From Investment to Impact: Measuring the ROI of AI

As organizations increase their investment in AI, how can they determine whether those investments are delivering meaningful business value?

In this webinar, Mike Haven (Meta), Kimberly Bell (formerly American Express) and Zena Applebaum (Harbor) join Apertera CEO Gary Kalaci to explore practical approaches to measuring AI ROI.

Drawing on their experience in legal operations, technology and business transformation, the panel discusses how to identify meaningful KPIs, measure productivity gains and demonstrate the value of AI investments to leadership.

Key Takeaways

  • Define AI ROI based on business outcomes. There is no single metric for measuring AI ROI. Mike Haven outlines four categories for measuring AI ROI in in-house legal departments: deflecting work back into the business, keeping work in-house, absorbing additional demand and creating new capabilities. Each requires different measures of success.
  • Measure what happens with the time AI saves. Productivity gains don’t automatically translate into financial savings. Organizations need to consider how they’re using the capacity AI creates, whether that’s handling more work, accelerating workflows or allowing professionals to focus on higher-value activities.
  • Account for the full investment when measuring AI ROI. Licensing fees are only part of the equation. Implementation, training, governance, output verification and ongoing maintenance all affect the true return. Establishing reliable baselines and aligning KPIs with business objectives are essential to demonstrating value.

How should organizations measure AI ROI?
Start by defining what the AI investment is intended to accomplish and identifying KPIs that reflect those objectives. Depending on the application, relevant measures might include reduced external spending, faster turnaround times, increased capacity or new capabilities. Results should be measured against a documented baseline.
Does saving time with AI automatically translate into cost savings?
No. Time saved creates capacity, but its value depends on how that capacity is used. Organizations should evaluate whether AI enables teams to absorb more work, improve service or redirect resources toward higher-value activities rather than treating every hour saved as a direct financial saving.
What are the biggest mistakes organizations make when measuring AI ROI?
Common mistakes include focusing on adoption rather than business outcomes, equating hours saved with money saved, overlooking verification and implementation costs, and failing to establish reliable baselines. The panel also emphasizes avoiding double-counting benefits and distinguishing actual financial savings from estimated value.

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