AI Strategy for Executives
Building a Business Case for AI Initiatives
Boards and executive committees increasingly require business cases for AI investment. Cases that describe technology capability without P&L connection fail review—or worse, pass review without accountability for outcomes.
Define the problem in business terms
Begin with the business problem: manual processing hours, conversion rate gaps, decision delays, error rates, or customer churn drivers. Quantify the current state with baseline metrics leadership already trusts.
The AI solution description follows the problem—it does not replace it. Reviewers should understand what changes in business performance, not what technology deploys.
Model costs beyond licensing
Total cost includes implementation, integration, training, ongoing inference or platform fees, governance overhead, and change management. Business cases that model only vendor subscription costs consistently underestimate investment and overstate returns.
Establish accountability and review milestones
Define success metrics, measurement methodology, review dates, and decision rights for scale, modify, or retire. Business cases without accountability structures produce funded pilots that never face outcome scrutiny.
Key Takeaways
- Lead with quantified business problems and baseline metrics
- Include full cost of ownership—not subscription fees alone
- Define success metrics and executive review milestones upfront
- Assign clear ownership for outcome accountability
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