Session
Some Friction Was the Work
When Output Gets Cheap, Signal Gets Expensive
PAWorld26 NYC · 4–5 November 2026 · New York City
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– · Plenary
About this session
AI makes analysis, reports, summaries and recommendations easier to produce. But some of the work it removes was where people detected faulty assumptions, built expertise and understood the context behind a decision. Hayley Bresina explores why speed is not sufficient, why a nominal 'human in the loop' can fail, and how to redesign analytical workflows so access improves without losing the challenge, judgement and learning that make answers trustworthy.
Learning outcomes
- Distinguish unnecessary friction from work that protects decision quality.
- Assess whether human review provides a real opportunity to challenge an answer.
- Identify a safeguard or learning practice to retain when redesigning analytical work.
This session will explore
- Identify the checking, interpretation and learning embedded in analytical work.
- Examine why a nominal human review can fail to challenge an AI-generated answer.
- Explore workflow changes that preserve meaningful judgement as access and output increase.
Context
Some analytical friction helps people discover faulty assumptions and learn why an answer is credible. Removing it can accelerate output while weakening the expertise and challenge on which decisions depend.
Speakers and contributors
Hayley BresinaPresenterOne AI - Product ManagerOne Model