Digital transformation happens when businesses replace existing systems with brand-new digital technologies and change fundamentally as a result. But what is intelligent digital transformation? In short, intelligent digital transformation is digital transformation driven by artificial intelligence, or AI.
In practice, AI-driven digital transformation might include machine learning — algorithms that monitor consumer behaviors and personalize product recommendations, for instance. Some companies’ intelligent digital transformations revolve around Internet of Things (IoT)-enabled devices and accompanying AI-enabled software.
In this post, we’ll explore five of the biggest intelligent digital transformation trends for 2021: remote workforce management, automation, cloud infrastructure, IoT adoption and external networking.
Jack Be Nimble, Jack Be Quick
Before we dive into our list of effective digital transformation drivers, let’s take a moment to talk about the importance of being nimble. What does it mean to be a “nimble” company in a digital era? How can being nimble help your enterprise adapt and avoid common transformation pitfalls?
Start-ups, SMBs and corporations with outdated, monolithic infrastructures nearly always fall flat when they try to transform. Out-of-date ERP systems, legacy CRMs and 20th-century project management strategies, for instance, hamper or prevent digital optimization and digital transformation.
To embrace intelligent digital business transformation, your underlying company architecture has to be flexible and nimble. Running a cumbersome, unreliable enterprise software system? Bin it. Is your e-commerce platform powerful enough to support your digital business strategy? If not, upgrade. Do you have enough on-premise — or cloud-based — storage to accommodate your data exchange needs? Now’s the time to expand.
Digital transformation — particularly when it involves a degree of digital disruption — sits right at the top of the industry development S-curve. Your business is a rocket on a mission to Mars: To reach your destination, you must be able to adjust your strategy at will. That, in a nutshell, is a nimble company.
Top 5 Intelligent Digital Transformation Trends for 2021
At DMI, we combine human ingenuity with machine learning to create a unique intelligent digital transformation solution for each client. We operate in a shifting landscape — after all, the technologies that underpin digital transformation change constantly. Here are five of the strongest trends that we believe will shape intelligent digital transformation in 2021.
1. Remote Workforce Management
Under normal circumstances, industry disruptors emerge in advance of the apex of an S-curve. Businesses have time to prepare for — and embrace — change. In contrast, COVID-19 flew in like a curveball out of left field. Digital transformation immediately went from optional to essential, and some companies weren’t nimble enough to survive.
To continue operating, many businesses had to implement unfamiliar remote work technologies on the spot. In February 2020, just 17% of American workers were based at home. Two months later, 51% of Americans had a remote work setup. Zoom, Google Docs and project management platforms like Basecamp became the new normal.
Most traditionally office-based companies experienced teething problems. Stumbling blocks included connectivity issues, software glitches and poor remote workforce management strategies.
AI Solutions for Remote Workforce Management
Remote work isn’t going anywhere — not for the foreseeable future, and probably not ever. Now’s the time to update stopgap technologies and hone systems installed on the fly. Intelligent solutions for remote workforce management include:
- Engagement feedback tools: These AI-driven trackers use natural language processing to analyze messages sent by email or via work chat apps (Slack, for example). In turn, managers receive helpful engagement snapshots.
- AI-powered coaches: These app-based chatbots provide information and encouragement — and they also flag work-related problems and mental health problems like depression and stress. AI-powered coaches constantly gather data, so they provide valuable workforce insight.
- AI-driven training: Remote training can be a resource-heavy endeavor. AI-based training bots provide user-specific learning opportunities and reduce the need for human involvement. The result? You save money and time.
2. Digital Process Automation
Digital process automation (DPA) is the digital descendant of traditional business process automation (BPA). Companies evolving via digital transformation often automate processes before they do anything else. Why? Because DPA helps create a more efficient workflow, improves accuracy and relieves employee boredom.
When correctly implemented, DPA makes life better. The more mundane, repetitive processes you automate, the more time your workers regain. You can automate email campaigns, weekly website traffic reports, supply chain tasks, file transfers, payments, customer support tickets and a slew of other tasks — and quickly, too.
DPA is a holistically beneficial strategy. Customers benefit from your streamlined operation, and workers benefit because they don’t have to spend their lives performing monotonous tasks.
AI Solutions for Automation
DPA and AI are uniquely compatible. Artificial intelligence removes or reduces the need for human intervention — so AI is, itself, a form of automation. Companies can use information gathered via AI to decide which processes to automate; later, AI helps further enhance automated processes.
When DPA and AI combine, you get intelligent process automation, or IPA. IPA is an emerging collection of new technologies, which range from cognitive software to virtual robotics. IPA applications include:
- Cognitive agents: These AI-infused programs use natural-language generation (NLG) and machine learning to mimic human beings. When deployed, these “agents” can perform set tasks and make decisions, just like a regular worker.
- Machine learning: Capable of learning and mimicking patterns, these algorithms provide greater insight via advanced analytics. When used in an HR setting, machine learning can help managers identify suitable candidates for open positions, for instance.
- Robotic process automation: RPA is effectively a programmable software-based janitor. When properly engineered, these digital tools can clean databases, send auto responses, transfer files and free up system resources. Meanwhile, human workers can concentrate on more interesting things.
3. Cloud Infrastructure
Cloud computing isn’t a new concept — at least, not anymore. What is new is cloud infrastructure deployed at scale. Clunky in-house IT services are out, and virtually unlimited cloud-based IT resources are definitively in.
Cost is one of the main driving forces behind the switch from in-house to cloud computing. Software as a service (SaaS) and platform as a service (PaaS) providers take care of programming and development and then market their programs to the masses. Companies simply don’t need to spend hundreds of thousands of dollars on a proprietary platform in 2021.
