What happens when the very pricing model meant to speed up AI adoption ends up slowing it down?

In this episode of Tech Talks Daily, I sit down with Sameet Gupte, CEO and co-founder of EvoluteIQ, to discuss a part of the enterprise AI story that still doesn't get enough attention.

While so much of the conversation around AI focuses on models, copilots, and the latest agentic promises, Sameet brings the discussion back to a business reality that every enterprise leader understands. If the economics do not work, adoption stalls. And if success in a pilot makes the final rollout even more expensive, something has gone wrong long before the board signs off on scale.

Sameet argues that many organizations are still trapped by legacy pricing structures built for an earlier generation of automation. Per-user and per-bot pricing may look manageable at the pilot stage.

Once a company tries to expand automation across departments, processes, and geographies, the numbers can quickly stop making sense. That creates what many now call pilot purgatory, where a company proves something can work, but cannot justify taking it any further. It is a problem rooted in incentives, procurement, and fragmented technology stacks, and it is one that CFOs are watching very closely.

What I found especially interesting in this conversation is how Sameet frames the issue. He believes most enterprises do not actually have an automation problem. They have an orchestration problem.

In other words, the challenge is rarely a lack of tools. It is getting all the systems, workflows, approvals, data flows, and legacy infrastructure to work together to produce a clean business outcome. That idea changes the conversation from buying isolated features to rethinking the process as a whole.

We also discuss why outcomes-based pricing is increasingly resonating with enterprise buyers. Sameet explains why predictable costs, transparent commercial models, and shared accountability are helping move automation conversations out of innovation teams and into the CFO's office.

For public companies and large global enterprises, that matters. Leaders want fewer surprises, fewer overlapping vendors, and a much clearer line between spend and return.

There is also a broader theme running through this episode about where the market is heading next. Sameet sees real urgency around vendor consolidation, enterprise simplification, and the need to rethink how AI is introduced into the business. His view is that companies need to pause, define what they actually want AI to do, and then choose tools that fit the business, rather than reshaping the business around the latest platform pitch.

If you are trying to make sense of AI adoption beyond the hype, this conversation offers a practical and timely perspective on pricing, scale, and what real transformation could look like inside the enterprise.

After listening, do you think the future of enterprise AI will be shaped as much by commercial models as by the technology itself, and what are you seeing in your own organization?

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