The Executive's Guide to AI ROI

I see AI budgets get approved on a slide and vanish in one quarter. The technology works. The ROI does not.

The gap is not the model. The gap is how leaders define value and drive adoption. ROI from AI is not a technology problem. It is a leadership problem.

A use case says: "We use AI to summarize contracts." A value case says: "We spend 4,200 legal hours a year on triage. AI removes that triage for 90k. This frees 380k in capacity."

One is a demo. The other survives a CFO.

Before I fund anything, I require three numbers:

  • The baseline. What does this cost today in hours or errors? You cannot prove the "after" without the "before."
  • The addressable slice. AI rarely handles 100% of a task. Is it 30% or 70%? Overstating this ruins your ROI.
  • The realization path. Freed hours are not savings until you redeploy people. A 20% productivity gain with no reallocation is a 0% financial gain.

Model quality gets headlines. Adoption gets returns.

A tool with 90% accuracy used by 20% of the team returns less than a 70% accurate tool used by 90% of the team. The difference is trust and workflow fit.

ROI equals value per use times frequency of use times share of users. Two of those variables are human.

I budget for adoption like infrastructure. I look for:

  • Champions in every team.
  • Workflows designed around the tool.
  • Feedback loops from actual users.

Do not rely on vanity metrics like "AI drafts 5x faster." Faster drafting can lead to slower reviewing and more errors.

I track three layers:

  • Activity. Is the team using it?
  • Outcome. Did the target metric move?
  • System. Did something else break downstream?

Most failed AI projects make these mistakes:

  • Pilots that cannot scale. They test clean data instead of messy real-world data.
  • Buying capability instead of solving problems. Pick three deep value cases instead of broad, shallow ones.
  • Ignoring change costs. Licenses are 10% of the bill. Integration and training are the other 90%.
  • No owner. If the ROI is not on an executive scorecard, no one defends it.

Stop asking "what can AI do?" Ask "where do we lose money today, and can AI stop that loss faster than anything else?"

Fund fewer things, but go deeper. Measure the baseline first. Spend as much on adoption as on technology. Put an accountable name next to every dollar.

Source: https://dev.to/cedricbignet/the-executives-guide-to-ai-roi-52ah

Optional learning community: https://t.me/GyaanSetuAi