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Only 12% of Companies Generate Value From AI. Here's What They're Doing | Sanjeev Vohra, Genpact

59 min episode · 2 min read
·
Sanjeev Vohra

Episode

59 min

Read time

2 min

Topics

Productivity, Startups, Leadership

AI-Generated Summary

Key Takeaways

  • AI Maturity Distribution: Only 12% of companies qualify as AI leaders — generating tangible business value from AI in production workflows at scale. Another 15% are classified as advanced, 47% as assisted (using copilots but not embedding AI into operations), and 26% as emerging, meaning they have yet to establish a clear adoption path.
  • The Frozen Middle Problem: Senior leadership and junior employees are engaging with AI, but middle management — operationally overloaded and managing direct reports — represents the critical bottleneck. Companies must design specific rotation and training mechanisms targeting this layer, as ideas and execution both flow through mid-level managers who control day-to-day process workflows.
  • Governance Before Scale: Enterprises deploying multiple agents need a centralized platform that inventories all agents, enforces responsible AI and data privacy policies, monitors compute costs, and flags rogue behavior. Without this infrastructure, companies risk uncontrolled agent sprawl — analogous to past shadow IT problems — with no visibility into which agents are underperforming or operating outside policy boundaries.
  • Progress Over Perfection Framework: AI adoption cannot follow a fixed implementation playbook like an ERP rollout. Because the technology evolves faster than any organization can fully map it, waiting for a complete roadmap guarantees falling behind. Companies should set outcome-driven targets — Genpact's own goal is reducing G&A costs by 50% — then build programs downward from that specific, measurable commitment.
  • 10x Engineers, 3x Practitioners: AI tools are enabling engineers to become roughly 10 times more productive by coding in natural language rather than traditional syntax. Business-side professionals — accountants, lawyers, procurement staff — can achieve approximately three times their current output. Scaling this requires systematic company-wide enablement programs, not isolated pockets of high performers, to bring entire workforces to a consistent capability level.

What It Covers

Sanjeev Vohra, Chief Digital Officer at Genpact, shares findings from a 500-executive study revealing only 12% of companies generate measurable AI value. He outlines the three core barriers blocking enterprise AI adoption and explains what separates leaders from the 47% stuck in assisted, non-production AI use.

Key Questions Answered

  • AI Maturity Distribution: Only 12% of companies qualify as AI leaders — generating tangible business value from AI in production workflows at scale. Another 15% are classified as advanced, 47% as assisted (using copilots but not embedding AI into operations), and 26% as emerging, meaning they have yet to establish a clear adoption path.
  • The Frozen Middle Problem: Senior leadership and junior employees are engaging with AI, but middle management — operationally overloaded and managing direct reports — represents the critical bottleneck. Companies must design specific rotation and training mechanisms targeting this layer, as ideas and execution both flow through mid-level managers who control day-to-day process workflows.
  • Governance Before Scale: Enterprises deploying multiple agents need a centralized platform that inventories all agents, enforces responsible AI and data privacy policies, monitors compute costs, and flags rogue behavior. Without this infrastructure, companies risk uncontrolled agent sprawl — analogous to past shadow IT problems — with no visibility into which agents are underperforming or operating outside policy boundaries.
  • Progress Over Perfection Framework: AI adoption cannot follow a fixed implementation playbook like an ERP rollout. Because the technology evolves faster than any organization can fully map it, waiting for a complete roadmap guarantees falling behind. Companies should set outcome-driven targets — Genpact's own goal is reducing G&A costs by 50% — then build programs downward from that specific, measurable commitment.
  • 10x Engineers, 3x Practitioners: AI tools are enabling engineers to become roughly 10 times more productive by coding in natural language rather than traditional syntax. Business-side professionals — accountants, lawyers, procurement staff — can achieve approximately three times their current output. Scaling this requires systematic company-wide enablement programs, not isolated pockets of high performers, to bring entire workforces to a consistent capability level.

Notable Moment

Genpact deployed an AI agent named Amber as its Chief Listening Officer, conducting over 500,000 employee interactions in one year. Unlike annual surveys, Amber operates continuously, personalizes each conversation, and generates actionable retention recommendations — replacing a costly, infrequent process with real-time organizational intelligence.

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Episode Transcript

Do legacy companies have to worry about being overtaken by agentic native startups in their industries? It used to be that the IT department owned all this stuff, and now that's kind of broken that silo. And if you have 100 agents, one of the agents go rogue. Do you have a process to find out which agent has gone rogue, which is not behaving properly? The companies who will lean faster are not looking for the perfect answer would have more chance towards success. Don't wait until you understand everything because by the time you understand everything today, the technology would have moved on to tomorrow. You just have to jump in and swim. You know, I joined Genpact around, eighteen months back, and I am I'm truly enjoying, what's going on, in the industry and what about I'm doing in Genpact. It's, it's quite it's quite, being in the engine room, if you may. And, and let me explain. I mean, I I have always, been at the intersection of business and technology for last many, many years at more than thirty five years, and and, did a lot of, work in the business side in the first half of my career, and the second half was mostly consulting and strategy and technology. And, with only one intent of, leveraging technology to the best business outcomes and people, involved in enterprise and how we can make our lives easier and, more productive and efficient, by using technology. Now various ways of technology have gone from the, you know, from the main main frames to client server technologies like an ERPs like SAP and Oracles to, you know, to, to Internet technologies, that started in late nineties to, to digital technologies later on a decade back plus. Right? And then, and then between that was a cloud, the whole wave of cloud and data analytics and AI finally. Right? So what really attracted me in Genpact was the DNA of the company, and that's process intelligence. And, the way the company has that process intelligence, we call it the last mile, which means, understanding how the process actually operates. So it's not about advising the client, but it actually operating the process itself. And, that's why I said being in the engine room itself, and that gave me a lot of inspiration and idea of how to actually deploy artificial intelligence to make, to make sense of the business outcomes, if you need. Right? That's that's that's how I landed here. Yeah. And and I remember, when we spoke before, just for people who may not know, Genpact is short for generating impact, and it was spun out of, GE Capital as I recall. Is that right? Yeah. Was yeah. I was born in 1997. So it was inside, inside General Electric, started in 1997 as General Electric, Capital International Services. That's how the name was. And then, later on into 2007, 2005, it became independent company. And, 2007, …

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