Every AI business case I see is built on a vendor's ROI calculator.
Here's what the numbers look like when they come from deployments instead.
Three patterns that hold up
Decision latency collapses before headcount changes. At Cipla, decision latency fell over 90% and data adoption rose 8×. The win was speed, not savings — and it arrived first.
The IT burden drops as a second-order effect. An 80% fall in report requests wasn't the goal. It happened because people stopped needing a human to interpret data for them.
Diagnosis time is where the outsized numbers live. Campaign diagnosis went from days to effectively instant — roughly a 1000× improvement — because the causal path was already modelled rather than reconstructed by an analyst each time.
The pattern across deployments
| Organisation | Result |
|---|---|
| Cipla (pharma) | 8× data adoption · >90% lower decision latency · 80% fewer IT report requests |
| SSP Group (travel retail) | 40% less data-management overhead · 3× faster issue resolution |
| Confidential ARC (BFSI) | >95% lower evaluation cycle time · 100% regulatory coverage |
What this says about how to budget
ROI doesn't come from replacing analysts. It comes from removing the wait between a question and a trustworthy answer.
That ordering matters for the business case. Fund the layer that makes answers trustworthy and the productivity numbers follow. Fund the productivity tool first and you get a pilot, because nobody acts on numbers they can't defend.
The number to put in the model
Not headcount saved. Decision latency — how long between a question being asked and a defensible answer existing. It's measurable today, it's usually embarrassing, and it's the variable that actually moves.
The full evidence review — the deployment data, the methodology behind each figure, and how to build the business case — is here:
👉 The ROI of a Company Brain: What the Evidence Actually Shows Executives
Originally published at colrows.com/blogs/company-brain-roi-evidence-for-executives
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