In 2026, artificial intelligence (AI) is mainstream in IT service management (ITSM) (as long as you don’t look too closely). Autonomous workflows, predictive operations, and conversational experiences are already in production across many organizations. Sounds fancy, right? Now, the more important question is whether the operational foundation underneath those AI initiatives is capable of supporting them. Or do we need an ITSM reset?
AI Adoption in ITSM Has Reached a Tipping Point
Our latest global research suggests that many organizations still have work to do. I know. Here we were thinking, “The AI is going to do all the work for us!” Well… not quite, as it turns out. While AI adoption in ITSM is nearly universal, the maturity of the underlying frameworks on which ITSM operates is still lacking. In fact, ITIL maturity is – candidly – rather low, globally. This is creating a gap between what organizations hope that AI will achieve and what their underlying service management environment can realistically support.
Let me just come out and say it: work must be documented digitally for AI to do the work.
Great AI outcomes require strong data and process rigor, and those are two things ITSM orgs have struggled with for 3+ decades. The struggle didn’t magically go away because “We’ve integrated ChatGPT”.
This is why I believe 2026 is shaping up to be the year of the ITSM reset.
The AI Adoption Question Has Already Been Answered
Based on our research, it’s abundantly clear that AI is no longer an emerging technology in ITSM. It’s now part of everyday operations.
Among the more than 1,000 IT professionals we surveyed, 95% reported using AI to improve ITSM processes. Organizations are applying AI across a wide range of activities, and the use cases with the highest adoption rates include IT asset tracking and reporting (41.5%), AI-powered chatbots (39%), task automation (38%), trend analysis (38%), and incident prediction and prevention (37%).
Importantly, these IT leaders say they are generally satisfied with the results. Across the 11 use cases referenced, AI is largely meeting or exceeding expectations. Respondents gave the highest satisfaction scores to translating service responses and knowledge articles (54.5%), and to task automation, IT asset tracking and reporting, and incident prediction and prevention (all at 53%). The dissatisfaction scores reported are relatively low (less than 10% in most cases); however, there don’t seem to be any known answers across the industry about what value is being achieved and how it is measured.
These results are encouraging for service management teams and signal that AI is delivering. But that’s only part of the picture. One that hides the need for an ITSM reset.
The Hidden ITSM Maturity Gap Behind AI Initiatives
While AI adoption is widespread, our research also shows that relatively few organizations describe their service management practices as mature. Only 12% of respondents say their ITSM approach is proactive and fully mature. By comparison, 31% report operating in partially structured environments, while 11% describe their approach as ad hoc and reactive.
In other words, more than four in ten organizations are still working in environments where processes, workflows, governance, and operational consistency are largely absent, or still a work in progress. This creates a notable contradiction. It means that organizations are moving ahead with AI while underlying service management issues, probably ones that they have struggled with for years, go unresolved. Importantly, do they know they need an ITSM reset?
It’s also understandable. AI is hard to ignore. New capabilities are appearing in the platforms organizations already use and are increasingly present in the tools people use outside of work. When technology is advancing this quickly, it’s easy for attention to shift to new capabilities before longstanding operational issues are fully addressed. So, I understand why we’re here, but it also makes the incoming challenges pretty easy to predict.
Why AI Amplifies Process Issues Instead of Fixing Them
While AI can support teams in many useful ways (automating tasks, surfacing insights, improving the end-user experience), it cannot compensate for fragmented processes, disconnected systems, or inconsistent data. If your knowledge articles are still providing steps to resolve the issue on Windows XP, the answer the LLM retrieves will still be stupid.
In many cases, AI reflects the environment in which it operates. If the underlying process is efficient, AI can help scale that efficiency. If the process is inconsistent, AI can make those inconsistencies more visible and spread them faster. AI isn’t creating these issues, but it is revealing them very quickly, conveniently, and fluently. AI is showing up the need for an ITSM reset.
The Operational Challenges Holding Back AI Success
One of the most interesting findings in the research shows that AI deployment challenges closely mirror broader ITSM challenges.
When asked about the biggest obstacles to deploying AI, respondents cited data privacy and security concerns (23%), integration challenges (18%), a lack of expertise (14%), and costs (13%). When asked about their biggest challenges in delivering IT services effectively, the same areas topped the list: ensuring secure and compliant IT operations (39%), budget constraints (38%), a lack of skilled personnel (36%), and a lack of integration between IT tools (35%).
These are not separate challenges. The same operational issues that make service management difficult also make it harder to scale AI successfully.
This matters because many organizations continue to view AI as a technology initiative. In reality, the factors that limit AI are often operational. The challenge is often less about finding the right AI capability and more about creating the conditions, the environment where it can deliver consistent results. Those same conditions would also have helped the team of humans perform better.
You can choose the most advanced or sophisticated AI technology on the market, and that won’t be able to compensate for the friction that disconnected tools, poorly documented workflows, fragmented data, and inconsistent governance will create.
What an ITSM Reset Looks Like in 2026
For many organizations, the next phase of AI success will not come from deploying some leading-edge new AI tool. Instead, the next phase of AI success will come from strengthening the operational foundation on which AI relies.
