Regulatory Concerns Temper Agentic AI Adoption Across the Tech Sector, PYMNTS Finds

The fastest way to spot where agentic artificial intelligence will land next is to watch the technology sector, where it is already being built and tested.

Inside tech, familiarity is translating into early, careful moves along a classic S-shaped adoption curve.

The PYMNTS Intelligence report “ Tech on Tech: How the Technology Sector Is Powering Agentic AI Adoption ” found a widening readiness gap between tech companies and firms in goods and services. The report was based on a June phone survey of 60 verified chief product officers and heads of product at companies in the United States with at least $1 billion in annual revenue.

What stood out was less the hype around autonomous systems and more the pattern of how businesses learn.

Tech firms are closer to the source of new tools, so they absorb the capabilities earlier, develop internal talent sooner and build the first commercial uses. Other sectors tend to wait for proven applications, clearer payoffs and fewer unknowns.

That delay is not a sign of indifference. It is how adoption usually works when a technology moves from invention to everyday operations. It takes time. It takes repetition. It takes evidence. Full stop.

Familiarity led the curve, as 75% of tech firms said they were extremely familiar with agentic AI, versus 33% of goods firms and 38% of services firms.

Exploration was narrow, as 42% of tech firms were exploring how to bring agentic AI into operations, compared with fewer than 4% of goods firms and 0% of services firms.

Generative AI ROI is cooling, but not collapsing. In March 2024, just over half of tech firms reported “very positive” ROI from generative AI over the prior year. By May 2025, that share fell to 17%, while “somewhat positive” ROI rose to 50% and “negligible” ROI increased to about 25%.

The ROI shift matters because agentic AI is not arriving in a vacuum. Companies are treating it as the next step after generative AI, and they are using prior results as a guide for how much autonomy they are willing to allow.

The report suggested that early generative AI deployments may have produced outsized gains on earlier model iterations, followed by a more measured phase as tools evolved and organizations recalibrated expectations.

In that sense, returns may also follow an adoption curve, with benefits that can take longer to show up as the work moves from pilots to scaled change.

The report also found that trust in agentic AI was shaped by what a company did day to day. Goods-producing firms, managing physical supply chains, put emphasis on integration efficiency and sustainability. Services firms, often closer to customers and regulated workflows, stressed cross-platform compatibility and the need for human oversight.

Tech firms, more advanced in their AI literacy, focused on bias monitoring, reflecting reputational risk and a deeper awareness of how automated decisions can go wrong.

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