Why AI ROI is Lagging Expectations
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Key Takeaway
Companies escaping the AI ROI trap do so by treating AI as a portfolio of process investments – not a collection of tools – and by tying every initiative to measurable business outcomes before scaling. The results hinge on three disciplines working together: workflow-level redesign that captures value across the full process, fit-for-purpose KPIs that replace vague narratives, and enterprise-wide governance that enforces consistent decision rights rather than leaving each function to improvise. If your AI spending still looks like a scattered set of pilots with no clear exit criteria for underperformers, the evidence here suggests the problem is structural – and the fix starts with how you define, measure, and govern the portfolio before you commit more capital.
Companies escaping the AI ROI trap do so by treating AI as a portfolio of process investments – not a collection of tools – and by tying every initiative to measurable business outcomes before scaling. The results hinge on three disciplines working together: workflow-level redesign that captures value across the full process, fit-for-purpose KPIs that replace vague narratives, and enterprise-wide governance that enforces consistent decision rights rather than leaving each function to improvise. If your AI spending still looks like a scattered set of pilots with no clear exit criteria for underperformers, the evidence here suggests the problem is structural – and the fix starts with how you define, measure, and govern the portfolio before you commit more capital.
Originally reported by EY. Read the full story here.
