2026 Scaling AI in IT Operations: The Path to Maturity in 2026

Ivanti's latest research — a survey of 1,500 IT professionals and 2,400 office workers across six countries — reveals a striking reality: AI adoption in IT operations has crossed a critical threshold. More than half of organizations are now deploying AI broadly across multiple workflows or at full business-critical scale. Only 2% report no AI use at all. 

But adoption alone doesn't equal maturity. As AI moves from surfacing insights to taking autonomous action, a dangerous gap has emerged between how organizations think they're governing AI — and how they actually are. 

With IT organizations expecting AI and automation to handle nearly half of all operations within 18 months, the stakes have never been higher. How can IT leaders close the governance gap, build the right accountability structures, and unlock AI's full potential — without the risk? 

Ivanti's 2026 Scaling AI in IT Operations: The Path to Maturity report explores the progress, the pitfalls, and the path forward for IT professionals navigating the next stage of AI transformation. 

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To read the report and access additional media, including presentation-ready slides and downloadable charts and graphs, visit ivanti.com/scaling-ai-report 

For more Ivanti research on IT, security and the future of work, visit ivanti.com/research. 

About Ivanti 

Ivanti elevates and secures anytime, anywhere work so that people and organizations can thrive. We make technology work for people, not the other way around. Today’s employees use a wide range of corporate and personal devices to access IT applications and data over multiple networks to stay productive wherever and however they work. Ivanti is one of the only technology companies that finds, manages and protects each IT asset and endpoint in an organization. Over 40,000 customers, including 88 of the Fortune 100, have chosen Ivanti to help them deliver excellent digital employee experiences and improve IT and security team productivity and efficiency. At Ivanti, we strive to create an environment where all perspectives are heard, respected and valued, and we are committed to a more sustainable future for our customers, partners, employees and the planet. For more information, visit ivanti.com and follow @GoIvanti. 

Transcript

Introduction

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You're listening to the audio version of Scaling AI in IT Operations, The Path to Maturity in 2026. To read the full report and access downloadable charts and graphs, visit Avanti, Ivanti.com slash research. AI is delivering big wins for IT. Now comes the harder work: scaling what works, closing the governance gap, and making sure the benefits reach every corner of the organization. Two years ago, conversations about AI and IT were largely about potential. Today, they're about results, and about something more nuanced too. The difference between what AI can do on its own and what becomes possible when AI is embedded into the right operational structures and disciplines. Avanti's 2026 research makes that distinction unmistakably clear. We surveyed 1,500 IT professionals and 2,400 office workers across six countries. The United States, the United Kingdom, France, Germany, Australia, and Japan. What we found is a story of widespread AI adoption, but also a story about how much the outcomes vary depending on how deliberately organizations have invested in governance, skills, and the right use cases. More than half of organizations, 56%, are now deploying AI broadly across multiple workflows or at business critical scale, where AI is fully integrated and driving continuous improvement. Only 2% report no AI use at all, and yet the gap between early adopters and the most mature organizations is widening. Understanding what separates those two groups is what this report is really about.

Part 1: The maturity divide

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Part 1. The maturity divide. AI has become embedded in IT operations with remarkable speed. But advanced organizations don't just do more with AI, they've fundamentally transformed how IT operates. Because they've paired AI with the automation disciplines that turn its insights into action. To understand where organizations stand today, Ivanti's research measured two distinct dimensions of maturity. Organizational AI maturity. How broadly and deeply an organization has integrated AI into its operations. And individual AI maturity. How deeply a person integrates AI into their own daily work. Respondents rated their organizations on a five-point scale, from early experimentation and pilot projects, all the way to scaled, business critical use with continuous improvement. The picture that emerges is one of deep, widespread adoption, particularly in IT service management, ITSM, and endpoint management. More than half of organizations report broad or deeply embedded AI use in both. In IT service management, the most common current applications include virtual agent and chatbot support, adopted by 58% of organizations, ticket classification and routing at 56%, and automated ticket resolution at 51%. In endpoint management, AI is already handling anomaly detection at 57%, device vulnerability identification at 55%, and patch prioritization at 47%. AI's intelligence layer surfaces signals, flags issues, and accelerates the decisions that IT teams need to make faster than manual work could ever achieve alone. But here's something important to understand about those endpoint capabilities. AI detecting an anomaly or identifying a vulnerable device is a powerful first step. What converts that signal into a resolved issue before an end user even notices a problem is a broader operational discipline. That's where autonomous endpoint management comes in. Autonomous endpoint management is the framework where AI doesn't just detect issues, but where automation provides the workflow structure to actually resolve them. Autonomous endpoint management represents one of the highest value applications of AI and automation in IT. The data tells us it's where the most mature organizations are heading and where the real competitive advantage will be built. And the pace of change isn't slowing. IT organizations expect AI and automation to handle nearly half, 46% of their operations within just 18 months. That's a transformation timeline that demands urgent, deliberate action. Mature AI organizations don't just use AI more, they operate at a categorically different level of IT performance. They maintain clearer accountability for AI decisions and more formal governance structures. And across productivity, collaboration, and workforce planning, the gap between early experimenters and scaled organizations is dramatic.

