted a four- to six-week proof of concept (POC) that combined call log analytics with a root-cause analysis based on customer reactions, tone of voice, speech habits and hot topics that drive call-center traffic.

The project combined natural language processing (NLP) with text and sentiment analytics to determine what customers need most from a support call. When people call in with a simple problem, the conversational AI can implement a chatbot to direct them to a web page. If they have more complex problems, they get directed to expert agents who can help them figure things out.

Naturally, the most experienced and effective call center employees earn the most pay and represent major cost centers. Our system automatically rerouted minor calls away from them, giving them more time to deal with complex problems. That’s a win-win-win for customers, skilled employees and the company.

Voice and Prediction are the Future of AI

While these examples are specific to today’s health care challenges, they represent the path tomorrow’s AI will take.

Natural language processing and predictive AI use advanced pattern-matching algorithms that essentially teach themselves to seek success and avoid failure. Together, they will enable heavy industries to forecast commodity needs and prevent expensive production shutdowns. In the consumer sector, they’ll help marketers find buyers with laser precision.

Of course, AI is not a gold-paved road to utopia. A host of difficult security, privacy and governance challenges have to be ironed out. But the progress in health care suggests advanced algorithms can be a force for good for people, professions and the economy. It all depends our intelligence in putting these tools to their best use.

-Niraj Patel, SVP IoT

Yes, You Can Use Agile to Transform Your Enterprise

“We don’t do Agile.” “Scrum doesn’t work for us.” “Our CIO needs KPIs.”

We get it. You’re not ready to jump on the Agile bandwagon. You don’t think it’s a good fit for your organization. You have a long track record with conventional methodologies like waterfall. You don’t think Agile produces useful performance data.

But now you’re thinking about digital transformation at enterprise scale — migrating IT workloads to the cloud and implementing data-driven solutions with machine learning and artificial intelligence. With technology change accelerating so quickly, you have to weigh the risk of traditional methods producing unwanted outcomes: high costs, long timelines and products arriving on the market too late.

Agile principles can help any company improve their time to value on complex development projects. Agile succeeds where it was never intended to be used. NASA puts Agile to work on mission critical systems. A DMI client in the medical devices sector used Agile to improve efficiency while meeting the demands of FDA regulations. It’s far more than just a practical tool for simple software development.

We believe in Agile because it produces superior results by:

  • Accelerating time to value. Agile principles and Scrum teams help us deliver working solutions much earlier in the development process.
  • Improving quality. Rapid prototyping enables client feedback early in the process. That lets us work out flaws before they become baked into a product.
  • Reducing risk. Agile’s iterative nature builds on the feedback mechanism to diminish the likelihood of expensive project failures.

It’s true that Agile development isn’t a slam-dunk for large organizations. Every company has a unique spot on the Agile continuum where they currently exist — and a spot where they will see the most benefit. The key is finding your spot and adapting your Agile approach to your unique circumstances.

We find there are three fundamentals to Agile transformation:

  • Flexibility. You don’t just throw a switch and make your company Agile. You have to start in places where it’s most practical to implement and build on that experience. Because every company’s different, you need a custom approach to Agile transformation. Managing large programs or running multiple Agile engagements often require the use of frameworks like Scaled Agile Framework (SAFe) to scale traditional Agile team approaches.
  • Culture. You can’t just declare yourself Agile. The benefits of speed-to-delivery and speed-to-value can be realized only if you embrace a shift in your company culture. You need to begin prioritizing work based on value, true need and cost instead of marketing, perceived need and wants. Moreover, you need to embrace organizational change management, from the executive suite to the people who use the technologies to perform their work.
  • Metrics. Regardless of what some believe, you can measure the effectiveness of Agile teams. DMI developed a series of KPIs that help team leaders identify sources of underperformance and find opportunities to improve. We acknowledge that measurement drives behavior, so we focus on what behavior we want to foster and measure appropriately within four areas: value, quality, progress and productivity.

At its core, the Agile approach is about smart decision-making: Establishing priorities to figure out what gets built first. The minimum viable product, or MVP, forms the foundation for all the iterations to come that will produce a polished, effective product.

Of course, you wouldn’t use Agile for finished manufactured goods — the consumer expects to drive a fully functioning car off the dealer’s lot. But there may be countless opportunities in the auto industry to apply Agile methodologies that prioritize speed, collaboration, flexibility and time to value. The same principle applies in pretty much every industry.

