State of AI Decision Making 2026

Key findings

Four numbers that reframe the conversation.

67%

of knowledge workers use AI tools at least weekly — but most use them for summarising and drafting, not for structuring decisions.

<12%

use AI specifically to work through a high-stakes decision. The gap between AI adoption and AI decision support is wider than most assume.

3–5×

the average professional faces three to five high-stakes decisions per year. These are the decisions that shape careers, finances, and relationships for years afterward.

dimensions define genuine AI decision support: bias detection, structured framing, pre-mortem capability, and decision recording. Most LLMs address zero of the four.

What the report covers

The AI adoption landscape for decision-making
What people actually use AI for — and what they avoid
Why LLMs are structurally limited as decision tools
The cognitive bias problem: mirrors vs. correctors
What genuine structured decision support requires
The DecisionsMatter 6-step framework examined
Practical recommendations for individuals and teams

The report draws on decision science research from Daniel Kahneman, Gary Klein, Philip Tetlock, and others — applied to the specific challenge of using AI tools for high-stakes personal and professional decisions. References and further reading are included.

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About the author

Harish Keswani is the founder of Business Mojo and the author of The Decisions Matter Toolkit, out now. He built DecisionsMatter.ai — an AI decision assistant that structures high-stakes decisions using frameworks from Kahneman, Klein, Welch, and Munger.

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