Adopt new AI ROI tools or refine existing methods?
With 95% of AI pilots failing to show ROI under legacy metrics, traditional automation business cases and "hours saved" metrics fail to capture AI's strategic value, leaving leaders blind to true impact.
The question
New tools like Microsoft Foundry's 'ROI for Agents' and the Inference Efficiency Ratio (IER) are emerging to specifically measure AI value. Do we invest in adopting these specialized AI ROI measurement tools now, or do we refine our existing, broader financial ROI frameworks to better capture AI's impact?
Counsel's position
Refine existing ROI frameworks to integrate AI-specific value drivers, leveraging established processes while specialized AI measurement tools mature.
Verdict
The verdict: Refine existing ROI frameworks to integrate AI-specific value drivers, leveraging established processes while specialized AI measurement tools mature.
How the criteria decide
1 of 3 criteria resolved on cited evidence. 2 had none either way.
| Criterion | Favours | Evidence |
|---|---|---|
| measurement accuracy | Adopt specialized AI ROI tools | Foundry's ROI for Agents calculates value minus total operating cost ROI = (business value gained - total cost) / total cost Microsoft Foundry Blog articles A 10:1 Inference Efficiency Ratio yields $10 per inference dollar The Inference Efficiency Ratio measures how much AI product revenue is generated per dollar spent on production inference. |
| tool integration cost | No evidence either way | |
| organizational adoption | No evidence either way |
Foundry's ROI for Agents calculates value minus total operating cost
Given your decision on specialized tools, Foundry connects agent traces and token costs directly to custom business-value evaluators.
Traditional automation business cases fail to capture AI's strategic value
Given your debate over refining existing frameworks, traditional financial metrics often misalign with AI's varied value delivery timelines.
A 10:1 Inference Efficiency Ratio yields $10 per inference dollar
Given your evaluation of specialized metrics, IER connects model consumption directly to monetization to expose margin compression.
95% of AI pilots fail to show ROI under legacy metrics
Given your choice between specialized and broad frameworks, traditional metrics fail to capture AI's value when organizational bottlenecks negate AI's speed.
Hours saved metrics fail to capture AI's true enterprise value
Given your evaluation of ROI frameworks, traditional productivity metrics measure local efficiency rather than actual organizational performance.
Read another verdict
- Which process should we point AI at first?
- Put one person in charge of AI — or is a Head of AI premature for us?
- Buy a tool for this process, or build around our own knowledge?
- Centralize AI strategy under CEO or distribute ownership?
- Invest in pre-build costing or post-deployment ROI tracking?
- Our documents are a mess. Clean them up before AI, or after?
- How do we measure the return on an AI workflow — and what baseline is honest?
- Our best people's know-how isn't written down — can AI even use it?