AI opportunity prioritisation
Organisations run too many AI pilots in parallel. Few reach production. There is no shared logic for deciding which bets to fund, which to park, and which to kill.
A structured two-week engagement that scores candidate bets on payback, feasibility and risk, and produces a ranked roadmap leadership can actually fund.
- 01
We interview five to ten leaders across the business to surface the real pain.
- 02
We translate each candidate bet into a scored opportunity (payback, feasibility, risk, strategic fit).
- 03
The ranked shortlist is paired with a sequenced roadmap and named owners.
- 04
Each priority bet has a written scorecard: baseline, target, stop condition.
- 05
Leadership gets one document to make the fund decision, not a slide deck to argue about.
with 2x to 3x faster time from idea to production system.
Ranges drawn from production deployments and public enterprise benchmarks. For a specific rupee or dollar figure tailored to your volume, use the calculator below.
Prerequisites for a clean deployment.
- Leadership time (five to ten interviews across the business)
- An honest view of current AI efforts, including the ones that are not working
- Willingness to deprioritise bets that fail the scorecard
- A named exec sponsor for the roadmap
Put your own numbers on it.
“At 200 briefs a month and a loaded monthly cost of ₹1,50,000 per person, ai opportunity prioritisation would typically save ₹3.8 L to ₹5.6 L a year.”
Range uses this use case’s typical automation rate (40 to 60 percent) against the baseline time per task for research work, with your cost per person converted at 160 working hours a month.
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