Watch this 27-minute on-demand session to see why AI-inferred, self-reported, and manager-assessed skills data is claimed, not proven — and what it actually takes to build defensible, decision-ready skills data.
Most workforce skills data falls into one of three buckets: AI-inferred from projects and performance data, self-reported by employees, or manager-assessed through reviews and endorsements. The problem? All three are claimed, not proven. Building a skills strategy on claimed data is a risky bet when the decisions on the line are promotions, hiring, and where you invest next.
| The credibility problem. Why skills inference programs lose credibility before they ever influence a staffing decision |
| The validation shift. How validation through real work changes the equation, and why not every skill needs it. |
| Peer-driven validation at scale. What peer-driven validation actually looks like across a large, distributed workforce. |
| Building a defensible data layer. How to build a workforce data layer that holds up when it matters most. |
Most organizations collect trust-based data, then make decisions that require evidence-based data. That gap is the core problem the session addresses.
When an organization says it has "skills data," it usually means one of five things happened: someone completed a course, someone self-reported a proficiency, a manager filled out a rating form, an algorithm inferred a skill from a job title, or software scraped it off a resume. None of that is evidence of capability. All of it tends to get treated as if it is.
Inference still has a legitimate role. At enterprise scale, direct observation of every employee isn't practical, and resumes, role history, and self-report make skill visible enough to start working with. The rule of thumb: not every decision requires proof, but the higher the stakes, promotions, mobility moves, program investment, the higher the burden of evidence should be.
Before letting any data drive a real decision, the session walks through five questions most workforce data can't answer: is it observable, repeatable, contextual, comparable, and outcome-linked? Most self-reported or AI-inferred data fails at least three of the five. Structured peer validation, real work evaluated by trained peers against a shared rubric, is built to pass all five.
Key Takeaways
- Most workforce data is trust-based: proxy signals treated as proof.
- Inference has a legitimate role as a starting point, not a decision-maker.
- The higher the stakes, the higher the burden of evidence should be.
Who This Is For
CLOs, Heads of L&D, and Talent Development leaders looking for validated and reliable skill data.
Presented by Peerceptiv, built on 16+ years of peer learning research from the University of Pittsburgh.
| Watch On-Demand |