Other benefits of a cloud-based infrastructure include:
- Flexibility: Companies can choose which aspects of cloud computing to use while benefiting from an agile, usage-based buying model that can scale up or down at any time depending on the business needs or changes.
- Modernity: Providers upgrade and update cloud platforms and software automatically.
- Security: In-house cyberattacks become less of a concern when data lives off-site.
- Reliability: Cloud platforms and software keep data backups in multiple places, so they stay reliable even if specific nodes fail.
AI Solutions for Cloud Infrastructure
When cloud computing and artificial intelligence converge, that’s AI cloud computing. AI cloud computing is a fusion of machine learning and traditional software — Apple’s Siri, for instance, is an intelligent cloud-based digital assistant. Examples of AI in the cloud include:
- Customer tracking bots: These intelligent algorithms piggyback on top of existing CRM solutions. They capture consumer data, identify communications patterns, provide insights and make suggestions to improve customer experience.
- Artificial intelligence as a service: AIaaS is an accessible and low-cost alternative to in-house AI development. Examples of AIaaS include chatbots, cognitive computing APIs and cloud-based machine learning services.
- Deep learning algorithms: Big data means big news. Cloud-based deep learning algorithms help companies like Amazon digest a monumental amount of information.
4. Digitization and IoT Adoption
The Internet of Things is a physical manifestation of digital transformation. In simpler terms, objects connected to the industrial or consumer IoT receive instructions and send data via the internet. IoT examples include connected washing machines, smart thermostats, activity trackers, ingestible health care sensors and smart bathroom scales.
Digitalization strategies involving IoT usually involve developing and launching new products — specifically, internet-enabled versions of previously non-connected devices. Regular security cameras morph into smart security cameras; standard pacemakers evolve into connected pacemakers; normal contact lenses become sensor-embedded smart contact lenses.
AI Solutions for IoT Adoption
AI and IoT go together like peanut butter and jelly. Devices in the IoT ecosystem capture and transmit data; AI algorithms analyze that data and provide a response. When correctly configured, AI and IoT make systems much more efficient, enhancing UX. Here are just three examples of AI-centric IoT:
- Smart pacemakers: These clever pacemakers go one step beyond IoT. Instead of stimulating the heart constantly, they monitor a patient’s heart rhythm and send electrical pulses only when they detect a rhythm abnormality.
- AI-enabled self-healing machines: Machines enhanced with sensors can recognize and correct operational issues on the fly, removing or reducing the need for a human repair person.
- Self-driving cars: Autonomous vehicles equipped with wireless LANs use AI to predict pedestrian behavior, make weather-related driving adjustments and determine road conditions.
5. Augmented Reality
Augmented reality (AR) makes online shopping a more tangible experience. It’s not the same as virtual reality — AR isn’t a totally virtual environment. Instead, AR technology blends reality and virtuality by layering digital objects over real-world scenes. Wearable devices like Google Glass Enterprise Edition use AR to provide hands-free access to information, for example.
AR is more common than ever — especially in the retail industry. Consumers can try on clothes, eyewear, makeup and shoes via AR-enabled apps. Art galleries use AR to help consumers view paintings and sculptures; car manufacturers use AR to give virtual vehicle tours; furniture companies use AR to make home transformations feel real.
AI Solutions for External Networking
AR and AI complement each other beautifully. When developers incorporate AI into AR apps, user experience improves considerably. Voice commands trigger virtualization changes in AR apps enhanced with AI-enabled speech recognition algorithms, for instance. Other examples of AI and AR at work include:
- Object recognition: AI-enhanced AR apps recognize objects and display information automatically.
- Depth perception technology: When AR apps use AI to gauge depth and distance, virtual objects look more real in situ.
- Virtual manipulation: AI technology embedded in smart glasses lets users manipulate AR objects using voice commands or eye movements.
AI Adoption: Essential for an Enhanced Customer Experience?
Artificial intelligence is no longer science fiction. Instead, it’s swiftly becoming mainstream in the digital age. According to recent Genpact research, more efficient AI-driven companies will soon lead the field. If business models don’t adopt AI, they’ll likely lose their competitive advantages before 2025. In other words, digital transformation will be vital for survival.
If immediate organizational change doesn’t sound like a feasible plan, you could opt for a modular approach to transformation instead. AI-enabled microservices like integrated speech recognition software and chatbots improve efficiency and enhance service speed — and you don’t necessarily need to disrupt existing systems to deploy them.
Digital Transformation Strategy 2021: A Recap
Digital transformation trends for 2021 are based not only on existing industry momentum but also on sudden changes caused by the COVID-19 pandemic in 2020. The nature of employment in America, for example, completely changed as a direct result of coronavirus. Remote work went from unusual to commonplace in less than 10 months, and — where deployed — AI-driven team management technology gave companies an edge in the marketplace.
According to a recent McKinsey report, one UK-based company saw an impressive 330% ROI after deploying cognitive technology. Its cognitive chatbots made 40% fewer validation errors and increased the company’s conversion rate by 22%. With those results in mind, we believe AI-enhanced digital process automation — NLG, for instance — will also take center stage in 2021.
Parallel collaboration will be easier than ever this year, thanks in part to cloud computing. Cloud-based platforms and programs enable better communication, file sharing and project management. Meanwhile, IoT adoption and connectivity will enhance systems and improve UX.
Finally, AR will continue to drive change — especially in the retail and manufacturing industries. AI integrations will make AR apps progressively more intuitive and useful.
Investing in Change Management
Change — whether holistic or incremental — requires consent and cooperation across the board. Genuinely successful digital transformation depends on universal enthusiasm from CEOs, CIOs, decision-making managers and workers alike. If you’re ready to invest in change management or you need advice about digital transformation options for your company, connect with us online to begin the conversation.