AI Strategy, Value, and Business Transformation
What to know
AI strategy connects a business problem to measurable value. Know how to prioritise use cases by value, feasibility, data readiness, and risk - often plotted on an impact/effort matrix. Understand build vs buy vs partner, total cost of ownership (including data preparation, integration, monitoring, and change management, which usually dwarf model cost), and ROI framing with baseline and benefit hypotheses.
Transformation concepts: operating model change, process redesign, workforce reskilling, AI governance, and scaling from pilot to production (the "pilot purgatory" failure). Know the components of an AI governance model: ownership, policy, risk tiering, and monitoring. Understand competitive dynamics - advantage comes from proprietary data, workflow integration, and execution, not from access to a generic model that competitors also have.
Traps
Questions reward starting from a business problem, not from technology. Beware business cases that omit integration, change management, or ongoing monitoring costs, or that claim benefits without a baseline. Another trap: assuming a successful pilot proves enterprise value - scaling requires data pipelines, governance, and adoption.
Worked example
A retailer considers an AI demand-forecasting tool. The business case should state the baseline forecast error, the expected reduction, the inventory savings, and the full cost: data integration, licence, monitoring, and planner retraining. If payback exceeds the planning horizon, deprioritise. Rule of thumb: value = benefit × adoption − total cost of ownership.