An October PYMNTS Intelligence report, “From Zero to Beta: How Agentic AI Just Entered the Enterprise Fast Lane,” which surveyed 60 chief product officers at billion-dollar companies, found that product chiefs were warming to autonomous AI agents. However, their willingness to hand over the steering wheel depended on how comfortable they already were with letting machines make decisions.
Enthusiasm for agentic AI surged between June and August, the report revealed. Yet growth wasn’t uniform. Enterprises that had already automated much of their internal plumbing were accelerating. Those that hadn’t were standing still.
In short, enterprise AI adoption is splitting into two speeds, and the gap has little to do with compute power or budgets.
Key findings include:
- Automation predicts adoption, as 25% of enterprise product departments with the highest levels of automation were using agentic AI by August. Another 25% planned to do so within a year. None of the firms with low automation had made that leap.
- Sector divides run deep, as 20% of tech companies reported active or planned agentic AI use, compared with just 7% in the goods sector and 4% in services. Even so, the share of service companies with no plans to adopt agentic AI fell from 100% in June to 30% in August.
- Trust remains the bottleneck, as 98% of product leaders said they are not ready to give AI agents access to core systems. Even among highly automated enterprises, 75% cited governance and security as major concerns.
Those numbers point to a more nuanced story than a simple surge in adoption. Enterprises that already rely on automation, from ERP-embedded workflows to predictive analytics, see agentic AI as the next logical extension. For them, delegating tasks to self-directed systems feels like moving from cruise control to autopilot. For others, it feels like taking their hands off the wheel entirely.
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The report also underscored the practical limits of the build-it-yourself narrative. More than 90% of product leaders said they depend on third-party vendors or consultants to explore agentic AI, a sign that specialized expertise and risk management are now as important as technical capability.
Service sector firms were the most reliant on outside help, while a quarter of tech sector companies were training staff to bring agentic AI skills in-house, an investment that could yield control but also raises cost and compliance stakes.
Adoption patterns mirrored the differing ambitions of each industry. Tech firms favored agentic AI for user testing and product lifecycle management. Goods manufacturers wanted it for competitive analysis, scanning rivals’ pricing and product launches.
Sectors were feeling their way toward autonomy at a different pace, reflecting not just their data maturity but their institutional tolerance for machine decision-making.
The report found that progress is no longer limited by innovation but by conviction. The technology is ready; the humans are not. As agentic AI moves from prototype to production, the enterprises leading the charge will be the ones that trust their systems enough to let go.
