Worked Example: Finding Your AI Contribution
Turn domain experience into a direction and a two-week learning project.
All organizations, workflows, numbers, and results in these examples are fictional and provided for learning. Targets are proposed decision criteria, not validated standards or promised outcomes.
The person and starting point
Maya is a fictional HR operations specialist. She knows service-desk workflows, has improved escalation guidance, and enjoys helping colleagues use a clear process. She has not yet built an AI product.
Completed positioning session
Start with evidence of contribution and choose a small way to extend it.
- Experience
- Work understood deeply
- Evidence
- Contribution already demonstrated
- Direction
- Complementary role contributions
- Project
- A small result in two weeks
- Skill
- What to practice
- Review partner
- Support and access
- Evidence
- What to show
Completed responses
- Experience
- Policy lookup, escalation, and service handoffs; recognizes conflicting versions and incomplete questions.
- Evidence
- Standardized escalation guidance and trained analysts on policy lookup.
- Direction
- Practitioner: improve a daily workflow. Amplifier: teach colleagues to evaluate evidence. Builder is a capability to explore through a bounded prototype, not a new job title.
- Project
- Create ten fictional policy questions, write expected behavior, and evaluate cited drafts in an approved test tool.
- Skill
- Define test cases, inspect source support, recognize exceptions, and explain a failure clearly.
- Review partner
- A willing policy specialist checks expected answers and boundaries; no real personnel data is used.
- Evidence
- Ten annotated cases, one failed case, a revised brief, and a short explanation of human decision rights.
Completed two-week plan
Days 1-2: choose the workflow and confirm the approved test environment. Days 3-5: create synthetic policies and ten questions. Days 6-8: evaluate drafts and log failures. Days 9-10: improve the brief and discuss evidence with the review partner. Keep the focus on work quality, not the number of prompts used.
Completed review decision
Suppose Maya finds one draft that invents an eligibility condition. She documents the failure and revises the boundary to escalate individual decisions. Her next decision is whether to deepen evaluation skills, learn bounded building, or focus on enabling the team. The project demonstrates learning; it does not establish production readiness, a promotion, or a career outcome.
Record the next decision.
What will you do next, who owns it, and when will you review the evidence? Keep unresolved assumptions visible.