One sure sign of Agile’s success is the cadre of true believers passing judgment on whether a project or methodology is “truly Agile.” We see where they’re coming from, but we also believe in tailoring a solution to a company’s distinct marketplace challenges. We believe that implementing even a single, small Agile concept is a step in the right direction if it produces positive results. You can always continue to improve from there.

That’s the ultimate measure of agility.

-Brian Andrzejewski, Director of Business Transformation Services

Auto Parts: Using AI Components to Drive Cohesive Owner Experiences

Years from now, cars will drive themselves. But car owners won’t have to wait that long to enjoy the benefits of learning algorithms. We already have the tools to create digital assistants to handle everyday car-owner responsibilities.

The key to making this happen is orchestrating a series of AI engines that use predictive capability, pattern matching, natural language processing (NLP) and other machine-learning methodologies. Consider a straightforward use case: Your car needs an oil change.

Depending on the model, your car may be able to read your odometer and turn on a light telling you it’s time to change the oil. But you still have to contact the dealer and sync your schedule with available times in the dealer’s garage. If you use premium synthetic oil, you have to tell somebody in the service department and follow a specific schedule.

The next phase of AI in automotive is to use learning algorithms to automate much of this process. Several AI engines could be put into play:

  • A predictive algorithm can draw data from multiple sources to recommend the optimum mileage for changing your oil. The schedule accounts for your favorite synthetic oil.
  • A digital assistant algorithm can scan your calendar and sync it with available garage slots at your car dealer.
  • A pattern-matching algorithm can check all the previous times you’ve taken your car in for an oil change. If you usually take the car in on Tuesday mornings, the AI starts by looking for open dates at the dealer on those days and times.
  • An NLP algorithm can power a voice interface that walks you through a series of questions and options to set up the appointment hands-free while you’re driving to work.

The real value here doesn’t come from creating the algorithms. It comes from coordinating them to produce the desired outcome. This principle holds for any AI-powered services for car owners. As in-car services evolve, OEMs and vendors will have to figure out ways to simplify the lives of car owners — or stand by and watch somebody else do it.

Challenges to Improving the Car-Owner Experience with AI

Whether you’re an OEM or vendor in the automotive sector, you feel the pressure to use AI to improve the owner experience. Everybody has the same question: Where to begin? The best route is to start small: Develop one AI project that works. Find complementary areas where AI makes sense and delivers value. Build on what you’ve learned in the earliest phases.

The greatest mistake is taking on too much, too soon.

Another sticky challenge is data silos. Dealers, manufacturers and suppliers all have datasets that might not be compatible. Everybody has data-governance and security concerns. Compliance issues may cloud the picture even more. Getting all these factions to share their data is a demanding job.

But to make AI work at scale in a large industry like automotive, you have to knock down these silos.

Driving outcomes with data

If you have enough data, you can create an algorithm that strives to achieve a desired outcome. The algorithm is optimized to produce wins and avoid losses. Over time, the algorithm accumulates so many wins that it learns to perform better without human intervention.

In the auto sector, effective AI means using data to capture the ownership experience — and make it better. Data from smartphones, apps, and transactions helps us understand a car owner’s behavior and develop services that remove friction from their lives.

Putting data to work in predictive algorithms is the critical first step. But to drive lasting impact for car owners, you have to coordinate multiple AI engines to create a cohesive experience.

That’s the outcome we’re driving at DMI.

-Niraj Patel, SVP IoT

Why Your Cloud Strategies and IT Strategies Will Merge (If They Haven’t Already)

Cloud strategy used to mean deciding where to put your data and apps — in private, public or hybrid clouds. And IT strategy meant folding cloud capabilities into a wider program to use computing power wherever it made business sense.

Today cloud strategy means integrating remote apps, storage and processing power into every phase of IT that makes business sense. With more cloud apps and services arriving every day and more people using mobile devices to access these apps, it’s increasingly difficult to draw a line between the cloud and everything else.

Before long, cloud strategy will become indistinguishable from IT strategy. Let’s look at why this is a major shift for large organizations.

Cloud architecture is evolving quickly

It seems like a new cloud service or tool comes out every day. Large cloud providers keep adding goodies to attract new clients. And disruptive startups with a cloud-first philosophy are making everybody rethink their IT strategies.

