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AI Decisioning Platforms – It’s Time To Own One


I got a chance to listen to Mike Gualtieri of Forrester talk about his recent Wave report on AI Decisioning Platforms. This focuses in on a core set of vendors and compares them in detail as a follow-up to his earlier AI Decisioning Landscape report (which included Decision Management Solutions with our DecisionsFirst Modeler product).

AI Decisioning Platforms are a superset of ML/AI Platforms (which Mike also covers) and this wave represents an evolution – it started as a review of Business Rules Management Platforms, evolve to talk about Digital Decisioning Platforms and now focuses on AI Decisioning Platforms to emphasize the value of these platforms to those deploying and exploiting AI.

Mike pointed out that making decisions is the best possible use case for AI – especially as you should consider making a recommendations as a decision. He emphasized that enterprises rise or fail based on the collective efficacy of their decisions. And, while some of those decisions are big, strategic decisions, many more are rapid, transactional and operational. He also pointed out that insights are perishable, real-time insights especially so, meaning that decisioning really matters to the effective use of real-time insights. And as the time to decide shrinks, enterprise need to do more real-time decisioning.

Legacy architectures are not geared to this kind of data provisioning while legacy development approaches – writing code – is not going to keep human experts in control. Mike thinks a focus on “human governed AI” is essential and this means using an AI decisioning platform that combines a broad set of technologies, supports rapid learning loops, and can have industry accelerators.

Before getting into the details of the platform, Mike reminded the audience to begin “Decisions First”, pointing out that before using one of these platforms you need a decision model that combines several elements- rules, ML, AI , optimization. Our experience tracks strongly with Mike’s – you NEED a model first, ideally one built using the Decision Model and Notation (DMN) and a top-down, business-centric approach.

He then identified 9 of the most important criteria that were used in the Wave

  1. Data
    An ability to connect to sources, manage features and pipelines, support data annotation and cleansing.
  2. Provide a range of intelligence technologies
    • Statistics and queryable analytics
    • Pure math
    • Constraint based optimization / Operations Research / Mixed Integer Programming
    • Machine Learning
    • Human decision logic as rules, policies knowledge and processes. Capturing this business expertise is an essential feature of an AI Decisioning Platform he said.
  3. Low/no code.
    Tools for business experts e.g. decision modeling, abstraction as well as productivity tools for data engineers, data scientists and developers.
  4. Composability and reuse to drive enterprises decision agility, strong collaboration tools
  5. Trust, understanding and transparency. Business simulation is a critical element.
  6. Management (several layers up on top of Kubernetes)
  7. Model Ops – not just MLOps but a more holistic Ops function that deploys your whole decision model
  8. Multiple deployment options
  9. Scalability

Mike wrapped by pointing out that AI Decisioning can’t deliver itself – business users need to define the strategy criteria. Business experts MUST decide what and how to decide!

You can get reprints of the report directly from Forrester (if you are a subscriber) or from vendors like FICO who might offer it for free. If you want help selecting an AI Decisioning Platform or maximizing the value of one, drop us a line – that’s what we do.