Data Intelligence Becomes The Dividing Line For Enterprise AI Maturity, Says One Of Cisco's Field CTOs

The views and opinions expressed are those of Bogdan Muraru and do not represent the official policy or position of any organization.

The question that sorts enterprises on AI has stopped being whether they've adopted it. The sorting questions sound like audits now: where does the data live, on whose hardware, under which law, reachable by which agents? Answering them takes a data layer smart enough to know itself, and most infrastructure was never built for one.

Bogdan Muraru is a Field CTO for UK enterprise at Cisco, advising large customers on technology strategy, cyber security, and operating models. He holds an NCSC-certified master's in advanced cyber security from King's College London and a second in systems thinking, after twenty years across engineering, architecture, and transformation. From his seat, AI demand has met the physics.

"Everything they do with AI will not only amplify the business value they're looking for, but also amplify the burden on the infrastructure," Muraru says. "Most of the enterprises are not built for that."

The six-month business case

His clearest signal is how short plans have gotten. "Ten years ago there were five-year business cases. Then it was two, three years. Five years ago my VPs were saying 'If it's longer than two years, we're not going to talk about it,'" Muraru says. "Now nobody wants to talk about anything more than six months."

He reads that compression as realism. "We might have made some money with it. What we can count on is the experience to scale out or reshuffle and stay relevant. You just need to be faster and much more flexible." The operating model has to absorb that pace. An agentic mesh that responds too slowly misses the window it was built to catch.

What mature looks like from the field

Mature organizations split the work. "You get at least two divisions. One is busy onboarding technology for production workloads, with value in the near term," he says. "And then an innovation team, folks able to build their own labs and hack something, without affecting anything in production."

His fastest test is whether the company has established its own "front door" to AI. That could mean building an internal AI portal using proprietary or open-source models and agent-based tools, or providing governed access to leading frontier-model providers such as OpenAI’s ChatGPT or Anthropic’s Claude. "For most of the companies I work with, that is a clear maturity indicator," Muraru says. "It's not just the Copilot round. That's maybe the lowest one you can get these days." A company at that stage knows the difference between training a model and using one, worries about shadow AI, and protects intellectual property along with function.

A value chain with no common language

What's missing, in his view, is vocabulary. ITSM frameworks gave enterprises a shared process language, a way for the left hand to see the right. "For AI, the common language is not there yet. From acquiring data, cleaning data, training, getting tools to use the data, that common language is still missing," he says. "Right now the value chain is drawn in our heads more than anywhere else."

The usual translators trail too. "Usually it's the consultants, but the consultants now are also a few steps behind, because everything is moving so fast." An ISO standard for AI management exists now, and EU cyber rules keep arriving, but he finds even large enterprises unsure how to digest them.

Sovereignty goes all the way down

The sharpest conversations run through sovereignty, and he insists they run the full stack. "Data fabrics are the way forward. Like we had cloud fabrics in the past, data fabric is what will rule," he says. "You have to understand how that data fabric brings you the intelligence that you need." Classification settles nothing alone. "It doesn't matter that I've classified data as strictly confidential, accessible only from London, if my data center is in Berlin." A clean design still inherits its vendors' geopolitics. "If somebody decides to unplug Microsoft, Google, or Alibaba, what does that mean for the sovereignty we've just designed?"

He's watching conversations swing from cloud-first toward neoclouds and private AI, and he calls admitting you can't hold everything sovereign its own maturity sign. "If I'm building the next recipe for Cadbury chocolate in the UK, I'd better do it on a Cadbury server. And the same for Coca-Cola. They'd better do it in the States."

The frontier he has no clean answer for is identity. "You have a workflow that spins off agents, and those agents spin off other agents. You only know what to start with," Muraru says. "What if a US agent reaches out to a UK agent that is connected with a French agent? Is that part of the sovereignty piece? Do we have a way to confirm identities and control these workflows? That's a very complex issue right now."