The rapid emergence of artificial intelligence agents has created an unprecedented landscape of unknowns for IT leaders, outpacing even the early days of public cloud adoption.
With no single vendor offering a complete AI stack, organizations must rely on pre-validated partnerships to reduce risk. For CIOs, the challenge lies in understanding how different building blocks fit together to manage IT operations and optimize infrastructure, according to analyst Scott Sinclair, practice director, Omdia.
"There is no one single company that is the one-stop shop for everything you need from artificial intelligence," Analyst Scott Sinclair said, referring to analysis of Omdia’s AI Ecosystem database, which tracks over 1,000 companies involved in artificial intelligence, including hardware manufacturers, software developers, cloud providers and specialized startups.
"Not even Nvidia, even though they have great technology and they do a bunch of great things, it requires a number of different tools and technologies."
More data points to compounding challenges facing IT teams as they move more AI agents into production. Omdia’s 2026 analysis of "AI factory" infrastructure identified key complications as long time-to-market and ROI validation, digital sovereignty concerns, AI talent gaps and systemic engineering complexity. A separate Omdia analysis of the LLMOps market warned that open access to generative AI tools introduces fragmentation, shadow IT and technical debt at an unprecedented pace.
Sinclair said the pace of AI innovation forces organizations to partner with multiple vendors to stay current and avoid falling behind. They’re looking for pre-validated solutions that work together and reduce the inherent risks of adopting new technologies, but the market is teeming with ever more options.
"Every organization has an AI story, and they all sound very similar," Sinclair said. "Where the burden falls, unfortunately for right now, it falls to the CIO and their team to parse through the noise."
IT teams must determine what specific building blocks each vendor delivers and how those components integrate with existing systems, advised Sinclair. CIOs must constantly interact with vendors to leverage those relationships.
He sees IT organizations exploring how agents can manage IT operations, automate infrastructure and optimize environments. Early adoption typically centers on knowledge sharing, such as converting training manuals into searchable AI databases and improving customer support workflows.
Some organizations are exploring agent-based models for troubleshooting to identify root causes and automate remediation. Yet the industry remains in a learning phase, with different organizations exhibiting varying levels of risk appetite for agent integration.
Sinclair compared the current AI landscape to the early days of public cloud adoption. While cloud computing represented a fundamental paradigm shift, it remained somewhat contained among a handful of major players. Cloud migration primarily optimized existing data center operations using familiar language and concepts. Instead, the AI transition features a growing mix of leading tech providers, startups, new tools and capabilities.
"I can't think of a timeframe where there's just been this combination of scale of unknown as well as rapid evolution and innovation happening all at the same time," Sinclair said.
Video transcript (edited):
Scott Sinclair: I think something that is absolutely crucial when you think about artificial intelligence, there is no one single company that is the one-stop shop for everything you need from artificial intelligence. Not even Nvidia, even though they have great technology and they do a bunch of great things, it requires a number of different tools and technologies. Not only that, but we're also seeing rapid innovation in this space. Everyone's talking about agents right now. Nobody's really talking about agents a year ago. So in next year, there's probably going to be something else that we're all talking about that no one's talking about this year. So the challenge is that it's requiring partnerships A, because nobody can offer a full stack, everything you possibly need, infrastructure code, tools, everything. Number one, but then also the fact that organizations want to partner because they also need to stay in touch with all the new innovation that's happening to make sure they don't get left behind.
And so if you think about that from a business standpoint, from a consumer or an IT leader, a CIO standpoint, on one hand, that's great because what you want is more partnerships, it provides better validation to solutions. Hey, look, these companies are working together. They pre-validated. They've made sure it works. So it reduces some level of risk. That's helpful. The other part, though, is that it does make it complicated because every organization has an AI story, and they all sound very similar. And where the burden falls, unfortunately for right now, it falls to the CIO and their team to parse through the noise and understand, okay, yes, every company has an AI story. What is your building block, so to speak, that you're being delivered? How does that fit with the rest of the other building blocks? Who do you partner with, and what's the best way to leverage you?
And these are the types of questions that we see being asked over and over again, and organizations are just going to have to keep asking them. We see a lot of excitement in, okay, how can I put in agents? How can this help manage my IT operations environment? But at the same time, we sit and say, "Well, there are a lot of tools that are already developed to do a lot of AI, do a lot of automation for infrastructure, as well as optimization." So right now, I think we're still in a 'we need to learn' phase. I see a lot of interest in things like knowledge sharing. How can we basically put all our training manuals into AI and make that easier to search? We see a lot in terms of how do I use AI to do things like work with customer support and trouble tickets, things like that.
Those are early areas where people have seen adoption. I have seen organizations say, "Well, look, maybe we can do some sort of agent-based model to do troubleshooting and put in, identify possible root cause of issues, and some sort of remediation." I think we're still very early in that stage, but honestly, this is a stage that even I, as an analyst, am actively researching to see where organizations are. I think this is going to be a rapidly evolving space, and it's going to be where different organizations just have a different level of risk appetite to how they want agents to work and how quickly they're able to adopt them. Put in perspective how fast people have to learn things nowadays. As I think about that question, because it's something I think about quite a bit, I want to compare it to the early days of public cloud adoption, because it was very much AWS was out there, you saw movement from Google, you saw moving from Microsoft.
Okay, how do we modernize? Everyone had to move to the cloud. That was the thing. We all need to move to the cloud. But even that, even though it represented a fundamental paradigm shift in the way in which we think about apps and infrastructure and even IT operations, it was somewhat contained because at the end of the day, there were still a handful of major players. There wasn't just the proliferation of hundreds, if not thousands, of different startups of different tools and different technologies, and everyone's doing your own thing. And also, cloud in a sense was a way of optimizing things we already had, right? It was a way of, instead of doing our own data center, we can offload the work to somebody else. Okay, look, I can access it not from a component level. I can do it from an SLA level. So the language and the ideas stayed very similar.
I think with AI, one of the fascinating elements is the area of what's unknown is much bigger than it probably has ever been in a long time. I can't think of a timeframe where there's just been this combination of scale of unknown, as well as rapid evolution and innovation happening all at the same time. So yeah, to me it's unprecedented.
Ken Kaplan is Editor in Chief for The Forecast by Nutanix. Find him on X @kenekaplan and LinkedIn.
Jason Lopez contributed to this video. He is executive producer of Tech Barometer, the podcast outlet for The Forecast. He’s the founder of Connected Social Media. Previously, he was executive producer at PodTech and a reporter at NPR.
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