When AI sounds certain, ask why

Why enterprise AI needs transparency, traceability, and explainable reasoning

by · TechRadar

Opinion By Thor Olof Philogène Published 17 September 2026

(Image credit: Getty Images)

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AI has become remarkably good at producing answers. But smart business leaders don't make decisions based on answers alone. They ask where the information came from, what assumptions shaped the conclusion, and how much confidence they should place in the recommendation.

Thor Olof Philogène

Founder and CEO of Stravito.

Those questions are becoming increasingly important as AI takes on a larger role in enterprise decision-making. Marketing teams are now using it to evaluate campaign concepts. Insights teams are asking it to synthesize years of consumer research. Executives are relying on it to identify growth opportunities, assess competitive threats, and pressure test major investments.

Once AI starts influencing decisions instead of simply accelerating work, understanding how it reached a conclusion becomes just as important as the conclusion itself.

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Every recommendation deserves an explanation

Consider a CPG firm looking to enter convenience stores while continuing to sell products in supermarkets. The decision calls for balancing dozens of variables, from the impact on supermarket sales and pricing to customer demographics, channel growth, and long-term brand implications.

No single report has all this information. A leader needs to compile it from multiple sources and analyze it comprehensively before deciding whether to pursue the expansion.

AI can dramatically accelerate that process by synthesizing years of research, identifying patterns across hundreds of documents, and surfacing insights in minutes – helping teams to spend less time gathering information and more time evaluating it.

But AI doesn't eliminate the need for judgment. Leaders are still responsible for understanding the reasoning behind the recommendations they ultimately act on.

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