Program Nomination Support Agent
A program nomination support agent is an AI agent for learning & development that helps the panel that selects people for leadership programs, tuition sponsorship, or high-investment cohorts by assembling each nominee's stated goals, learning history, and manager nomination note against the published criteria, and by showing where the shortlist is skewed by function, level, or location.
How does the program nomination support agent work?
What flows in, what the agent does with it, where a person decides, and what comes out.
Reads from
Published program criteria · Nomination forms and nominee statements · Learning and skills history · Aggregate demographic data
AI agent · runs when a program's nomination window closes
Program Nomination Support Agent
A person decides
Selection panel decides every place; AI never ranks anyone
Produces
Nomination pack per nominee · Criteria-coverage summary · Shortlist skew view (aggregate) · Panel decision record for audit
What does the program nomination support agent do?
Helps the panel that selects people for leadership programs, tuition sponsorship, or high-investment cohorts by assembling each nominee's stated goals, learning history, and manager nomination note against the published criteria, and by showing where the shortlist is skewed by function, level, or location.
What does it produce?
A structured nomination pack per candidate, a criteria-coverage summary, and an aggregate view of who is and is not on the list
Who decides?
The selection panel decides who is offered a place; the AI organizes the evidence against the criteria and highlights gaps and skews. It does not score, rank, or recommend individuals for selection.
What systems does the program nomination support agent connect to?
Examples of the kind of systems this agent would read from or write to, so you can picture it in your own stack. The actual set is whatever you run.
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HRIS
nominee role and demographics, aggregate
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LMS / LXP
learning history
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Forms / documents
nomination forms and packs
What data does it need?
- published program criteria
- nomination forms
- learning and skills history
- aggregate demographic data for skew checks
How would you measure it?
panel hours per selection round; completeness of packs as rated by the panel; consistency of decisions against stated criteria, per round; shortlist skew before versus after panel, per round, aggregate
What does a first proof look like?
Use last cycle's nominations for one program. The agent assembles packs against the published criteria and shows the shortlist's skew by function, level and location. The panel reads them alongside what they used last time.
You'd call it working when
Packs are complete and neutral, and the panel says the skew view changed at least one conversation.
What usually goes wrong?
- Letting a criteria-coverage summary become a de facto score; keep it descriptive
- Criteria that were never actually published; write and publish them first
- Skew views on small cohorts that identify individuals; apply group-size rules
What are the guardrails?
- Never scores, ranks or recommends individuals; organizes evidence only
- Selection is promotion-adjacent: works council or employee-representative review before use, and EU AI Act high-risk obligations checked
- Demographic data used only in aggregate skew views with minimum group sizes
- Panel decisions and rationale logged by humans; agent output kept as supporting material only
- Nominees can see what is in their own pack
What leaves your boundary is set per build; the inputs above are the ceiling, and where the model runs, what it retains, and the DPA are agreed with your security team before anything is connected.
Our read
The job itself is a decision about a named person. This agent exists as decision support only; the decision stays with the person named above.
Some suites offer this as an automated decision. We do not build it that way: the agent prepares, a named person decides, and the record shows who.
Selecting people for career-shaping programs is a decision about individuals with promotion consequences; protected characteristics and works-council review are in play
Where it sits in the order
Touches who gets a career-shaping place; needs published criteria, representative review and trust earned elsewhere first
Is a Program Nomination Support Agent worth building for your function?
That depends on your numbers, your data, and what else is on the map for you. The strategy month works that out.
Thirty minutes. Bring the number this would move.