How to Build a Business Case for AI Investment
aiinsider ·
The article explains how to create a strong business case for AI investment, emphasizing that proposals often fail due to poor framing rather than weak technology. It advises starting with a specific problem and quantified current-state costs, not with the technology itself. Benefits should be separated into categories like direct cost reduction, revenue enablement, risk reduction, and strategic optionality, with conservative assumptions stated explicitly. The full cost lifecycle must include implementation, internal resources, maintenance, infrastructure, and compliance, not just licensing. AI projects carry unique risks, such as model underperformance and divergence in production, so the case should build in safety margins and name risks openly. Successful strategies often involve piloting simple, narrow use cases for early wins, augmenting staff rather than replacing them, and sequencing rollouts around measurable results. Clear KPIs tied to benefit categories help build evidence for ongoing investment. The underlying discipline is speaking to finance, operations, and leadership in their own terms with quantified returns and managed risk.
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