People analytics leadership for AI-enabled work

Frame the decision before opening the data

Begin with a bounded question: for example, whether a learning programme should be adjusted after a controlled pilot. Write down the decision-maker, affected group, decision date, available interventions, and consequence of a wrong conclusion. Then identify whether the people analytics leader is the owner, adviser, reviewer, or data steward for each step.

Do not start with every available field. Record the minimum data needed to answer the question, why each field is relevant, the lawful and organizational authority for its use, who may access it, and when it should be removed. Separate operational records from survey responses, assessment evidence, manager observations, and AI-generated classifications. These sources have different denominators and failure modes and should not be blended merely because they share a person identifier.

The NIST Privacy Framework is a voluntary risk-management tool focused on identifying and managing privacy risks to individuals. Its guidance on profiles and data-processing roles supports making purpose, processing context, affected people, and responsibilities explicit. It is not a substitute for applicable law or internal review.

Sources: [1]

Build the decision-evidence ledger

Create one row per measure. Include the source system, population, inclusion rule, time window, numerator, denominator, missingness, transformation, AI contribution, validation check, privacy risk, access owner, interpretation limit, and decision the measure may inform. Add a separate field for what would change your conclusion.

Keep observations separate from inferences. “Forty-two of fifty invited participants completed the exercise” is an observation. “The programme improved capability” is an inference that requires a suitable comparison, a defensible measure, and evidence beyond attendance. AI may help classify free-text themes or draft a summary, but the ledger should retain the source, instructions, version, exceptions, and human review.

The OECD report Building an AI-ready public workforce connects workforce capability with governance, strategy, experimentation, and continuous learning. Its public-sector findings are useful design prompts, but they do not establish outcomes for a particular employer or authorize reuse of workforce data.

Sources: [2]

Worked artifact: a synthetic learning-pilot review

Imagine a synthetic organization reviewing an AI-writing workshop. The initial dashboard shows 84% completion, a higher self-confidence score, and more weekly use of an approved assistant. The decision-evidence ledger reveals three problems. Completion excludes six people who could not access the exercise; confidence has no stable pre-programme baseline; and tool activity does not show whether outputs were checked.

The people analytics leader narrows the decision. Instead of claiming capability improvement, the team asks whether the next pilot should add verification practice and an accessible alternative format. The ledger adds a small, consistent simulation scored for source use and error checking, plus an accessibility log and missingness review. Results are reported by sufficiently broad groups, with suppression rules agreed before analysis.

The next cohort completes the simulation, but the sample remains small and participation is not random. The leader reports observed task-bounded patterns and a confidence limitation, recommends another controlled iteration, and keeps individual evidence out of unrelated performance decisions. This example is synthetic and is not proof of Two Prune customer outcomes.

Verify the evidence chain

Reproduce every published metric from its stated population and transformation. Test missing and duplicate records, confirm time boundaries, inspect AI-assisted classifications against a varied sample, and look for groups systematically excluded by access or collection choices. Ask a privacy or legal reviewer to evaluate jurisdiction-specific obligations rather than treating a generic framework as compliance advice.

Before release, require a decision owner to sign off on the conclusion, limitations, permitted use, and review date. Two Prune may supply task-bounded assessment evidence from a controlled simulation for human interpretation. It does not prove workplace performance, set employment decisions, or make a people analytics conclusion automatically.