Once a science-fiction staple, artificial intelligence (AI) is now a core part of digital transformation. But what is AI, and how can it help you make business decisions?

We’ll unpack all things AI in this post. First, we’ll define what the term really means from a digital perspective, and then we’ll explore the history of software-based AI. Finally, we’ll look at ways you can use AI to drive change in your organization.

What Is AI?

Forget humanoid robots for a moment: Software-based AI doesn’t emulate Hollywood. In practical terms, AI is code. Popular and familiar coding languages for AI include Python, Java and C++. Some developers use specialized AI coding languages like Artificial Intelligence Markup Language (AIML), Lisp, Prolog and R instead.

Most contemporary AI-powered systems — investing tools, virtual assistants, monitoring algorithms and chatbots, for instance — demonstrate “narrow AI.” In other words, they’re programmed to mimic at least one specific behavior typically associated with human intelligence. AI “skills” include learning, planning, solving problems, reasoning, gauging motion and creating.

Most businesses use AI in one of two ways:

  • AI-powered business software: Smart email categorization, process automation, voice-to-text and dynamic price optimization
  • AI-powered robotics systems: Autonomous cars, manufacturing robots, AI-equipped drones and cleaning devices

In this post, we’ll concentrate on software rather than robotics. Later, we’ll show you how AI-driven software can help guide your business forward.

AI Development in Brief

Perhaps unsurprisingly, AI began as a science fiction concept. Later, scientists like Alan Turing began exploring the logic and math of artificial intelligence. Turing wrote a paper on AI but didn’t have the funding or the processor power to continue his research.

The very first working AI software emerged in 1956. Developed by researcher Alan Newell and programmer Cliff Shaw in collaboration with Nobel Prize-winning economist Herbert Simon, Logic Theorist was an automated reasoning program.

Logic Theorist triggered an explosion of AI enthusiasm. Machine learning algorithms emerged as computers got more powerful, and governments began to fund AI research in earnest. After a brief slump in optimism in the 1980s, AI development accelerated into the 1990s. In February 1997, IBM’s Deep Blue program beat chess grandmaster Garry Kasparov 3-1/2 to 2-1/2 in a six-game match under tournament conditions.

Speech recognition software — namely NaturallySpeaking — also materialized in 1997. Integrated into Windows 95 and Windows NT 4.0, NaturallySpeaking made life much easier for computer users with mobility issues. In 2000, an AI-equipped robot called Nomad began to explore Antarctica; two years later, a very different robot — Roomba — started vacuuming autonomously.

Autonomous cars and movement-tracking devices like Microsoft’s Kinect device quickly followed. The first in a wave of digital assistant apps, Apple’s Siri, launched in 2011. Game-playing bots like Google DeepMind’s AlphaGo and algorithms like Deepstack began challenging human opponents in 2015.

After that, AI programmers started to concentrate on data analysis — specifically big data. Now, natural language apps, speech recognition software, machine learning and deep learning algorithms are ubiquitous.

AI in Digital Transformation

Digital transformation is a unique process for each organization. Some companies switch from analog to fully digital across the board, while others opt for a select few microservices — things like payment processing gateways, for instance, or search tools. Still, others morph gradually from traditional processes to digital processes.

So, which digital tools should you implement first? Is it better to take the plunge with a wholesale digital transformation blueprint, or should you come up with a two-, three- or five-year digital plan?

Many companies begin with AI — specifically, AI-based data analysis. AI-driven data analysis can help you pinpoint where AI-based automation tools might work best, which suppliers provide value for money or which CRM to use. In short, AI can help you create a great digital transformation strategy.

AI-Driven Decision-Making

AI-driven decision-making is a natural progression from data-driven decision-making. Both AI-driven and data-driven decision processes base decisions on data, with one crucial difference: human involvement, or in AI’s case, a lack thereof.

Data-driven decision-making depends on human interpretation. In short, it goes something like this:

  1. You identify data-collection opportunities and set goals.
  2. You gather and collate data.
  3. You examine and interpret the data.
  4. You create a list of potential data-driven actions.
  5. You take action and make changes.

AI-driven decision-making uses machine learning to interpret data. Here’s a breakdown:

  1. AI algorithm identifies data-collection opportunities.
  2. AI algorithm gathers and collates data.
  3. AI algorithm analyzes data.
  4. AI algorithm suggests changes and responses, and/or
  5. AI algorithm initiates changes and responses.

Some low-risk AI-driven software requires very little human intervention. Its AI algorithm works in a closed circuit, gathering and interpreting data and initiating changes ad infinitum. Previously, you might have had to monitor and hone processes manually; AI-infused software hones and perfects processes constantly, all by itself.

Why Reduce Human Decision-Making?

Human beings have many abilities — empathy and critical thinking skills, for example — that AI-driven programs don’t have. Humans can’t beat intelligent algorithms at the data analysis game, though.

For starters, emotions get in the way. People have trouble accepting unexpected or disappointing data, for instance. Irritated and frustrated, they go into denial and come up with dismissive theories to counter facts they find unpalatable. In contrast, AI programs accept data sets on face value and come up with strategies to improve performance.

Human beings get tired; AI-enabled software works around the clock with no drop in performance. Human beings make flawed decisions based on gut instinct; AI-based programs make logical decisions based on pure fact. Human beings calculate answers in minutes; intelligent algorithms do the same thing in nanoseconds.

