IT professionals share their views on the most pressing enterprise technology projects for 2026.
It’s the season for “looking ahead to next year” articles that tell some group or another what they should be doing or what some “expert” says they should do. Let’s take a different slant and focus on what one group— arguably the most important tech buyer group — actually says they’re going to do. I’ve collected 284 comments on tech priorities for 2026 from enterprises, and here are the top five.
1. AI optimization and uncertainty
Let’s start with a priority that embraces two unsurprising sentiments. The first is that the top priority, cited by 211 of the enterprises, is to “deploy the hardware, software, data, and network tools needed to optimize AI project value.” The second is that there is significant uncertainty regarding just what those tools are and even over whether they could be confidently identified.
The great majority of the 211 said that their focus is on the agent form of AI, but recall from this blog that enterprises have always seen three distinct agent models: the “interactive” one workers used much like we use chatbot AI today, the “workflow” model that sits in an application workflow like another piece of software, and the “embedded” form that’s built into an application. Enterprises think they have a handle on workflow AI, in terms of what it runs on and what it connects to. But even there, many enterprises say that a new AI element in the workflow is useful because it integrates insights drawn from a broader set of sources, which might in theory be anywhere, data-wise. And they’re even less sure about the impact of the other two models.
Embedded AI agents are mostly used in either business analytics or to support network or IT operations, and both these missions seem to put AI in a simple extension role, so aren’t generally expected to require a lot of rethinking on infrastructure or practices. Not so, say enterprises. AI agents are, in general, data magnets. They want more information, they want more consistent information, and they need some measure of data value or weight in making decisions. For example, a netops AI tool might want information on seasonal sales patterns to forecast traffic better. Without broader information sources, it’s harder to build enough value to make a business case, and AI use of data is often implicit in how the model works, where traditional applications use data because the developer called for it. How do you know what AI is going to call for?
The interactive form of AI poses the greatest risk. While enterprises see this model as limited to perhaps ten or fifteen percent of workers, those with the highest unit value of labor, it’s nearly impossible to predict what resources a given worker interaction might demand. “One question could use as much compute, as much data, and generate as much traffic, as a week’s running of a normal application,” one planner complained, noting that this sudden resource draw-down can actually impact IT performance across a big chunk of the business.
And interactive agents might broad access permission, raising data governance concerns. Enterprises agree you never want to give an agent access to everything. First, obviously, this would create massive privacy and governance risks, but second, there’s a significant risk generated by any sort of redundancy in the data. It does beyond simple deduplication, too. Enterprises recommend against using a mixture of detail and summary data, including “derived” data. There’s a risk that working on mixed detail levels can bias results accidentally. “Twenty summaries of the same detail data can look to AI like twenty other sources,” one enterprise noted. IBM’s decision to buy Confluent, a known player in building data-flow applications, may be linked to the data access control and governance issue.
2. Cloud backups
The second priority is also unsurprising given recent news. Of 284 enterprises who commented, 173 said that they needed a strategy to back up cloud components in the event of cloud outages. This, they say, is a lot more complicated than senior management thinks. First, you have to decide just what really needs backed up. “You can’t totally immunize yourself against a massive cloud or Internet problem,” say planners. Most cloud outages, they note, resolve in a maximum of a few hours, so you can let some applications ride things out. When you know the “what,” you can look at the “how.” Is multi-cloud the best approach, or can you build out some capacity in the data center? Enterprises note that building in resilience in any form may require redesigning some applications to make cloud-hosted elements portable, and that can also mean looking at where application data is stored and how access to it is connected.
3. Infrastructure simplification
Priority three is managing the technical complexity of infrastructure, cited by 139 enterprises. “We have too many things to buy and to manage,” one planner said. “Too many sources, too many technologies.” Nobody thinks they can do some massive fork-lift restructuring (there’s no budget), but they do believe that current projects can be aligned to a long-term simplification strategy. This, interestingly, is seen by over a hundred of the group as reducing the number of vendors. They think that “lock-in” is a small price to pay for greater efficiency and reduction in operations complexity, integration, and fault isolation. This is the biggest shift against multi-vendor or open infrastructure I’ve ever seen.
4. Prioritize governance
The close number four priority is more administrative than infrastructure-related; 124 enterprises in our group said they needed to “totally revamp governance.” Yes, AI is a big factor in this, but so is the elastic-hosting model of cloud and multi-cloud, and the “sovereignty” issues associated with operating across multiple jurisdictions, and the increasingly chaotic nature of regulations. The percentage of enterprises who say they need some formal “government affairs” input to management practices has increased from 12% in 2020 to 47% for 2026. For example, EU cloud and AI sovereignty concerns impact plans for both AI and cloud application resilience.
The biggest problem, these enterprises say, is that governance has tended to be applied to projects at the planning level, meaning that absent major projects, governance tended to limp along based on aging reviews. Enterprises note that, like AI, orderly expansions in how applications and data are used can introduce governance issues, just like changes in laws and regulations. AI complicates this because it’s difficult or impossible to know just what data AI is accessing, if there are no filters on data availability. All this is a governance challenge, but it can pale in comparison to the fact that companies aren’t used to even thinking about governance absent a project framework. Do you need to create “governance projects?” If so, how are they justified, funded? Where there are hard changes in law or regulations, there are procedures, but not so much with other challenges. AI agents, even workflow agents, can creep into governance problems as usage grows, for example.
5. Cost management
The final priority on our list, with 108 enterprises citing it, is in many ways a barrier to fulfilling any of the other goals they identify: Do more for less. Of our 284 enterprises, 226 said that they were under more budget pressure for 2026, and only 9 said they had less pressure (for the rest, pressures were the same). It’s interesting, though, that number five on the priorities list is the lowest scored for cost management since 2008/2009.
The interesting thing about this particular priority is that, unlike prior years where the “cost” being managed was presumed to be the capital cost of the technologies involved, the equipment and software, the focus for 2026 is the total cost, what would be classified as “total cost of ownership” or TCO. This would be easy, or at least possible, in the context of traditional project thinking, but so many of these priorities blur the lines between “projects” that require review, justification, and approval, and normal day-to-day business decisions that usually dodge much of that formality. How do you assess the TCO of AI efficiency optimization overall, or cloud application resilience, or governance?
Overall, the comments from enterprises suggest that while they’re prioritizing many expected issues, they’re also dealing with more subtle ones, and even on topics like AI, they’re taking a different slant than many had expected. It’s more about their ecosystem than the individual parts, and that should make 2026 an interesting year with a lot of important trends to watch!
