Strategic Workforce Planning in the Age of GenAI

GenAI is changing throughput, task mix and role design. This session shows how to reflect that – conservatively – in a driver-based workforce plan that Finance trusts. We connect demand drivers to capacity via productivity, attrition and hiring yield; define assistive vs automation scenarios with confidence ranges; and convert hours saved into capacity, cost or service outcomes. You will see the operating cadence, validation checks and decision rights that keep the model auditable, plus the unit economics used to secure funding. We close with a simple scorecard and a worked example to prioritise where to hire, reskill, automate or redeploy.

• Driver model – link demand to capacity via productivity, attrition and hiring yield.
• GenAI impact – assistive vs automation with task-level productivity ranges.
• Skills-based planning – critical capabilities, role clusters, mobility and reskilling.
• Capacity mix and location – FTE vs contingent, sourcing choices and risk trade-offs.
• Finance and governance – unit economics, convert hours to cost or capacity, validation and privacy.

• Use a minimal, GenAI-ready workforce planning model aligned with Finance.
• Quantify AI-driven productivity and convert into capacity, cost and service impacts.
• Run and compare baseline, assistive and automation scenarios.
• Build CFO-grade cases to hire, reskill or automate.
• Set cadence, roles and checks so stakeholders use and trust the plan.

Peter Louch

CEO · Vemo

Budgets are tight, skills gaps are widening and location choices carry cost and regulatory risk. Boards want quantified scenarios that include GenAI effects, not promises. Without a Finance-aligned model, hiring, reskilling and automation become guesswork – and execution stalls. Macroeconomic uncertainty and evolving AI governance raise planning risk, making repeatable, auditable workforce planning essential.