AI Transformation
AI in Production — Not in a Deck
While most organisations were still debating AI strategy, Gartner Digital Markets India was measuring AI outcomes in dollars. $1M+ annually. Five functions deployed. Before AI was mainstream. The approach: outcome-first, not technology-first.
The Approach
Outcome-first. Not technology-first.
Most AI transformation programmes fail not because the technology does not work — but because the operations it is being applied to were never designed to be AI-enabled. The question Vicky asked was not "how do we use AI?" but "which problems cost us the most, and can AI solve them?"
Each deployment began with a documented baseline — volume, cost, quality metrics measured before AI, and then again after. Deployments were evaluated on business outcomes, not technology metrics. If it did not move revenue or cut cost, it was not deployed.
"If it does not move revenue or cut cost, it is not AI transformation — it is a slide."
Five Functions — All in Production
Review Moderation
100K+ reviews/month. Quality maintained. Cost halved.
Prospecting Pipelines
AI-assisted vendor qualification at scale.
Sales Support
Automated query handling and response drafting.
Revenue Operations
AI-assisted pipeline analysis and prioritisation.
Sales Enablement
Content generation and battlecard automation.
The Deployment Model
How to move from pilot to production.
01
Select the right problem
Highest volume. Clearest quality impact. Measurable baseline already in place.
02
Measure the baseline
Volume, cost per unit, quality score — all documented before AI is introduced.
03
Deploy in production
Not a sandbox. Not a demo. Real volume, real output, real accountability.
04
Report in business terms
Cost saved, throughput gained, quality maintained — not model accuracy.