The Great Unlock, or Why Decision Management + AI is Like Peanut Butter & Jelly

Rafael Goldberg


Pop Quiz: What combination of things or people do you think has created an outsized impact on the world?

Let’s start with peanut butter and jelly. The average American schoolchild will consume about 1,500 peanut butter and jelly sandwiches by the time they graduate from high school, according to the Peanut Advisory Board. This timeless combo is a runaway favorite for inclusion in my personal foodstuffs hall of fame.

Or if you’re a science nerd like me, consider scientists James Watson and Francis Crick, both accomplished researchers in their own right. But their collaboration in the 1950s led to the discovery of the structure of DNA, which stands as one of the most significant milestones in the history of scientific exploration.

For a more contemporary choice, there’s Beyonce and Jay-Z. Individually and as a power couple, they have left an indelible mark on the realms of music, popular culture, and fashion.

The point of this exercise is that collaboration between already awesome entities can still create even more powerful synergies. Marketer and entrepreneur Jeff Galloway, explains this as an “unlock,” where the discovery of an accelerant for the brand, product, or service, is invisible in plain sight.

We’re now seeing another unlock unfolding as AI takes hold within business. Organizations across industries have adopted AI methods like machine learning (ML) to uncover insights to customer behavior and operations, betting significantly on data science practices and technologies. And since last year’s breakout hit of OpenAI’s ChatGPT and the ensuing GenAI arms race, these same organizations are experimenting with how to leverage Generative AI in their business for operational efficiency and competitive advantage.

The Great Unlock

So, what’s the great unlock? It’s combining this exciting field of probabilistic engineering with the equally exciting field of deterministic engineering represented by decision management. Decision management, a discipline that emerged 15 years ago from the field of business rules management, revolutionized software by providing a model of business logic. Just as Ted Codd’s relational model of data and third normal form of the late 60s and early 70s transformed data management, decision management transformed business rules management.

With the rise of decision management, gone were the days of IT developers coding rules from business requirements. Business analysts could now model their requirements directly, and then test, govern, and generate code for enterprise software solutions. Now, decision management and AI are combining for a great unlock of AI Decisioning.

Use Case #1: GenAI + Decision Modeling

Today’s decision management has vastly improved from the business rules era, but analysts still must become experts in decision modeling. By applying GenAI to the decision modeling process, the barrier to entry has been lowered significantly, and organizations can push decision ownership further into and across the business. Much like we have all become adept at chatting with OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Copilot, decision management vendors are now integrating GenAI chat features that provide analysts with the ability to describe their requirements by using natural language and applying the software’s algorithms to generate the decision model structures. Talk about a great unlock of productivity and democratization!

Use Case #2: ML + Decision Modeling

Insights derived from ML models are based on probabilities that need to be combined with declarative business decision models that enable action. Once ML-based outputs are incorporated into a business decision model, they can be deployed in core processes. Examples include ML model outputs to execute loan approvals for individuals meeting a specific risk profile or to straight-through-process a first notice of loss claim. After all, in making business decisions, there’s no place for “maybe.”

But operationalizing ML models often exposes skills and technology challenges. A highly technical field, it’s easy for businesses to become beholden to the black boxes of ML models and accompanying technologies. Like managing business decisions in the old business rules management era, ML model management gets complicated and expensive quickly. After years of business change requests and IT implementations, organizations can find themselves replicating the same spaghetti code nightmare with ML that they experienced with business rules. Decision management solved the business rules management problem. By combining decision management with ML, we can bring governance, transparency, and efficiency to the operationalization of ML insights.

Consider most analysts’ ability to encapsulate an ML model in their decision model visual workbench. Their business stakeholders can view in a simple visual structure how the business decision is being made through declarative rules determining the action with a predictive model input as a condition. Decision models and ML models are points across the decisioning continuum and combining them into a unified decision management system promises a great unlock to organizations across industries.

This view is shared by leading technology analysts. Mike Gualtieri, VP & Principal Analyst at Forrester Research, says, “A large enterprise is not going to have just one way of creating machine learning models.  They’re going to have half a dozen or a dozen, but they do need one way to use those models with their applications – to govern them and manage that process.  And I think AI decisioning is a great way to do that.”

The Final Word

I’m so excited about “The Great Unlock,” combining decision management with the power of generative AI and ML to unlock the next set of improved business outcomes. So, the next time next time you’re kicking back with a peanut butter and jelly sandwich or listening to Beyonce and Jay-Z, think about how AI and decision management can unlock the potential that’s hidden in plain sight.

For more information on the Great Unlock, listen to the Sapiens Insurance 360 podcast, “The Great Unlock: AI/ML and Decision Management for Transformation.”

  • AI
  • Decision
  • North America
Rafael Goldberg

Rafael Goldberg Rafael Goldberg is Head of Sapiens Decision, where he focuses on go-to-market, product strategy and overall operations. With broad experience across global software and consulting operations, Rafael has spent the last 10 years supporting clients implement and adopt enterprise decision automation systems.