Decisions under deep uncertainty: a practitioner's handbook for AI-era leaders
Decision-making frameworks for executives and leaders facing unprecedented uncertainty introduced by rapidly changing AI capabilities and enterprise AI adoption.
Authors
E. Rahman, AURELIUS, 11 contributors
Published
2029
Citations
174
Overview
Traditional decision models assume relatively stable operating conditions and predictable outcomes. AI adoption introduces unprecedented levels of uncertainty: capabilities are changing monthly, competitive dynamics shift rapidly, and organizational readiness is difficult to predict. This research develops decision frameworks tested with 120 leaders across diverse industries.
Methodology
Qualitative interviews with 120 C-suite executives making major AI decisions. Case study analysis of 45 enterprises tracking decisions and 18-month outcomes. Simulation modeling of decision outcomes under varying uncertainty conditions. Validation through advisory board of 8 Fortune 500 CDOs.
Key Findings
Traditional risk analysis fails in high-uncertainty environments because it assumes knowable probability distributions. Executives using probabilistic decision trees predicted outcomes with 34% accuracy in high-uncertainty AI contexts. Those using robust satisficing (optimizing for outcomes that work across multiple scenarios) achieved 67% accuracy in the same contexts.
Decision speed and decision quality show non-monotonic relationships: decisions made too quickly ignore critical information (65% success rate with <1 week decisions), but decisions made too slowly miss window-of-opportunity effects (68% success with >6 month timelines). The optimal window appears to be 3-8 weeks for major AI decisions.
Leader experience in traditional domains (finance, manufacturing) provides limited advantage in AI decisions. Leaders with direct experience in technology transitions, organizational change, or ambiguous business models showed 2.1x better AI decision outcomes regardless of industry background.
Decisions framed as 'bets with defined stopping points' outperformed decisions framed as 'commitments' by 3.2x in terms of course correction and value capture. Reversibility and learning are more important than certainty in AI-era decisions.
Impact & Application
Handbook downloaded 45K+ times. Used as curriculum in executive education at 8 business schools. Enterprise adopters report 34% improvement in successful AI initiative outcomes.
Contributors
Lead: Dr. Eva Rahmann (Executive Leadership school). Advisors from Microsoft, Google, JPMorgan Chase. Supported by Accenture strategy group for case study research.