A telecom consultant I worked with long ago used to tell the story of a company whose phone service he was hired to audit. The company wanted to find potential savings where services were being paid for but not used, that kind of thing. He reported back to management that after reviewing phone bills, he thought usage in the Sacramento office seemed high. Leadership replied that the company didn’t have a Sacramento office - only to find after checking it out that there was, in fact, such a location. The kicker? The folks in Sacramento, who’d been happily going about their business without interference from Corporate, were furious with him for (unknowingly) blowing their (unintended) cover.
A generation later, after innumerable rounds of cost-cutting and “right-sizing,” that degree of corporate obliviousness seems highly unlikely. Still, any IT leader at a large enterprise knows the nagging feeling that their end users are almost certainly using unsanctioned technology, and that even approved applications, especially those like AI that employ emerging technologies, could be creating unforeseen problems.
So AI governance is a hot topic and one that every enterprise IT leader must keep near the top of their list of challenges. Sumit Agarwal, VP analyst at Gartner, has a useful piece at CIO Dive laying out not just the key governance issues, but the technology underpinnings enterprises must put in place to actually embed governance in the technology. Crucially, he points out that today’s AI, by its nature, is much more complex and difficult to lock into a set of governance-focused functionality.
“Traditional AI governance models built on periodic process audits and static policies can’t keep up with nondeterministic, modern AI architectures, such as retrieval-augmented generation and autonomous agent-based systems,” Agarwal writes. This means governance mechanisms must be “embedded directly into the AI architecture.”
Agarwal argues that enterprises “must shift to an architecture-first approach. Governance must be established as a foundational technical requirement instead of a compliance afterthought.” He advocates for a toolset that includes:
- Guardrails
- Observability
- Traceability
- Centralized AI gateways
- AI catalogs
- AI wrappers
As luck would have it, No Jitter has a current article offering a graphic demonstration of the magnitude of the risk if enterprises can’t exercise effective AI governance. Attorney Martha Buyer recounts the story, first reported in the MIT Technology Review, of an AI agent that went rogue and generated a “nasty and misleading post” about an individual with whom it had had an unsatisfying (from the AI agent’s perspective) interaction.
Martha, who presented an Enterprise Connect 2026 session on AI governance with Irwin Lazar of Metrigy, writes that in the case she references, “the ‘thing’ that caused the harm is likely not able to be held responsible for its evil—and potentially illegal—acts, let alone identified in any meaningful way.” Clearly this is a gap that enterprises can’t tolerate over the long term.
I’ll close by returning to the long-ago world of public-network telephony. One of that era’s maxims was, “The most expensive part of phone service is the phone bill.” In other words, the systems that tracked and accounted for every single phone call, so they could generate a bill, represented a bigger cost to the phone company than the switches, phone lines, and other equipment that actually delivered the calls. That level of accountability made it possible for an outside consultant to find out something about a company that the firm’s own leaders didn’t know. It’s hard to imagine any AI vendor or service provider equipping enterprises with that level of visibility into their infrastructure today, but this inability will bring new and unprecedented risks for as long as it persists.
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