Skills Intelligence That Makes AI Productivity Real
Skills Intelligence That Makes AI Productivity Real
The diagnostic layer behind credible AI at scale
Many organisations see early promise from AI pilots but struggle to convert them into sustained productivity gains. The root cause is rarely the technology itself, but weak understanding of work, skills and where capacity can realistically move.
This session sets out how skills intelligence, grounded in work diagnostics, enables leaders to make defensible decisions about automation, redeployment and reskilling. It provides the analytical foundation required before operating models can be redesigned and AI scaled with confidence.
This session explores
- Why AI productivity pilots rarely compound into enterprise-wide gains.
- The leadership decisions that skills and work diagnostics must support.
- A practical diagnostic loop linking skills signals to automation and redeployment choices.
- The minimum viable skills layer required to inform workforce and AI decisions.
- Common traps that undermine skills-led productivity programmes.
Learning outcomes
- Distinguish skills intelligence from static skills frameworks and taxonomies.
- Assess whether current AI productivity plans are evidence-led or aspirational.
- Define the minimum skills and work data required before scaling AI.
- Challenge teams and partners with sharper, decision-focused questions.
26 February 2026
09:10-09:40 CET
Value
Lens
Learning Pathways
Why this matters
Productivity pressure, rising labour costs and accelerating AI adoption are forcing leaders to justify workforce decisions under closer scrutiny from Finance, regulators and employee representatives. Without credible insight into work and skills, AI investments risk over-claiming value and underestimating organisational disruption.