Top AI Challenges for IT Leaders: Skills, Data, and ROI

Even more than CEOs and finance leaders, IT leaders see artificial intelligence (AI) improving productivity, revenue, and cost control. What IT leaders also see, though, is rising AI adoption putting pressure on their teams, with 86% reporting the strain is moderate or higher.

Another sign that IT leaders are prioritizing keeping up with the technology: 59% tell us that if they had one extra hour each day, they’d spend it learning about AI. That’s one of many insights about the state of IT and businesses found in our recent Insights for IT leaders, which is based on a global survey of executives and examines current challenges along with recommendations for addressing them.

What Are the Top AI Challenges IT Leaders Face in 2026?

The numbers above hint at the top AI deployment challenge for IT teams: a shortage of development skills, as 47% of leaders report. Concerns around data are right there with the skills shortage, though, as 47% worry about data security and 42% cite poor internal data. Not far behind are difficulty measuring ROI (39%) and change management (38%), closing out the top five AI deployment challenges for IT teams.

This blog examines those findings and how IT leaders can:

• Pursue ways to better assess ROI.

• Strengthen AI data quality, governance, and skills.

• Provide their teams with the resources to ensure AI success.

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How Can IT Boost AI Outcomes? Better Data.

AI models need vast amounts of consistent, clean, and complete data to be most effective, which means organizations need the be confident in the accuracy and completeness of their data to ensure successful AI adoption. But many still have a ways to go, especially when it comes to finance data: IT leaders point to skills deficits among finance colleagues in AI/machine learning knowledge (56%), data literacy and analytics (50%), and data governance and quality (45%). However, just over 7 in 10 IT leaders—71%­—say finance and IT share ownership and governance of finance data, which provides common ground for collaboration on improving both areas while supporting upskilling efforts.

How IT Can Help Drive Better Data Quality and Governance at Their Organizations

As mentioned, data concerns sit at the top of AI deployment challenges IT leaders are encountering. That is also true when survey participants were asked about key pain points of AI adoption:

• Data engineering and quality (60%)

• Cybersecurity (53%)

• Skills and staffing (52%)

• Integration with legacy systems (51%)

• Proving ROI and business value (48%)

To reduce the pain, organizations should:

• Select a single owner or dedicated team accountable for ensuring data quality for an AI system or project, even if ownership is shared across departments.

• Develop structured and regular cross-functional meetings to ensure alignment on data governance and quality.

• Collaborate with finance to close skills gaps, particularly in areas of weak data quality or observable competency shortfalls (and make AI skills growth a performance management metric while you’re at it).

How Do IT Leaders Assess AI ROI and What Factors Have the Biggest Impact?

Compared with finance leaders and CEOs, IT is less likely to call evaluating ROI a challenge: 39% vs. about half of top executives. IT leaders, tasked with deploying the technology and having a more holistic view of the tech stack across departments, have worthy opinions on what can raise or lower the likelihood of ROI.

So, what measures of success do IT leaders look at when evaluating AI ROI? Asked to identify their top three, 50% chose productivity and time savings while 51% named accuracy and quality improvements.

What Factors Improve AI ROI?

Cross-functional collaboration leads the list of success factors from IT leaders, illustrating the need to work with finance and other stakeholders to develop an AI delivery strategy and align on goals and KPIs.

Other contributors to increased AI ROI include:

• A strong data foundation

• High adoption by teams

• Less need for retraining or rework

• Quick productivity gains

• Faster-than-expected implementation

What Can Decrease an Organization’s Returns from AI?

Factors that lower AI ROI include:

• Benefits slow to appear

• Poor data quality or integration

• Skills or talent gaps

• Overly optimistic expectations

• Lack of adoption by teams

• Use case wasn’t appropriate

• Higher-than-expected costs

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Conclusion: Empower IT Teams with Resources and Strategy for Successful AI Implementation

Having the right blend of data, finance, and technological skills is clearly valuable for driving AI adoption and ROI. In addition, AI initiatives’ likelihood of success will increase if businesses look beyond the upfront cost of solutions, set realistic expectations, and take a measured approach to implementation.

Organizations should dedicate budget and resources to support IT teams during AI deployments, including the data cleaning and preparation phases, in order to keep projects from becoming bottlenecked. If resources are tight, consider starting with business areas or functions where strong data foundations are already in place and implementing AI in phases instead of all-at-once rollouts. These are just a few examples of a strategic, systematic approach that enables IT teams to lead the charge in driving AI outcomes and ROI.

FAQs

What are the biggest AI challenges facing IT leaders in 2026?

IT leaders cite skills shortages and data security concerns at 47% each, followed by poor internal data quality, ROI concerns, and change management challenges.

Why is data quality so critical to AI success?

AI systems depend on clean, complete, and consistent data. IT leaders say stronger data governance, clear ownership, and better collaboration between finance and IT are essential for improving AI outcomes.

How do IT leaders evaluate AI ROI?

The top measures are productivity and time savings, along with improvements in accuracy and quality. Leaders also value quick implementation, strong adoption, and reduced rework.

What can organizations do to improve AI adoption and returns?

Organizations should invest in IT resources, strengthen data foundations, close skills gaps, and roll out AI projects in phases. Cross-functional collaboration and realistic expectations also help increase ROI and reduce project risks.