Transformation Stories
Four Problems. Four Outcomes.
Read these as consulting case studies. Problem. Decision. Outcome. The pattern that explains the career.
The Four Intern Experiment
From Four Interns to a $15M Revenue Engine. Leadership said no. He built it anyway — with four interns and his own operations budget.
Problem
In 2016, Vicky identified a significant commercial opportunity in prospecting and catalogue management. He took the business case to leadership. The response: interesting, but not a priority. No budget. No team. No timeline. Most leaders would have moved on. He did not.
Decision
Using discretionary budget within his own operations function, Vicky hired four interns. Their mandate: build a proof of concept for the prospecting and catalogue capability. The pilot ran for six months. It worked. The data was undeniable. He went back to leadership — this time with results, not projections.
Outcome
The pilot was approved for full investment. Four interns became 10 FTEs and one manager — a dedicated capability with its own mandate, processes and metrics. That team grew and scaled to generate $15M+ in annual revenue. A capability that did not exist before Vicky decided to bet four interns on an idea leadership had declined.
Ownership means building the proof when nobody will fund the proposal. Business judgement means knowing which risks are worth taking with your own resources. Entrepreneurship does not require a startup.
Trust & Quality · 2016–2025
Building Marketplace Trust at Scale
At 2,000 reviews a month, trust is a policy. At 100,000 reviews a month, trust is an operating system.
Challenge
As Capterra, Software Advice and GetApp grew, so did the incentive to game the review system. Spam and fraudulent listings were increasing at a rate that threatened the platform's core value proposition: trustworthy, authentic software reviews. Quality had to scale with volume — without slowing the platform or creating friction for legitimate vendors.
Approach
Built a moderation operating system designed to scale horizontally — not just a set of rules, but a living capability that adapts as fraud patterns evolve. Process design, quality frameworks, governance structures, and the early deployment of GenAI-assisted review workflows — deployed in production before AI was mainstream strategy.
Outcome
Review volume grew from 2,000 to 100,000+ per month. Spam reduced by 90%. $3M+ in annual fraudulent payments eliminated. Cost per review cut by 50%. The moderation capability became a competitive moat — something competitors could not easily replicate because it was built on operational discipline, not just technology.
Trust is an operational capability, not a policy. When quality and scale appear to be in conflict, the answer is almost always better design, not a trade-off.
GCC Leadership · 2016–2026
From Support Team to Strategic Asset
Most GCCs are built to reduce cost. This one was built to create capability — and it became one of the most strategically significant India centres in Gartner's global footprint.
Challenge
When Vicky joined the GDM India founding team in 2016, the implicit assumption was that India would provide operational support for processes designed and owned elsewhere. This is the default trajectory for most GCCs: cost arbitrage, support functions, limited strategic ownership. He had a different view of what this could become.
The Build
Rather than accepting a support mandate, Vicky consistently expanded scope through demonstrated capability. Each time a function was built and delivered results, the next conversation was about what else India could own. The GCC earned scope through outcomes — not through politics, lobbying or corporate restructuring.
Result
The GCC survived a corporate acquisition. It outlasted its original mandate by a decade. A support team became a strategic asset. The difference between a cost centre and a strategic asset is a decade of building things that matter and proving they work.
AI Transformation · 2023–2026
AI In Production — Not In A Deck
While most teams were still running pilots, this operation was measuring AI outcomes in dollars. $1M+ annually. Before it was mainstream.
Problem
By 2023, AI was a strategic priority — but the gap between ambition and execution was significant. Pilots were running. Demos were being shown. Business outcomes were not being delivered. Operational functions — moderation, prospecting, sales support — were still largely manual despite clear AI potential.
Approach
Outcome-first. Not "how do we use AI?" but "which problems cost us the most — and can AI solve them?" GenAI deployed across moderation first — highest volume, clearest quality impact. Then prospecting pipelines and sales support. Each deployment measured against a baseline and evaluated on business outcomes, not technology metrics.
Outcome
$1M+ annual productivity impact. Five functions AI-enabled in production. Processing speed improved. Quality maintained. Costs reduced. More importantly: a repeatable model for moving from pilot to production — an AI operating capability that could be applied to any new function.
AI transformation is not about technology. It is about the discipline to deploy it against real problems, measure it against real baselines, and scale what works.