Part 2: What AI is actually doing for IT

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Part 2. What AI is actually doing for IT. AI is saving IT teams thousands of hours, shifting operations from reactive to proactive, and unlocking capacity for the work that actually matters. But the shift to true proactive operations doesn't happen through AI alone. Ask any IT professional what AI has done for their day-to-day work, and the answers reveal a genuine transformation. Nearly half of all IT workers, 45%, say AI makes their work both faster and better. 63% say they spend less time on repetitive tasks. IT professionals as a whole report saving more than 200 hours annually due to AI. Five full work weeks of recovered capacity. When you look at the maturity breakdown, these findings become even more compelling. At organizations where AI is scaled or business critical, 54% of IT pros say AI makes their work faster and better. That is more than double the rate at organizations still in early experimentation, where only 24% say the same. And at the individual level, the most advanced AI users save an average of 6 hours per week, double the three hours saved by those at the least mature level of use. Beyond individual productivity, AI is also helping IT teams get ahead of problems before they become disruptions. Overall, 64% of IT professionals say AI often or very often helps their team detect or fix issues before end users are even aware there's a problem. That figure rises from 43% at early experimentation organizations to 89% at scaled, business critical ones, a gap that tracks directly with how deeply AI and automation are integrated into endpoint workflows. Here's a key distinction our research reveals. AI is essential to that shift from reactive to proactive, but it can't complete it alone. AI surfaces signals and guides decision making. Autonomous endpoint management provides the discipline and the workflow structure that transforms those signals into action, empowering IT teams to resolve issues before end users ever encounter them. That's the partnership that makes truly proactive IT possible. At the most mature organizations, we see exactly that in action. AI-driven automation is moving beyond alerting to acting autonomously, automatically adjusting performance settings, reported by 52% of scaled organizations, isolating risky devices at 50%, restarting services at 47%, and applying patches at 46%. These autonomous actions are deployed at more than double the rate of less mature organizations. And the payoff is automated decisions, intelligent remediation, fewer disruptions, and lower risk. The metrics tell the same story. Among scaled organizations, 64% measure AI's impact through time to resolution, 64% through customer satisfaction scores, and 65% through cost savings, compared to just 38%, 26%, and 37% respectively, at early experimentation organizations. Mature AI organizations perform better, and they measure that performance far more rigorously. The collaboration benefits are substantial as well. More than half, 54%, of advanced organizations say AI has improved cross-team collaboration, compared to just 16% of basic level organizations. 60% report using more common tools and platforms across IT, security, and business teams. 57% say AI has improved knowledge sharing. 53% report sharing data more easily. And 50% have created joint workflows that now span multiple teams. AI is breaking down the silos that have historically slowed IT down. This all points to what the research calls the strategic unlock. Half of IT pros say AI helps them focus on more complex or strategic work, and 45% say it gives them better visibility and insights for decision making. Among the most advanced individual AI users, that first figure rises to 61%. Compared to just 22% of basic users, the most forward-looking IT leaders are already asking the right question. Not, how do we use AI to do what we're already doing faster? But, what should IT be doing that it never had time for? AI and automation are clearing the path. The strategic question is what to build with the capacity they free up. As Sterling Parker, Ivanti's senior vice president of Global Solutions and Services puts it, the organizations that will pull ahead are those that focus on business outcomes and ensure accountability is structural embedded and clearly defined. Every IT leader must ask, is what we've adopted truly yielding the returns we want? Are employees seeing that same value? Because AI, at the end of the day, is making it possible for humans to be even better at what they do. Part

