FICO Says Businesses Need to Explain Their AI Decisions
FICO's President of Software, Nikhil Behl, emphasizes the critical need for businesses to implement a robust "decision layer" to ensure explainable AI outcomes. This layer connects raw AI output with real-world business decisions, such as loan approvals or fraud flags, by integrating data, models, rules, governance, and observability. Behl states that without this infrastructure, companies risk making decisions without a clear record of how they were reached, leading to indefensible outputs. The discussion highlights that 85% of leaders report technology, data, process, and talent debt limiting their AI value, with only 6% having established and measured programs to address these issues.
The discussion around explainable AI, particularly from FICO's Nikhil Behl, points to a significant challenge for Asian enterprises adopting AI: the gap between AI models and accountable business decisions. Many companies in the region are rapidly deploying AI, but often without the necessary "decision layer" infrastructure that connects data, models, and business rules. This oversight can lead to opaque decision-making, which carries substantial regulatory and reputational risks, especially in highly regulated sectors like finance and insurance prevalent across Asia. The core issue is not just about AI's technical capabilities, but about process intelligence, as Genpact's CEO Balkrishan Kalra notes. For Asian firms, this means that simply investing in AI models is insufficient; the real value comes from redesigning operational processes to ensure transparency and accountability. The finding that 85% of leaders face debt in technology, data, process, and talent underscores that many Asian companies may be struggling to translate AI investments into tangible, governed outcomes.
