Track OAI-LEAD 6 domains · 50-item mocks Free · Academy-aligned Independent preparation

Independent, executive-grade preparation for leading AI adoption — built from publicly available OpenAI learning objectives, not from any official OpenAI exam. This track prepares you for the OpenAI Academy AI Leadership course and assessment. It is not an official OpenAI course or practice exam, and Academy badges and pathway certificates of completion are not certifications. Read the credential landscape and assessment model pages before your first mock.

What this track prepares you for

This track mirrors the standalone Academy course AI Leadership — at 180 minutes, the longest single course in the Academy catalogue. Unlike the product-specific courses, its focus is strategy and adoption rather than one tool, and its published objectives are to set priorities, align stakeholders, and create an initial AI strategy draft. It is a standalone course: it does not belong to the Foundations, API or Codex pathways, so completing it earns a course badge but not a pathway certificate. The audience is executives, functional leaders, and transformation and enablement owners.

Our material goes deeper than the guided course, because independent preparation must build the recall and judgement that survive a randomised assessment. Every domain here gives you frameworks, decision tables and board-ready artefacts you can apply the next morning. There is no code in this track — the skills are organisational, financial and political.

Blueprint

#	Domain	Weight	Items (approx.)	Track page
1	Opportunity and Value	18%	~9	D1
2	Strategy and Roadmap	18%	~9	D2
3	Governance and Risk	18%	~9	D3
4	Adoption and Change Management	16%	~8	D4
5	Workforce and Enablement	14%	~7	D5
6	Measurement and ROI	16%	~8	D6

Where the marks are

Opportunity, Strategy and Governance are each 18% — together 54% of the mock. The through-line is judgement under organisational reality: sizing value credibly, sequencing a roadmap, and building governance that enables rather than blocks. Master those three before drilling adoption, workforce and measurement.

Two independent mock exams

This track ships two independent, domain-weighted mock exams. Mock Exam 1 is your diagnostic — sit it first, untimed, to find your two weakest domains. Mock Exam 2 is deliberately harder — more multi-constraint stems and FIRST / BEST / MOST cost-effective / TWO qualifiers — and is your readiness gate: reach 80%+ before taking the Academy assessment. All items across both mocks and the domain pages are distinct.

The mindset this track rewards

The AI Leadership assessment rewards a leader who treats AI as a means to stated business priorities, governed and adopted through people, and measured honestly. Correct answers tend to:

Trace every initiative to a stated priority, and kill the ones that trace to nothing.
Size value credibly — driver-based, with assumptions, a counterfactual and a range — never a hero number.
Sequence prove → scale → embed, gating scale on baselined benefit, not popularity.
Govern to enable: tiered decision rights and controls turned on, not a committee for everything.
Change behaviour, not just buy licences — managers model, champions carry, the majority follows.
Level with people honestly about workforce impact, and report to the board without overclaiming.

Wrong answers tend to: chase the biggest number, scale the unproven, route everything to a committee, count seats as adoption, reassure falsely about jobs, and attribute the whole delta to AI.

Suggested time allocation

A focused 20-hour plan, weighted by exam marks and by how much practice each domain needs.

Domain	Weight	Hours
Governance and Risk	18%	4
Opportunity and Value	18%	3.5
Strategy and Roadmap	18%	3.5
Measurement and ROI	16%	3.5
Adoption and Change Management	16%	3
Workforce and Enablement	14%	2.5

Hands-on preparation checklist

Leadership skills are built by doing the artefacts, not reading about them. Do these in a real context — your own organisation or a realistic hypothetical.

Draft a one-page opportunity frame for a real candidate, running it through the V-A-L-U-E screen (D1).
Write a driver-based benefit estimate with assumptions, a counterfactual and a range, and split cashable from capacity.
Produce an initial AI strategy draft with the eight sections and a prove → scale → embed roadmap (D2).
Map a real use case through G-U-A-R-D and name the enterprise control (SSO, SCIM, EKM, RBAC, residency) each requirement needs (D3).
Diagnose a stalled or hypothetical rollout with A-D-O-P-T and design a champion network and use-case library entry (D4).
Build a capability map for one team and route roles onto Academy pathways, describing badges vs certification accurately (D5).
Write a credible board line for an initiative with a baseline, a range, an attribution method and net run cost (D6).
In a real ChatGPT account, generate a draft strategy narrative from your notes, then verify and edit it — practising the leader-as-reviewer role the tracks teach.