Faster Isn’t Smarter: A Systems View of Decision-Making at AI Speed
About this session
Enterprise AI presents a paradox. Eighty-eight percent of organizations now use AI in at least one business function, and controlled studies demonstrate meaningful productivity gains on individual tasks. Yet only 39 percent of organizations report any enterprise-level EBIT impact, and most remain unable to scale AI beyond isolated use cases.
Most explanations blame the technology, the data, or the talent. This talk argues that the gap is structural: AI compresses the production side of decision loops while often leaving the learning side unchanged. Organizations generate analyses, recommendations, and outputs faster, but the feedback that reveals whether those decisions were correct may remain slow, fragmented, or invisible. Meanwhile, standard metrics such as adoption and time saved measure only half the loop, while rework, downstream consequences, and the potential erosion of human judgment emerge later, often in someone else’s budget.
Grounded in practitioner experience, including leading a Microsoft Copilot deployment that reached 83 percent weekly active usage among enabled users, this session gives leaders three practical tools: a metrics audit that separates activity, output, and outcomes; a method for classifying decisions by feedback speed, causal clarity, reversibility, and consequence; and a graduated autonomy ladder that increases AI authority only as evidence and control maturity accumulate.
When an organization acts faster than it can learn, acceleration becomes instability.
Speaker
Key takeaways
- A metrics audit that separates activity, output, and outcomes
- A method for classifying decisions by feedback speed, causal clarity, reversibility, and consequence
- A graduated autonomy ladder that increases AI authority only as evidence and control maturity accumulate