Human judgments are based on previous experience, intuition, implicit and cognitive bias, memory, prior learning, how much sleep we got last night and how much coffee we drank this morning. AI-driven judgments are based on data, logic and math — and that’s all.

Human-AI Collaboration

AI-based software is great at data analysis. AI can find hidden patterns and translate reams of data into usable information more quickly than we can — and that’s okay. When we use intelligent algorithms to evaluate structured data, we streamline processes and save time. We can use that extra time to create, ruminate, network and collaborate with colleagues.

Artificial intelligence isn’t a replacement for human cognition. For a start, AI programs don’t understand cause and effect like we do. AI-integrated VoIP systems recognize speech, but they don’t know where that sound comes from. Even deep learning algorithms don’t ask, “But why?” Humans do, though.

AI-enabled programs make investment suggestions; humans change those decisions based on niche market forces. AI systems suggest specific inventory levels; humans adjust the incoming stock to accommodate seasonal sales. When workflows use input from human and AI sources, decisions improve.

Practical AI-Driven Decisions

Big data and AI are best buddies. AI-enabled programs reduce human workload — especially in the big data analytics arena. If your business decisions are based on a lot of data, AI-integrated programs bring clarity.

In practice, you’ll need to add non-digital data manually to make AI-driven decisions. AI provides insight, but it doesn’t know everything. Let’s see seven examples of AI-driven decision-making at work.

1. Intelligent Data Analytics

Intelligent data analytics programs make bulk data processing look easy. You can use intelligent software — AI-powered CRMs, for instance — to unravel patterns in large amounts of customer data. AI-powered CRMs convert unstructured data into structured data and then make sense of it all to reveal the big picture.

Intelligent CRM tools include:

  • AI-driven lead qualification
  • Intelligent content organization and production
  • Customer-facing product recommendation

Many AI-driven CRMs also incorporate intelligent assistants, which route calls, schedule meetings and complete other simple office tasks. When companies use AI-driven CRMs and other data analytics tools to distill information, employee satisfaction increases — and customers benefit from clever, streamlined solutions.

2. AI-Driven Data Mining

In a nutshell, data mining is automatic data collection. Usually, companies gather data from various sources — mines, in effect — which they analyze to gain crucial industry or consumer insight. Two of the most helpful types of business data mining are:

  • Opinion mining: Existing customers’ opinions about your company and about the products you sell
  • Sentiment mining: Consumer reactions and attitudes toward your company and your industry in general

To mine opinion and sentiment, companies look at social media posts, review sites, reviews left on their own platforms, blog posts, influencer material and anything in between. Opinion mining reveals how well you’re pleasing — or not pleasing — your existing customer base. Sentiment mining can tell you how efficiently your sales pipeline pulls in new leads.

Opinion and sentiment analysis can drive business decisions in a big way; however, opinion and sentiment mining take considerable time. That’s where AI comes in. AI-powered data mining removes the human resources barrier to opinion and sentiment analysis.

3. AI Modeling Techniques for Marketing

Marketing decisions are hard. Which brand copy is better? Which logo design is most compelling? Which visual ad will make the most impact? Before AI, executives made complex short-run and long-run marketing judgments based on their experiences, on their understanding of their customers and on consumer trends.

AI can help you make marketing decisions in two main ways:

  • Intelligent design support systems (IDSS): A marketing IDSS is essentially a virtual marketing consultant. Intelligent marketing decision support systems use data mining, forecasting and trend analysis to make marketing suggestions.
  • AI-augmented marketing programs: AI marketing software can make automatic marketing-centric decisions based on data gleaned by a number of helpful tools — natural language processing, media buying and real-time personalization algorithms, for instance.

4. Automated Processes

Business automation goes beyond the physical production line. In fact, you can automate a wide range of human resources, sales and marketing processes with AI. Campaign management, email marketing, marketing segmentation and personalization are all prime candidates for AI automation.

If you’re a manufacturer, you can use AI to monitor raw materials stocks; if you’re a distributor, you can use AI to track inventory levels. AI spam filters and smart email categorization tools make communications-related decisions in the office; AI-powered voice-to-text makes communication easier on the go. Meanwhile, fraud detection systems automatically maintain security on your website, while dynamic price optimization tools keep you profitable.

5. Problem-Solving Expert Systems

Last but not least, AI-integrated expert systems emulate human experts to help you solve complicated problems. Expert systems are very specific — marketing expert systems provide advice by drawing upon vast amounts of marketing data, for instance. Businesses typically use expert systems to streamline processes and improve outcomes.

You might already be familiar with the following types of expert system:

  • Management information system: MISs gather data and present business information in a comprehensible format to help executives make better decisions.
  • Enterprise resource planning system: ERP systems help manage everyday business tasks. ERP software usually incorporates project management, compliance, risk management and supply chain optimization tools.
  • Executive support system: ESSs let users turn enterprise data into accessible executive-level billing, staffing and accounting reports.
  • Transaction processing system: TPSs collect, modify, process and retrieve data transactions. Examples include payroll, employee records and shipping systems.

Create Your Digital Strategy with DMI

If you’re curious about artificial intelligence, we’d love to hear from you. We’re big fans of AI here at DMI — we use AI to identify opportunities for digital transformation, and we build bespoke AI-enabled apps from the ground up. To find out how AI could benefit your organization, get in touch with us online or call one of our seasoned experts at 855-963-2099.