AI challenges for IT teams in 2026: What’s really holding organizations back?

AI adoption in IT has reached a paradoxical moment. While AI tools are spreading faster than ever, many organizations are struggling to turn early momentum into sustained, enterprise-wide impact. Research from MIT has shown that nearly 95% of AI initiatives fail to deliver expected business outcomes, often stalling at pilot or early deployment stages.

McKinsey Research also reports that nearly all companies are investing in AI, but only about 1% describe themselves as fully mature. The finding reinforces a growing reality: adoption alone does not translate into enterprise-wide scale or measurable transformation.

The question facing IT leaders today is no longer whether AI works, but how to deploy it in a way that is secure, trusted, and sustainable, both within IT and across the business.

We recently published the 2026 edition of the State of AI in IT report in collaboration with ITSM.tools and went deeper into the biggest AI challenges IT teams face, why they persist even as ROI improves, and what separates organizations that scale AI successfully from those that stall.

The biggest AI challenges IT teams face in 2026

When IT professionals were asked to name their top barriers to AI adoption in 2026, the answers were revealing.

1. Data privacy and security risks (57%)
2. High implementation costs (50%)
3. Inaccurate or biased AI outputs (34%)
4. Governance and regulatory compliance (33%)
5. Resistance to change and cultural barriers (31%)
6. Lack of skilled talent (30%)
7. Unclear ROI (25%)

What stands out is not just which challenges ranked highest, but which ones fell. Governance and compliance, which topped the list in 2025, dropped to fourth place in 2026. ROI concerns also declined sharply.

This ‘shift’, if we can call it that, tells a clear story: AI skepticism is giving way to AI pragmatism. Organizations now believe that AI can deliver value, but they are far more cautious about how AI is deployed and where it is allowed to operate autonomously.

1. Data privacy and security remain the top AI challenge

Despite growing trust in AI, data privacy and security risks remain the top concern for 57% of IT professionals. This concern reflects a widening gap between how quickly employees are adopting AI tools and how slowly enterprise controls are evolving to govern them.

The 2026 data underscores this imbalance. 82% of end users say they use ChatGPT or similar AI tools that were not procured by their IT teams, and among those users, nearly 80% use these tools at least weekly. This surge in “Shadow AI” expands the organization’s attack surface and introduces new risks around data leakage, inconsistent access controls, and regulatory exposure.

As Henrique Teixeira, SVP of Strategy at Saviynt puts it, there is a “plumbing problem” when it comes to shadow IT.

If you look at the average organization today, they have hundreds, thousands of apps they need to bring into their identity programs. Surveys show that, on average, only 11% of those apps get onboarded into an IAM program. There is a plumbing issue here, it’s almost like we’re running massive corporations without running water. - Henrique Teixeira

2. High AI implementation costs: real constraint or perceived barrier?

At first glance, cost appears to be a stubborn obstacle. Half of IT professionals (50%) cite high implementation costs as a key barrier to AI adoption, reflecting the scrutiny AI initiatives face as they move beyond pilots and into scaled deployment.

However, the challenge is not simply the size of the investment. It is the difficulty of justifying that investment with clear, defensible ROI, especially when AI value accrues unevenly across teams and over time.

While 67% of IT professionals describe the ROI from their AI initiatives as positive, only 3% report negative ROI. This suggests that AI is delivering value, but that value is not always easy to quantify in ways that align with traditional cost-justification models.

In practice, “cost” often becomes shorthand for deeper structural issues: integration complexity across legacy systems, ongoing governance and compliance overhead, and the effort required to redesign workflows around AI rather than simply layering new tools onto existing processes.

For many organizations, then, the real challenge is not whether AI delivers value, but how quickly and credibly that value can be demonstrated at scale to leadership, finance, and the business.

3. Inaccurate and biased AI outputs continue to undermine trust

Concerns around AI accuracy have not disappeared. 34% of IT professionals list inaccurate or biased outputs as a top adoption barrier.

This concern reflects a growing realization inside IT: model performance alone does not guarantee reliable outcomes. In enterprise environments, AI systems are only as effective as the quality of the data they access, the freshness of organizational knowledge, and the clarity of the workflows they support.

Without strong context, accurate knowledge bases, well-defined processes, and clear escalation paths, AI systems can amplify confusion rather than reduce it. This is especially problematic in enterprise support scenarios, where incorrect responses can erode employee trust quickly.

As a result, many IT teams are shifting focus away from raw model capability and toward context-aware AI, where outputs are grounded in enterprise-approved knowledge and continuously reviewed.

