TechEx: Enterprise AI roadblocks and security fixes

At TechEx North America on Tuesday in San Jose, presenters across the AI and Big Data, Enterprise AI Implementation, ROI and Adoption, and Cyber Security tracks examined why many enterprise AI pilots stall and highlighted security risks tied to rapid adoption of agentic AI by business units.

Presenters traced stalled projects to one-off pilots that did not scale. Companies commonly fund single-user experiments-often described as a “personal copilot”—that produce measurable gains for individuals but fail to deliver consistent improvements across teams or departments. Executive use can increase interest, but organizations reported difficulty moving from isolated wins to coordinated, organization-wide rollouts.

Speakers recommended limiting agentic AI at first to clearly defined business areas and investing in data foundations so models work on reliable, governed inputs. Finance-focused sessions advised planning for token-based charging models as model usage grows. Infrastructure panels covered whether to buy or build on-premises compute and how to match capital and operating costs with expected returns.

Cybersecurity sessions described a gap between how quickly business units adopt AI and the capacity of security and governance teams. Panelists noted that generative AI and autonomous agents expand both defensive options and attackers’ capabilities. Delegates raised concerns about shadow AI when employees use unsanctioned tools or when approved systems are not properly bounded, increasing the enterprise attack surface.

Zero trust was presented as a control to limit unbounded access: speakers urged a default-deny posture for humans and machine agents and recommended applying identity and privilege checks to services and automated workflows so agents operate only within authorized scopes.

The Physical AI track attracted large audiences and humanoid robots on the show floor drew attention. Delegates reported that early commercial gains from large language models have appeared in software coding tasks. Presenters outlined expectations that specialized models and integration techniques will be used for automated physical systems, with LLMs supporting human-facing interfaces.

Hands-on sessions in the TechEx Learning Hub included workshops on spinning up agentic models in Google Colab and exercises on how agents can iteratively improve. Labs ranged from IDE introductions for newcomers to production-focused techniques for experienced developers.

Organizers framed day two as a practical assessment of scaling, governance and cost management for enterprise AI deployments. The next TechEx event is scheduled for Amsterdam in September.