ITSM Reset #1 – Simplify and Standardize Core Processes
It’s only natural that service management environments accumulate clutter over time. Unnecessary approvals, overlapping workflows, disconnected tools, and redundant steps are really inefficiencies that create complexity for employees and make automation less effective.
ITSM Reset #2 – Reduce Tool Sprawl and Improve Integration
It’s no surprise that integration is one of the most persistent challenges organizations face. AI works best when it can access reliable information across connected systems. Information spread across disconnected tools and data sources makes this much, much harder.
ITSM Reset #3 – Strengthen Governance for AI-Driven Operations
As organizations expand their use of AI, they need confidence that processes are documented, decision-making responsibilities are clear, and data is being managed properly. Governance is sometimes viewed as bureaucracy, but it provides the structure that enables innovation to scale safely and responsibly.
ITSM Reset #4 – Build Security and Compliance into Workflows
Given that security and compliance is the top operational challenge cited by respondents, organizations cannot afford to treat them as separate initiatives.
ITSM Reset #4 – Measure AI Outcomes Consistently
While many respondents report positive experiences with AI, organizations need consistent ways to evaluate its business impact over the long term. Without clear measurement, it will be difficult to understand where AI is delivering results and where adjustments are needed.
Building Agentic Readiness Through ITSM Maturity
But this is a list of needs and constraints… which can feel a bit overwhelming. So let me be clear: The single most useful thing IT teams can do is think LEAN. Simplify down. Get back to the basics.
Identify the core processes and focus on getting them right. Find SIMPLE, obvious use cases for AI, and deploy it for one specific use case at a time. Then check back on it, see how it’s going, and make improvements. From there, when solving issues, seek out the single bottlenecks and tackle them one at a time.
Every day, we do slightly better than the day prior. This is how the whole industry is moving forward with AI.
The Next Competitive Advantage: Operational Excellence Before AI Scale
The next challenge is no longer about whether AI can improve service management. We are already seeing evidence that it can. The question now is: Do organizations have the operational maturity to expand those improvements across the enterprise?
The organizations that get the greatest value from AI over the next several years may not be the ones that deploy the fastest. I suspect they’ll be the ones that spend time strengthening their service management practices first. I’m referring to this as investing in “agentic readiness” instead of buying AI tools, crossing your fingers, and hoping for the best.
For IT leaders, service desk managers, and ITSM practitioners: it’s time to take a hard look at the fundamentals. Consider process design, integration, governance, security, and measurement. AI cannot create operational maturity on its own. It depends on the system of record it is placed on top of. As organizations pursue larger-scale automation and more autonomous service experiences, the strength (or weakness) of that foundation may prove to be the most important differentiator of all.
ITSM Reset FAQs
AI in ITSM depends heavily on structured, consistent, and well-governed operational data. In ITSM environments where processes are fragmented or poorly documented, AI struggles to deliver reliable outcomes at scale because it simply amplifies the quality (or lack of quality) of existing systems.
The ITSM maturity gap refers to the difference between high levels of AI adoption and relatively low levels of operational maturity. Many organizations are actively using AI in ITSM, but still operate with inconsistent processes, limited governance, and fragmented workflows, which prevent AI from reaching its full potential.
Yes. Many organizations report that AI is meeting or exceeding expectations in areas such as chatbots, task automation, asset tracking, and incident prediction. However, while satisfaction is generally high, many organizations still lack consistent methods for measuring the true business value delivered.
The most widely adopted AI use cases in ITSM include IT asset tracking and reporting, AI-powered chatbots, task automation, trend analysis, and incident prediction and prevention. These are typically focused on efficiency, speed, and improved end-user experience.
AI does not fix broken processes or poor data quality. Instead, it reflects and accelerates what already exists. If ITSM processes are inconsistent or poorly documented, AI will replicate and scale those inconsistencies rather than resolve them.
The biggest barriers include data privacy and security concerns, integration challenges, lack of skilled personnel, and budget constraints. These are the same challenges that already impact ITSM effectiveness, showing that AI issues are often symptoms of broader operational weaknesses.
AI relies on access to accurate, connected data across multiple systems. When tools and workflows are disconnected, AI cannot reliably interpret or act on information. Strong integration is essential for enabling consistent automation and decision-making.
An ITSM reset involves simplifying and standardizing processes, reducing tool sprawl, improving integration, strengthening governance, embedding security into workflows, and establishing consistent outcome measurement. It focuses on operational maturity before scaling AI initiatives.
Agentic readiness refers to the ability of an organization’s ITSM environment to support autonomous or semi-autonomous AI agents. It depends on clear processes, reliable data, strong governance, and integrated systems to ensure AI can safely execute actions across the service landscape.
A practical starting point is to adopt a lean approach: simplify core processes, focus on high-value use cases, reduce complexity, and improve one area at a time. Incremental improvements in process design, integration, and governance lay the foundation for scalable AI success.
Keith Andes
Keith builds product marketing functions where market intelligence drives the strategy. From customer insights to competitive intelligence to analyst relations, he focuses on collecting the right inputs to make sure every decision (from messaging to go-to-market strategy) is rooted in clarity and confidence, reasoning from First Principles.
