Every second of every day, all across the globe, vital, life-affecting decisions are increasingly made not by people, but by AI decisioning. Yet, despite its ubiquity, for many, the question remains, what exactly is it?
According to Forrester Research, “AI decisioning platforms (AIDPs) provide enterprise business and technology teams with tools to author and automate business decision logic in a wide variety of applications by leveraging combinations of decision intelligence technologies such as business rules, machine learning models, mathematical models, and more.”
In a webinar hosted by InRule, Forrester Vice President and Principal Analyst Mike Gualtieri explained that AI decisioning platforms are comprised of repeatable decisions embedded in software processes, such as deciding if a transaction is fraud, whether or not to write an insurance policy, or the specific products to recommend to a customer.
Gualtieri detailed that the defining characteristic of an AI decisioing platform is that it allows business experts to design human decision logic in conjunction with machine learning models that either they create or are created by data science teams.
That’s the AI part of it – that it’s embedded and automated. But what sets AI decisioning platforms aside from other AI platforms is the incorporation of a human element to minimize risk. AIDPs include ML models that are protected by guard rails governed by human decision logic. This dynamic, humans-in-the-loop approach enables human oversight and intervention at application-approval step.
AI decisioning is uniquely accessible to non-technical business users and subject matter experts. A plain-language interface is a must-have for any competitive decisioning platform. Some other key capabilities that AI decisioning platforms should include:
- Explainability – As detailed in an earlier blog post, explainability answers the ‘why’ behind automated outcomes. Explainability builds trust, ensures compliance, and makes outcomes actionable. AI decisioning should feature complete, easy-read, detailed historical records of every rule change enabling users to trace a misaligned decision to its source.
- Open architecture – Few automated decisions are made by a single platform. Decisioning should accommodate integration with other AI platforms, giving users the power to combine different technologies into making a single decision.
- Multiple testing options – Knowing ML algorithms and automated decisions will yield intended results requires robust testing capabilities. The ability to run multiple test models, such as A/B testing, gives users the confidence to incorporate new technologies into decisioning, most-notably generative AI.
- Rich collaboration – Typically, decisioning is managed by a team. The best decision platforms facilitate collaboration among all stakeholders and SMEs.
To learn more about AI decisioning, download The Forrester Wave™: AI Decisioning Platforms, Q2 2023
