Listen to my podcast with James Taylor, the CEO and Principal Consultant of Decision Management Solutions. James is author of the book, Digital Decisioning: Using Decision Management to Deliver Business Impact from AI. In this podcast, we discuss how decisioning has changed in the age of digital transformation, how companies need to break down the decisions they make, how to decide which decisions can be automated, the challenges of bringing AI into an organization, predictive analytics, and how, in the future, companies are going to have to be ruthless with their decision-making. Give it a listen.
[02:24] Being data-driven is taken for granted. It wasn't that long ago, we were still arguing about the relative value of gut decisions and being data-driven and trying to persuade people that being data-driven was a good idea...Now it's about how!
[02:53] For many organizations, really the sort of the bit that isn't digitized now is their decision-making, You know, they've got digital data, they're storing it all, their storing digital content, they're using digital processes to move it all around, and then we're still using people to make all the decisions.
[04:49] We also find it helpful if people think about micro-decisions, as we call them, which is this idea of finding places in their business where they make the same decision for everyone when they could in fact, make a different decision for each person.
[07:37] When people set up AI teams, AI groups, AI programs, they're being technology lead, right? And that's a mistake.
[09:18] The way to succeed is to stay really focused on the business problem, the decisions you're trying to deal with, mix and match technologies that, some of which are what now we would now call AI.
[11:28] If you read the Amazon annual report, he says, "You know, the kind of improvements being made by AI are often invisible, small, incremental improvements to operational effectiveness."
[12:24] Companies think in terms of a few predictive models. And we look a companies and we look at the problems and we look at how we break the problem down and we go, "Man, this company is going to need hundreds of models, but that's got to be a corporate wide capability."
[14:17] I hear a lot of people say, "Oh, perhaps there's a new business in our data." Realize you're a big stodgy insurance company but maybe your data will let you become this cool data-driven company. Well, no, it probably won't.
[15:42] These are decision-making technologies. We invest in analytics to improve the quality of our decision-making.
Decision Management Solutions is a consulting services provider focused on helping organizations make more data-driven decisions by applying advanced analytics, artificial intelligence (AI), business rules, and supporting technologies. Oriented to the F500, DMS is especially knowledgeable in insurance, banking, manufacturing, telecommunications, travel and leisure, health management, and retail. Company leadership are engaged as co-submitters to Decision Model Notation (DMN) standard and as contributors to the International Institute of Business Analysis (IIBA) Business Analysis Body of Knowledge® (BABOK) sections on decision modeling. Vendor services include strategic advice on product strategy and road map, independent validation of solutions, product reviews, and independent market research.
Data Decisioning's focus is "the decision", as the key output of management. Big data or middleware or analytics or AI, all these technology conspire to support management in decision-making. Decisions can be "discrete, operational or strategic", but they are the "stuff of management". For this reason, James Taylor's new book, and the interview shared here, is a much needed exploration of the "how" of decision-making. Because better decision-making is a matter of getting details right.
Mr. Taylor's "how" will be very much welcomed by executives facing pressures to exploit technology. On the road to business transformation, we are all supposed to embrace AI and analytics. And embrace dozens or even hundreds of other specialized technologies; "chat bots" are just one example. But what is the framework within which one chooses a technology? Any technology business case needs to highlight the value of that technology. And a business case needs to answer the question "how does that technology contribute to mission success?"
By reverse engineering the decisions that an organization needs to make, it's possible — indeed even much easier — to trace the value of any given technology. If a technology contributes to better decision-making, then that value can be justified.
As Mr. Taylor points out, not only is any technology justified by how it supports management decision-making, but specific technologies of decision support are especially helpful. Decision support technologies naturally include analytics and AI — but one should assess as well the purpose-built technologies of business rules management. In fact, as complements to analytics and AI technologies, business rules technologies have a major role — and are under-exploited. There are real wins available for adopters of business rules technology!
What's your inventory of decisions, decisions at any level, that define your organization? Are you managing decision-making processes explicitly? What opportunities can you identify where you could make more decisions, better decisions, faster decisions? Will technology help? Will traditional management attention help?
Read the podcast transcript: How Decision Transformation is Essential to Digital Transformation!