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How AI is Revolutionising the Way We Make Decisions

In today's world, the landscape of decision-making is rapidly shifting towards AI-driven automation, impacting critical life decisions across the globe. The term "AI decisioning" might be widespread, but it remains a puzzling concept for many. According to Forrester Research, AI decisioning platforms (AIDPs) equip enterprise business and technology teams with tools to author and automate business decision logic using various decision intelligence technologies, including business rules, machine learning models, and mathematical models.

Recently, during a webinar hosted by InRule, Forrester Vice President and Principal Analyst Mike Gualtieri shed light on AI decisioning platforms. These platforms consist of repeatable decisions integrated into software processes, like determining fraud in transactions, issuing insurance policies, or suggesting specific products to customers. The distinctive aspect of an AI decisioning platform lies in its ability to allow business experts to design human decision logic in tandem with machine learning models, either created by themselves or data science teams.

One of the key features setting AI decisioning platforms apart from other AI solutions is the incorporation of a human element to mitigate risks. AIDPs include machine learning models protected by guard rails governed by human decision logic. This human-in-the-loop approach empowers human oversight and intervention during application approval steps.

Accessibility is a paramount feature of AI decisioning, catering to non-technical business users and subject matter experts. The use of plain language interfaces makes these platforms user-friendly and competitive. Additionally, some other vital capabilities that AI decisioning platforms should offer include:

Explainability - The ability to comprehend the reasons behind automated outcomes is crucial. Explainability fosters trust, ensures compliance, and enables actionable insights. AI decisioning platforms should maintain comprehensive, easy-to-read historical records of rule changes to trace misaligned decisions back to their source.

Open architecture - Automated decisions often involve multiple platforms. AIDPs should facilitate seamless integration with other AI platforms, empowering users to combine different technologies for more informed decision-making.

Multiple testing options - Ensuring intended results from ML algorithms and automated decisions demands robust testing capabilities. AIDPs should enable users to run various test models, such as A/B testing, instilling confidence when incorporating new technologies like generative AI into decision-making processes.

Rich collaboration - Decision-making is often a collaborative effort involving multiple stakeholders and subject matter experts. The best AI decision platforms promote collaboration and cooperation among all team members.

Implementing a Business Rule Engine such as InRule, offers numerous benefits, including increased agility, improved automation, enhanced compliance, better alignment between business and IT, improved accuracy, empowered business users, and scalability. These advantages contribute to operational efficiency, cost savings, risk mitigation, and improved customer satisfaction.


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