What to watch for as enterprises move from AI experimentation to autonomous operations

SHARE UPDATED 17:29 EDT / OCTOBER 26 2026

For the past three years, the enterprise artificial intelligence conversation has focused on what models can say. The next three years will be about what systems are allowed to do. That’s a harder problem that better models alone won’t solve.

On Wednesday, Oct. 28, I’ll be in Boston for the Bloomberg Tech Event: Building the Agentic Enterprise, sponsored by Hewlett Packard Enterprise Co. The agenda focuses on those who will be deploying and living with agentic AI. This includes chief information officers from Mercedes-Benz North America and elsewhere; chief security officers at Booking Holdings and Carnival Corp.; Estée Lauder Cos.’ head of data, analytics and AI; and James Robertson, HPE’s global field chief technology officer for networking.

The inclusion of enterprises is important. Vendors can talk about autonomous operations, but it’s the operators who decide when and how to turn it on.

The event comes a month after HPE’s Networking Investor Day, where the company argued that the network has shifted from plumbing to strategic infrastructure for AI. “AI is reshaping the technology stack, making the network more strategic,” said Rami Rahim, executive vice president, president and general manager of HPE Networking. Investors heard that as a growth story. CIOs should hear it as an operating model story, because the infrastructure that runs AI is increasingly being asked to run itself.

Here are five things I’ll be watching that should matter to CIOs, CTOs and enterprise leadership.

Generative AI has made insights table stakes. Most enterprises now have more recommendations than they can act on. The real question is no longer “Can AI generate insights?” but “Can I trust an AI system to take action without human approval at every step?”

An AI model that drafts a bad email creates an awkward moment. An AI agent that changes a network policy, reroutes traffic or quarantines a device and gets it wrong can take a business offline. I’ll be looking to understand how organizations draw the line between actions they fully automate, those that require human approval, and those that remain manual.

In my experience, the most advanced companies don’t treat autonomous operations as an on-off switch. They treat it as something a system grows into, one category of action at a time.

At Networking Investor Day, Sujai Hajela, HPE’s executive vice president and general manager of campus and branch, distilled the philosophy behind the company’s self-driving network: “Up is not the same as good.” Keep this in mind for any AI operations discussion. A network can be fully available yet still deliver a poor video call or stall an AI workload.

That shift, from measuring infrastructure health to measuring user and business outcomes, is a prerequisite for autonomous operations. You can’t let a system fix problems on its own if it can’t tell what a problem looks like from the user’s point of view.

The best example at the investor day came from a customer. Sajeev Nair, senior director of digital core services at ServiceNow Inc., described years of point products that kept “creating another fragmentation in your telemetry that is already fragmented.” After consolidating, he said, a manual workload that once took 2,000 to 3,000 hours a year now takes fewer than 60.

At the event, I’m also hoping to talk to practitioners about how they define success in the agentic era. I’m expecting the answers to focus on outcomes that move the business forward, such as hours returned to engineers, incidents that never reach a ticket queue, and faster time to value for new services over headcount reduction.

Rahim made a point at the investor day that every CIO funding AI should take seriously: companies can spend billions on graphics processing units, but if the network can’t meet performance demands, “those resources are not fully utilized.”

AE Natarajan, who runs HPE’s routing business, explained why AI traffic is different. Upstream and downstream traffic become more balanced, AI traffic can’t be cached like video, and agents generate traffic around the clock. “Digital users don’t fall asleep,” he said.

When agents become a new class of user on the network, the network has to understand who they are, what they’re allowed to reach and how they behave. That makes the network an identity and policy layer for AI, not just a transport layer. That’s where infrastructure, security and governance budgets start to converge.

The speaker lineup is interesting: at a networking event, three speakers lead security organizations. In most organizations, the chief information security officer, rather than the CIO, will decide how quickly autonomous operations proceed.

Security leaders face a two-sided problem. On one side, AI agents need access to systems, data, and each other, creating a growing population of nonhuman identities that traditional zero-trust models weren’t designed for. On the other, attackers now have AI, too. Anthropic’s Claude Mythos has helped vetted organizations identify more than 10,000 high- or critical-severity vulnerabilities, and that capability is expected to spread. That puts old, unsupported network gear squarely in the crosshairs.

In my conversations with security and network professionals, the “Mythos moment” was a wake-up call to modernize the environment. There is a tremendous amount of network infrastructure that is more than a decade old. These older switches and routers do not have the feature set to protect against AI-generated threats.

David Hughes, HPE’s senior vice president and general manager of SASE and security, made the case at investor day that “the network as a sensor” can detect threats and that the network itself can contain them, extending zero trust to agents and devices. I want to hear whether CISOs see autonomy as a risk to manage or a defense they need.

My view is that it’s both. Human-speed security operations won’t keep up with machine-speed attacks, so the same trust model enterprises build for autonomous operations will also determine how well they defend themselves.

Ask practitioners what’s slowing agentic AI, and you rarely hear “the model.” You hear about fragmented data, legacy infrastructure, unclear accountability, skills gaps, cost and, above all, trust.

At the investor day, HPE described trust in the self-driving network as evidence-based, with the system showing operators what it recommends and why before acting on its own. That’s the right approach, but it requires clean telemetry, which many organizations lack. Rahim also warned that when buyers hear self-driving claims from vendors, “you really have to look under the covers to see how much is actually there.”

At the event, I’ll also be exploring the hot topic of tokenomics. Bloomberg Intelligence software analyst Niraj Patel is on the program, and the return-on-investment conversation is where many agentic AI projects stall. Leaders need to show that autonomy cuts costs or unlocks new revenue, not just that it works in a pilot.

For CIOs and CTOs looking to move from AI pilots to autonomous operations, I’d recommend four steps:

The move to autonomous operations won’t be won by the best model or the most GPUs. It will be won by the enterprises that build the trust, data and infrastructure to enable AI to act safely. That’s why this lineup interests me: It brings together the people accountable for uptime, security and business results alongside the vendors on the same stage.

Networking Investor Day was an excellent AI “amuse-bouche” that showed HPE’s self-driving network has moved beyond the slide deck stage. The Bloomberg event is the main course and should reveal whether CIOs and CISO agree.

Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE.

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