Copilot Deployment at Scale: Lessons from the First 12 Months

Copilot Deployment at Scale: Lessons from the First 12 Months

People Analytics World PAWorld mark

Practical lessons on use cases, adoption, governance and value tracking

This session shares the practical realities of rolling out AI copilots at scale across HR and Finance. The speaker explains how use cases were selected, how prompts and data were prepared, and how users were trained and supported once the pilots ended.

It also covers governance, transparency, fairness safeguards and EU AI Act considerations, along with adoption challenges, value tracking and how capacity gains were converted into service improvements. Expect an honest, practitioner-ready account of what actually works.

This session explores

  • Selecting and validating copilot use cases across HR and Finance workflows.
  • Preparing data, prompts and guard-rails for compliant day-to-day use.
  • Training, onboarding and adoption support after the initial pilot.
  • Governance, transparency and oversight aligned to emerging AI regulation.
  • Tracking value to convert time savings into real service improvements.

Learning outcomes

  • Know how to prioritise copilot use cases that reduce friction in daily work.
  • Understand data, prompt and training preparation needed for sustainable adoption.
  • Apply practical governance measures for transparency, fairness and oversight.
  • Measure adoption, value and capacity gains with credible metrics executives trust.
  • Anticipate common pitfalls that stall copilot rollouts beyond pilot phases.
Zurich Switzerland DACH Europe People Analytics Conference
25 February 2026
14:15-14:45 CET

Josip Lazarevski

Global Director of Data Science and AI

Value

Lens

Learning Pathways

Why this matters

Organisations are under pressure to realise productivity benefits from AI without exposing themselves to regulatory, ethical or operational risk. Copilots promise major efficiency gains, yet most deployments stall after early pilots because of unclear guard-rails, weak adoption support, or missing value metrics. HR, Finance and IT leaders need workable methods for scaling AI assistants safely while maintaining trust and compliance.

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