4. Organizations are drawing clear boundaries around AI autonomy

It is not surprising that IT leaders are drawing firm boundaries around AI autonomy. While trust in AI is rising, decision-making authority remains carefully constrained:

Only 16% of organizations fully trust AI to make and execute operational decisions and 36% use AI, but require humans to make the final decisions

Organizations are willing to let AI assist, recommend, and automate, but only within governance structures that IT can stand behind. Companies, in fact, remain selective about where AI is allowed to operate.

In 2026, the top “AI no-go areas” identified by IT professionals were:

• Ethical and legal decision-making (43%)
• High-level strategic planning (37%)
• Customer relationship management (34%)

This must not be mistaken for fear or resistance. Instead, it reflects a growing consensus that AI is most effective when augmenting human judgment (and not replacing it).

Establishing clear AI governance frameworks and addressing ethical and data governance concerns early on helps overcome this barrier of trusting AI systems.

The TRUST framework developed by Atomicwork provides a structured approach. It ensures that AI systems are transparent, responsible, user-centric, secure, and traceable and that they meet the highest enterprise security, compliance, and safety standards.

The beauty of AI is it forces us to think of security from the beginning, and not as an afterthought. When security and IT are aligned, we innovate faster, build trust with our customers, and create products that are not only competitive but resilient. Aysha Khan, CIO and CISO of Treasure Data

5. AI skills are still falling short

‍The talent gap in AI implementation impacts both the initial implementation phase and ongoing management of AI systems.

The skills shortage manifests in various ways, from difficulty in identifying use cases to challenges in maintaining and optimizing AI systems. This barrier highlights the critical need for focused training programs and strategic talent development initiatives.

Since AI teams haven’t been a thing for too long, finding the right talent can also take time. AI is a broad term that encapsulates Machine Learning, GenAI, Agentic AI, Computer Vision, and more. Prioritize hiring and training for the bespoke skills you need.

There’s a lot of focus on AI automating jobs, but that’s not always negative. It’s a great opportunity to upskill your staff in areas like prompt engineering, data governance, AI ethics, and knowledge management — skills that will help them thrive even as AI reshapes certain roles. Roy Atkinson, CEO of Clifton Butterfield LLC

Also, have a “build vs buy” discussion early on. You might not need to spend a ton of time and money on building AI-based systems in-house unless you’re hoping that the AI-based solution gives you a unique competitive advantage.

6. Organizational AI readiness is becoming a cultural challenge

By 2026, most organizations are no longer debating whether to use AI, they are grappling with whether their people, processes, and operating models are prepared to scale it responsibly.

Organizations that treat AI purely as a tool deployment risk creating confusion, resistance, or silent workarounds. Those that invest in clear communication, role clarity, training, and change management are far more likely to see AI improve employee experience rather than undermine it.

So what really drives AI readiness? Three strong correlations stood out:

1. Higher AI maturity correlates with stronger ROI, greater trust, and higher readiness to scale
2. Trust in AI is closely linked to measurable impact
3. AI initiatives led by IT leadership show the highest success rates

Companies where AI adoption is driven by IT leadership, rather than fragmented experimentation, are far more likely to build trust and achieve sustained outcomes. In fact, 54% of AI investments in 2026 were initiated by IT leadership, up sharply from previous years.

This pattern mirrors broader enterprise trends. Deloitte’s research on enterprise AI adoption shows that while most companies plan to increase AI investment, organizational barriers, particularly governance, integration, and workforce readiness, continue to slow meaningful scale.

This reinforces a key lesson for 2026: scaling AI is a cultural problem, not a tooling problem. The IT version of change management addresses the technical side of the planning, rollout, and, if required, rollback process. Another important part of rolling out an org-wide change is the human aspect.

A positive attitude is contagious, and it's amazing what a team can achieve with the right mindset. Slowly but surely, involve everyone from the service desk to leadership to feel connected to the bigger picture — ask them for inputs to show how their work contributes to the bigger picture. Mark Gill, Senior Director of IT at Zuora

What it takes to overcome AI challenges in 2026

The data points to a clear conclusion: the organizations that scale AI successfully are the ones getting the fundamentals right.

That typically means designing governance into AI initiatives from day one, setting clear boundaries around autonomy, especially for sensitive or high-stakes decisions, and grounding AI systems in trusted, enterprise-grade context. It also means treating AI as a leadership-led capability, aligned tightly to business outcomes, with continuous human oversight as AI expands into more workflows.

Take Chad Ghosn, CIO and CTO at AMMEX Corp, for example. As the IT leader of his organization, Chad was looking for a way to increase self-service adoption with AI. Having clearly identified their challenge and laid down goals, they partnered with Atomicwork to improve their ticket deflection from 20% to 65% leveraging AI.

The next phase of AI in IT will be defined not by how quickly organizations deploy AI, but by how responsibly they scale it.