Decision Intelligence For Dummies - Pamela Baker

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realized that there is a point — they just hadn’t arrived at it yet.

      Eventually, the data driven model was flipped into a decision driven model as people experimented with making the point first and working their way back to the start from there. Decision intelligence is the name of the game where the gamers can all be winners. Now every move made has a point — a point that has value. That’s because the point is based on a decision aimed at creating a specific business impact.

      The current turn to decision intelligence

      Several leading AI luminaries and tech giants have been pioneers in, and first adopters of, decision intelligence. They've already added the title of chief decision scientist to their leadership ranks. One example is Google’s eminent chief decision scientist, Cassie Kozyrkov, who spends her days at Google democratizing decision intelligence and developing a more reliable AI approach. She also teaches it to others via conference speeches, YouTube videos, and writings in many online publications.

      Kozyrkov appears to embody decision intelligence, partly because of her formal training in economics, mathematical statistics, psychology, and neuroscience. Decision intelligence incorporates all these disciplines — and then some. Although not all who share her title possess the same skill mix, they nevertheless do share strong critical thinking skills as well as a thorough understanding of creative problem-solving strategies, decision theory, and decision science approaches. (For those not familiar with the term, decision science focuses on decisions as the unit of analysis; it is the interdisciplinary application of business, math, technology, design thinking, and behavioral sciences to the decision-making process.)

      Every day, more leaders are stepping forward to endorse the decision intelligence framework and explain its workings. Many of them work in AI, but others hail from disciplines collectively known as the decision sciences. Business leaders outside the technical domain are also catching on and reveling in their official return to the helm, as opposed to following data’s lead (which most never did anyway), and armed with a better strategy. They’re also happy about being able to keep their traditional analytics and tools. You don't win battles by limiting your options or abandoning your investments.

      At its essence, AI automates decisions that are executed rapidly in an exceedingly large number of instances, often simultaneously. You train it by having it work with task-related data sets so that it can recognize what it’s looking at in other data sets and learn from the patterns it finds there. Then it makes decisions based on well-defined business rules. (The reality is a good bit more complicated than that, but that's pretty much the gist of it.)

      For example, banking institutions use AI to automatically decide which loan applications to approve and which to reject. This is how you can get an answer on your loan application within seconds, no matter how many other people are applying for loans at the same time you are. AI makes these decisions based on the rules it has been given, such as a range of acceptable credit scores, length and types of employment history, items of public record, and other such risk weighting values. AI is able to make such decisions on each individual application, yet at enormous scale and all of it within seconds or minutes. Therefore, borrowers can receive immediate responses to their applications, and lenders can secure more loan deals in minutes than they previously were able to secure over a period of months and at the larger payroll cost of many manhours.

      AI is set to continue to serve in this and other automated roles for the foreseeable future. As a technology, it will continue to improve as all technologies do, but placing it within a decision intelligence framework means that its performance will improve exponentially because it is given not only rules to follow but also a target to aim at. Its tasks will be set upon a path of specific actions necessary for creating a specific business impact, and it will faithfully pound away at these tasks until its model decays or someone makes a new model to create another path leading to another targeted impact.

      

Other technologies, such as robotic process automation (RPA) and application programming interfaces (APIs), integrate processes. (RPAs are now called virtual workers because they mimic how human workers work, including interacting with user interfaces in the same way.) As RPAs continue to automate processes that were previously difficult to automate, AI can be added to make some automated decisions affecting these processes as well. In other words, the whole of technology engaged in decision making is getting smarter and better and more able to work together.

      

AI is better cast in the role of sidekick, where it augments human decisions rather than dictates or directs them. The same is true of analytics tied to other automated processes as well.

      Much of the decision intelligence revolution is happening out of the end user’s line of sight, but there’s one place where anyone can see the changes unfolding: AI digital assistants such as Google Assistant, Alexa, and Siri. Watch closely as they move from giving you facts in response to your questions to making unprompted recommendations based on your behavior and moods.

      Fact reporting such as, “Here are pharmacies near you” or “The name of that song is ABC” will begin to shift to customized and unprompted recommendations. They may look and sound something like this: “XYZ Restaurant has added one of your favorite dishes to its menu. Would you like for me to book the opening in the reservation schedule on Thursday at 7pm and put it on your calendar?” Or, it may say something like this: “Would you like for me to place your favorite coffee order for the pickup window? The one a block from your meeting place has less than a 10 minute wait.”

      The AI assistant will also produce files for meetings and other handy actions as the user moves through their day. As sidekicks in a user’s personal and professional life, the augmented activities will be far more productive than had the human personally tended to all the details and micro decisions.

      In digital decision-making, AI will improve at everything it now does — and then some. For example, it will improve at writing algorithms to rapidly meet an organization’s or researcher’s desired outcomes. That means that, for today and far into the future, AI will be in a position to continue its role as sidekick, producing everything you need to win the day. It’s unimaginable that AI won’t have some role, small or large, in most Decision Intelligence processes.

      Though the decision intelligence framework is perfect for guiding AI to consistently produce business value for you, the methodology can be used with no digital data or machines. For example, you can use AI to make decisions on a spreadsheet, on the back of a napkin, on a single sheet of paper, or even on a wall (using a crayon, of course). That’s because the process you use is up to the decision-maker to choose. The Decision Intelligence process itself can be quick and short, or it can be quite complex and take some time to complete. You may want to start with a SWOT table listing the Strengths, Weaknesses, Opportunities, and Threats when making your initial decision. From there, you can determine the steps you need to take to make your decision render a desired impact in the real world.

      The

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