These trends require nimble cloud architectures that can scale up quickly and respond rapidly to changes in their marketplace. A well-designed cloud architecture has to get five things right:

  1. Operations must meet the demands of the business — traffic spikes and other technology demands shouldn’t affect the customer.
  2. Compliance must be robust enough to account for industry-specific rules.
  3. Solutions must apply the best applications for specific business needs.
  4. Budgets must adapt to the shifting cost structure of the cloud without placing undue burdens on creativity and innovation.
  5. Strategy must assess how cloud technologies affect everything else in IT.

Folding Point 5 into your cloud architecture means accepting the reality that there’s a role for cloud services and mobile technologies in pretty much every corner of the enterprise.

There will always be specific use cases where the cloud isn’t the best fit. And the concept of fit will evolve over time. But it’s becoming ever more obvious that there’s precious little difference between a cloud strategy and an IT strategy. The sooner they merge, the better.

— Rob Decker, Executive Director, Cloud Services

How AI Improves Safety and Efficiency in the Federal Government

Artificial intelligence and machine learning algorithms are helping the U.S. government deliver better results to taxpayers. Two use cases — worker safety and equipment maintenance — showcase these benefits.

Before we explore those areas, let’s review the basics of AI/ML. AI engines use complex algorithms that scan huge databases to find patterns that reveal insights. Over time, the algorithms learn by prioritizing positive outcomes and discouraging negative results. Here’s a quick look at two examples:

Making Workers Safer and More Efficient

Federal regulators have two critical worker-safety concerns: reducing workplace risks and keeping federal workers safe. And because they have limited budgets, regulators need to help their workers get more work done in less time.

DMI’s work with the Mine Safety and Health Administration illustrates how AI/ML can improve safety and efficiency. We contracted with MSHA to make mine inspections more efficient. Before this project, MSHA inspectors carried laptops, digital cameras and thick books of regulations into the depths of the nation’s mine shafts. Even with these modern tools, inspections were cumbersome and time-consuming. Some inspections took months to complete by the time all the paperwork was wrapped up.

Our project digitized the inspection process and sent inspectors to work with Microsoft Surface tablets that stored digital versions of the regulations. With new tools and methodologies, inspection timelines shrank significantly, reducing the time it takes to convert findings into actions from months or weeks to days.

AI technologies play a key role in this process. We can use AI to comb inspection data, correlate with other data source, and develop predictive models suggesting when accidents or outages might happen in a mine. We can use these findings to coach mine owners on proactive maintenance and measures to reduce the risk of accidents.

With AI and digital technologies, we can make mines safer for miners and the people who inspect them. And the increased efficiency means inspectors can visit more mines and identify more safety risks.

Predicting Equipment Maintenance

The federal government’s equipment inventory is mind-boggling. The Pentagon springs immediately to mind with the U.S. military’s armada of high-tech weaponry and its fleet of cars, trucks, planes and other vehicles. But the feds also own data centers, distribution lines, medical equipment and hundreds more varieties of machinery.

All of these devices face everyday wear and tear. Some endure extraordinary stress in hostile environments. Predictive AI algorithms can tell field commanders that their tanks need to come in for repairs to prevent breakdowns on critical missions.

The same concept applies across the government’s inventory: Replace worn parts in conditions you control. Then you don’t have to deal with, say, a blown engine or transmission that can cause damage far beyond the site of the breakdown.

Bringing Speed, Safety and Insight to Government

Government agencies answer to a demanding public. Reams of regulations encourage safe practices and discourage risky behaviors. Data-governance rules require extra effort to keep personal information secure.

These forces naturally slow the progress of federal technology projects. Indeed, some timelines become so elongated that IT solutions are obsolete when the government starts using them. It doesn’t have to be that way. As DMI learned with projects such as MSHA, mobile technologies, the cloud and AI algorithms make it possible to speed up timelines while improving service to the public.

The government will probably never be as nimble as the public sector. After all, agencies have to reconcile competing demands on a scope rarely seen in the private sector. But our experience shows immense potential to use AI/ML to do the public’s work while elevating safety and efficiency.

It’s become popular to fret over the future of AI. While advanced automation may prove sinister in the decades to come, for now AI is taking repetitive chores off of people’s to-do lists and helping them get more work done in narrow time frames. That’s a change for the better in the government realm.

— Varun Dogra, Chief Technology Officer