AI, Productivity and the Operating Model: What It Really Takes to Scale
AI, Productivity and the Operating Model: What It Really Takes to Scale
Executive realities once AI moves beyond pilots
As AI moves from pilots to everyday use, the limiting factor is rarely technology. It is the operating model. In this fireside conversation, a COO perspective cuts through ambition to examine how AI reshapes productivity, decision rights and accountability once it touches core workflows. The discussion focuses on where value materialises, where friction accumulates, and which executive choices matter most when scaling AI across HR, Finance and Operations.
This session explores
- Where AI-driven productivity gains emerge early versus slowly.
- Differences between task acceleration, throughput and decision quality.
- Operating model assumptions that fail as AI scales.
- Decision rights and accountability across HR, Finance, IT and Operations.
- Governance trade-offs between speed, trust and control.
Learning outcomes
- Recognise where AI genuinely improves productivity and where it does not.
- Diagnose operating model friction that limits AI value at scale.
- Clarify HR’s contribution to cost, capacity and risk decisions.
- Understand which governance controls are essential versus excessive.
- Re-sequence executive actions for scaling AI in 2026.
26 February 2026
09:40-10:10 CET
Ann Jameson
COO Microsoft Switzerland
Value
Lens
Learning Pathways
Why this matters
European organisations face rising productivity expectations, constrained labour budgets and increasing scrutiny of AI-enabled decisions. As AI moves into core processes, leaders must adapt operating models, governance and accountability to realise value without increasing risk, coordination cost or organisational drag.