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Director of HR

Drive performance management and talent development

Enhances◐ 1–3 years

What You Do Today

Administer the performance review process, manage talent calibrations, and support development planning. Ensure the process develops people rather than just documenting them.

AI That Applies

Performance analytics that detect rating inconsistencies and identify high-potential employees based on multiple data points.

Technologies

How It Works

The system ingests multiple data points as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Performance data becomes more useful for talent decisions.

What Stays

Coaching managers on how to give feedback and have meaningful development conversations.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for drive performance management and talent development, understand your current state.

Map your current process: Document how drive performance management and talent development works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Coaching managers on how to give feedback and have meaningful development conversations. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Workday tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long drive performance management and talent development takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your CHRO or VP HR

Which training programs have the highest completion rates, and which have the lowest — what's different?

They're deciding the AI adoption strategy for the function

your HRIS or HR technology lead

How do we currently assess whether training actually changed behavior on the job?

They manage the platforms that AI tools integrate with

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.