Part 3: The governance gap

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3. The governance gap. Organizations are moving faster on AI deployment than on AI governance. With nearly half of IT operations expected to be automated within 18 months, that gap is becoming a liability. And for autonomous systems, it's a non-negotiable one. Here is one of the most important tensions our research uncovers. The speed of AI adoption has outpaced the development of the governance structures needed to manage it safely and effectively. And as AI moves from surfacing insights to taking autonomous actions, that gap carries real operational consequences. Governance isn't just a best practice, it's a non-negotiable foundation for AI adoption. That means clear and embedded protocols for when AI can act autonomously and when it must escalate, and the organizational discipline to actually follow them. Without that foundation, AI turns from a strategic asset into a serious liability. Most IT organizations report having basic governance processes in place, primarily risk review processes at 65%, policies for evaluating and approving new AI tools at 59%, acceptable use policies at 58%, and oversight bodies at 49%. On the surface, that sounds encouraging, but the accountability picture beneath it is more sobering. 85% of IT pros claim there is a named accountable owner for every AI agent and workflow within their organization. Only 42% say that accountability is actually clear in practice. And among companies with AI policies in place, just 24% of employees say those policies are followed very consistently in day-to-day work. There is a significant distance between having a governance framework and genuinely living by it. This matters all the more given the risk landscape. 68% of IT professionals say they have personally seen AI produce hallucinations, incorrect or misleading outputs, with potential operational impact. More than half, at 52%, say their team caught those errors before they caused real problems. But 16% weren't so fortunate. When AI is increasingly making or enabling autonomous decisions about critical infrastructure, that's a risk profile that demands serious governance investment. And the shadow AI problem runs deep. Organizational leaders are nearly twice as likely to keep their AI use secret compared to other employees, 42% versus 23%. Among those leaders who admit to hiding their AI use, fully 52% say they do so for what they call a secret advantage. This isn't a cultural curiosity. It's a signal that governance frameworks aren't yet reaching the people who have the most influence over how AI gets deployed. In fact, governance has become the single most commonly cited barrier to faster AI deployment. Among IT professionals, 27% identify governance, security, or compliance concerns as their biggest obstacle, outpacing skills shortages at 20%, technology limitations at 17%, and data challenges at 14%. The governance gap divides sharply along the maturity spectrum. At scaled, business critical organizations, 69% report comprehensive governance is in place. At early experimentation organizations, that figure is just 15%. Governance is not a barrier that mature organizations must clear. It's a capability they've deliberately built in the foundation of their AI deployment initiatives. Effective governance resolves the core tension in AI adoption by codifying trust thresholds for different types of operations. It defines where AI can and should act autonomously, restarting a failed service, applying a routine patch, and where human validation is required, such as system-wide configuration changes or high-severity incident responses. The organizations that have built this kind of governance haven't just reduced their risk, they've unlocked the ability to deploy AI and automation with confidence. Brooke Johnson, Avanti's chief legal counsel and senior vice president of HR and Security, frames the solution clearly. Rather than playing an endless game of whack-a-mole with rogue AI use, forward-thinking companies are shifting toward governed enablement. That means transitioning to guardrail-based governance, establishing clear operating boundaries that let teams deploy confidently without case-by-case approval.

Part 4: AI experience drives optimism

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Part 4. AI experience drives optimism. For most IT professionals, working closely with AI has made them more optimistic, not less. And it's giving them a clearer picture of the human skills that will matter most as AI and automation take on more of the routine work. Start with job security. Fear of displacement is low. 79% of IT professionals and 77% of office workers say they're not at all, or only somewhat, worried about AI reducing or replacing their jobs. That's confidence rooted in what people are actually experiencing in their work. Many IT pros say AI has made their professional lives measurably better. 53% say it's given them more control over their time and priorities. 49% find their work more interesting or satisfying. And 46% report a better work-life balance. A large and growing share of IT professionals also view AI as something more than a productivity tool. Among the most advanced users, 37% now describe AI systems as mostly or fully a virtual teammate, up from just 10% at the basic level. And more than half of IT pros, 53%, say they'd be comfortable having their performance compared to that of an AI agent. That number would have seemed startling just a few years ago. But it reflects a workforce that has grown fluent enough with AI to see it clearly, including both its strengths and its limits. IT professionals are clear-eyed about where human judgment remains non-negotiable. 55% say they would never rely on AI without human review for high severity incidents. 52% say the same for communicating incidents to executives or other stakeholders. AI earns trust in proportion to the stakes and the context. And experienced IT professionals understand exactly where that line falls. This connects directly to how the skills required of IT professionals are evolving. As routine tasks shift to AI and automation, the capabilities that matter most are shifting too. 83% of IT pros agree that as AI automates more routine tasks, emotional intelligence will actually become more important for their profession. When AI handles detection and remediation, IT professionals shift from firefighting to strategy. They need to understand what the business needs, make judgment calls about risk and priorities, and explain complex technical decisions to people who aren't technical. And critically, AI literacy is now a core professional skill, not just about using AI fluently, but knowing when to trust it, when to override it, and when to push back. The individual maturity data reveals a powerful self-reinforcing cycle. The more deeply people engage with AI, the more motivated they are to grow. Intent to upscale climbs from 37% among basic AI users to 86% among the most advanced. A 49-point gap that signals a workforce actively investing in its own future. IT professionals are already well ahead of the broader workforce on AI literacy. 62% rate their AI literacy as high or very high, nearly double the 27% of office workers who say the same. IT pros are also far more likely to use AI frequently or as a fully integrated part of their work, at 51% versus 33% for office workers. But that 62% figure also means more than a third of IT professionals still need foundational development. As AI takes on a larger share of routine operations, the organizations that close that gap systematically will capture far more of the value their AI investments can deliver. Organizational structures are already changing to reflect this. Nearly three in four organizations, 72%, have already created dedicated AI roles or teams, with another 13% planning to do so. At scaled organizations, that figure rises to 91%. AI product and program owners are now present in 54% of organizations, embedded AI specialists in 51%, and governance committees in 50%. All increasingly standard features of the mature IT function. More than one in three organizations, 37%, report that at least a few IT roles or teams have been significantly reshaped by AI, a figure that reaches 57% in the technology industry. The IT workforce isn't being replaced, it's being redefined.

