AI Transformation Leader study plan

Define AI strategy in business terms by writing a one-page value thesis linking AI capabilities to a specific revenue or cost lever

Map the organization's value chain and identify three candidate AI use cases ranked by business impact and feasibility

Distinguish strategic AI bets from operational AI improvements and justify the portfolio mix

Draft the questions to ask executives to surface AI-alignment gaps between stated strategy and funded initiatives

Translate the top use case into data requirements by listing needed sources, grain, freshness, and quality thresholds

Assess current data estate against those requirements and flag gaps in availability, lineage, and ownership

Explain how business-alignment decisions constrain data architecture choices and vice versa

Identify build-vs-buy-vs-partner tradeoffs for the data and platform layer

Construct an AI risk register covering model, data, operational, and third-party risks with likelihood and impact ratings

Assign risk ownership and define escalation paths for high-severity AI risks

Apply responsible-AI principles to a concrete use case by writing guardrails and human-oversight requirements

Draft the minimum governance artifacts (policy, review board charter, model inventory entry) needed before deployment

Design an AI-ready data architecture sketch covering ingestion, storage, feature/context layer, and serving

Define model lifecycle stages from problem framing through monitoring and retirement, with entry/exit criteria per stage

Specify evaluation criteria and acceptance thresholds for a model before any build begins

Identify monitoring signals (drift, quality, cost, safety) and the response playbook for each

Conduct a design review of the week-4 lifecycle against governance requirements and close identified gaps

Define a human-in-the-loop and override design for the highest-risk decision point

Document model documentation and audit-trail requirements that satisfy both engineering and risk stakeholders

Produce a rollback and incident-response plan for a failing or misbehaving model

Map stakeholder groups and their change readiness, resistance sources, and influence

Design a role-based AI literacy and upskilling plan tied to the week-1 use cases

Draft a communication and adoption plan with concrete behaviors to reinforce and incentives to adjust

Identify the operating-model changes (roles, decision rights, team topology) required to sustain AI delivery

Define a value-realization framework linking each use case to baseline, target, and measurement cadence

Separate leading from lagging indicators and specify data sources for each metric

Map applicable regulatory and compliance obligations to specific governance controls already designed

Identify documentation and reporting artifacts needed to evidence compliance to auditors or regulators

Synthesize prior deliverables into a single end-to-end AI transformation roadmap with sequenced phases

Stress-test the roadmap against the top risks and value assumptions, and document contingency actions

Prepare an executive-ready briefing that defends the roadmap's tradeoffs in business and risk terms

Complete a timed practice assessment across all domains and review every missed item to close knowledge gaps