From pilot to production The untold truth of enterprise GenAI

AI pilots impress on slides but stumble in systems. From token blowouts to trust issues, GenAI in the enterprise is more trial than triumph. Here's what recent field experience reveals about what works, what breaks, and what's coming next

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Generative AI may be redefining what's possible in code, content, and customer interactions, but within enterprise tech, the story is more complicated.

The hype is everywhere. So are the prototypes. Yet, the vast majority of enterprise AI pilots don't survive the leap into production.

In a wide-ranging conversation with Vikrant Karnik, Executive Vice President and Head of Technology, Coforge who has overseen hundreds of generative AI deployments across industries like banking, travel, and insurance, the complexity of “AI in real life” comes into focus.

This isn't about tools or trends. It's about outcomes, and how smart teams are evolving their engineering, governance, and developer workflows to make AI actually work at scale.

The pilot graveyard: Where most GenAI projects go to die

Enterprise AI adoption often starts the same way: a successful pilot solving a niche use case. Common examples include document digitization, video transcription, or processing insurance claims. The results usually look great in isolation. But the jump from lab to production is where most of them fall apart.

Over a span of 300-plus AI pilots across multiple verticals, only 5 to 10 percent made it to full production. The bottlenecks weren't purely technical. They were rooted in business misalignment, lack of measurable outcomes, and an inability to scale responsibly.

One key insight stood out: when pilots were outcome-driven with clear business impact, they progressed. When they were built simply to showcase AI's capability, they didn't.

AI agents, cost spirals, and the rise of governance engineering

Another major friction point emerged with the growing use of autonomous agents, which are self-executing code blocks that tap into language models for reasoning and action.