Part 5: The path forward

SPEAKER_00 20:55

Part 5. Early AI gains are real and measurable, but they'll only compound if an organization builds the foundation beneath them to scale. Given that more than half of IT organizations are already deploying AI at broad or business critical scale, and that 46% of all IT workflows are expected to be automated within the next 18 months, the window for measured, thoughtful action is narrowing. Here is what the research tells us organizations need to do. The first imperative is to start where you are. The maturity data carries an important message for any organization that feels behind. You don't have to leap to the most sophisticated AI deployments to see meaningful results. The gains begin early and compound with investment. For most organizations, that means getting more from the automation they already have, deploying it more broadly, measuring it more rigorously, and building the operating Habits that set the stage for the next level of scale. The second imperative is to build governance in, not bolted on afterward. Organizations that have closed the governance gap share a common approach. Accountability is structural. It's embedded in policy and practice, not aspirational. Every AI agent and workflow has a named owner. Escalation paths are defined before they're needed. Policies exist and are consistently followed. Achieving that standard means building governance controls directly into the platform itself, so that trust thresholds, escalation paths, and approval requirements are enforced automatically. It means aligning CIO and CISO incentives around shared outcome metrics, risk appetite, uptime, resolution rates, and employee experience. And it means moving from siloed point solutions to platforms designed for autonomous operation that can connect signals to actions across the entire IT and security environment. Without this kind of unified foundation, the risk of autonomous AI deployment outweighs the reward. As Sterling Parker, Avanti's senior vice president of Global Solutions and Services puts it, you can't govern what you can't see. Organizations scaling successfully today are consolidating on a unified platform that serves as a system of record, especially for agentic AI components. That's the foundation that makes AI auditable, predictable, and scalable. The third imperative is to redesign roles, not just augment them. When AI and automation handle routine detection, triage, and remediation, IT's value shifts to work that requires context, judgment, and relationships. That transition demands redefining performance metrics, rewarding strategic impact and cross-functional collaboration rather than ticket closure speed. It means redesigning career paths to create advancement opportunities for IT professionals who excel at translating technical complexity into business value. And it means rethinking hiring criteria to evaluate candidates on business acumen and communication skills alongside technical expertise. Augmenting existing roles isn't enough. The organizations that will lead are the ones that rethink what IT is for. The fourth and final imperative is to make AI upskilling systematic. Rising intent to upskill is one of the most promising signals in this research, but intent only converts to capability when organizations build structured programs to support it. That means investing in AI literacy training for the more than a third of IT professionals who still need foundational development, and for the broader office workforce, where only 27% rate their AI literacy as high or very high. It means building business acumen training to help IT professionals understand how their work connects to revenue, customer experience, and competitive positioning. It means developing communication and influence skills, the ability to explain technical decisions to non-technical stakeholders and build buy-in for change. And it means creating real opportunities for IT professionals to work on cross-functional projects that require judgment, not just execution. Literacy isn't just about using AI, it's about knowing when to trust it, when to override it, and when to push back. The organizations that act on these imperatives will do more than survive the AI transformation. They'll lead it. The ones that wait, assuming governance will sort itself out, or that roles will naturally evolve without deliberate investment, are taking on risks in an environment that is moving too fast for passive approaches. Avanti's research makes one thing clear above all else. The question is no longer whether AI will transform IT. It already is. The question is whether your organization will shape that future, or simply be pulled along in its current. The path to maturity is well lit. The organizations that walk it with intention, pairing AI's intelligence with the automation disciplines that turn insights into outcomes, will emerge with IT operations that are faster, more resilient, and more strategically valuable. If you enjoyed listening to this report and want even more Avanti research, you can subscribe to this podcast to get the latest Avanti research in your feed as soon as it's released. You can

Outro

SPEAKER_00 26:49

read the full report, download charts and graphs, and explore the rest of Avanti's research at Avanti.com slash research. You can follow Avanti on social media at GoAvanti. And you can visit us at avanti.com to learn more about our products and solutions. Thanks for listening.