Skills-Based Organisation: What to Build, Prove and Park

Jenny Wu

Head of People Analytics Consulting · Ericsson

Ying Li

Snr Director, Global Head of People Analytics · PepsiCo

Aashish Sharma

VP, Workforce Intelligence · RTX

A skills-based organisation depends on robust data and practical adoption. This panel shares lessons on building a usable taxonomy, testing AI inference responsibly, and proving value with early pilots. Hear what global companies chose to build, what they proved in practice, and what they parked for later – with a focus on scaling the craft.

• Overlay vs rebuild: technical decisions in taxonomy design and governance.
• Validating skills data – thresholds, auditability, and AI inference methods.
• AI ethics and policy guardrails for skills-based systems.
• Early pilots – from gig marketplaces to skills-based learning programmes.
• Practical lessons on what to build, prove, and consciously park.

• Compare overlay and rebuild approaches to taxonomy with real-world examples.
• Apply validation models and AI inference responsibly and audibly.
• Design early pilots that prove skills data value within 90 days.
• Recognise adoption pitfalls and what to park until readiness improves.
• Translate technical building blocks into scalable workforce applications.

Organisations need scalable ways to capture, validate, and use skills data as work and technologies change faster than job structures. Without reliable taxonomies, validated skill profiles, and adoption across platforms, skills initiatives stall. Building practical, scalable solutions in the first 90 days is essential for impact.