Stop Automating the Easy Things
About this session
Most AI rollouts target the wrong layer of the organization. Leaders automate the easy, repetitive work first because it's the path of least resistance, but that work is the substrate on which judgment is built. Entry-level reps, low-stakes decisions, pattern exposure over time, these aren't inefficiencies waiting to be optimized away. Strip them out, and you remove the conditions under which junior people become senior ones.
This talk introduces Decision Architecture: a framework for deciding where AI should take on a task and where humans need reps. Drawing on 20+ years building capability inside Target, Instacart, PayPal, and BMW, I'll cover the Two-Clock Problem (performance metrics move in 12 to 24 months, judgment takes 5 to 7 years, and most orgs optimize for the wrong one) and a five-lens framework for evaluating automation decisions before they hollow out your bench strength.
This isn’t an anti-AI talk. AlphaFold is the model: AI absorbing a hard constraint while expanding what experts can do, not degrading it. The problem isn't automation. It's automating without architecture.
Speaker
Key takeaways
- Why automating "easy" tasks is the highest-risk move in AI rollout, not the safest one
- The Two-Clock Problem: why performance metrics and judgment development run on incompatible timelines, and how to stop optimizing for the wrong one
- The five-lens Decision Architecture framework (Capability, Concentration, Movement, Calibration, Accountability) for deciding what to automate and what to protect