7 issues impacting AI strategies — and how CIOs should respond

CIOs remain at the forefront of setting the course for AI adoption in their organizations. In fact, 82% of CIO respondents to CIO.com’s 2026 State of the CIO survey are responsible for researching and evaluating AI products, with 78% of IT leaders saying their IT departments are driving AI adoption efforts, with business units aligning their strategies accordingly. As such, CIOs are leading or co-leading AI strategies at the majority of organizations, with many also playing a key role in tackling AI change management . They report encountering numerous factors — from heightened pressure to deliver ROI to challenges with trust in AI outputs — as they formulate and shape those AI strategies. Here’s a look at seven notable issues impacting AI strategies in 2026. 1. Increasing pressure to show ROI for AI investments The era of AI experimentation and pilots is over. Boards and CEOs are making it clear they want to see quantifiable returns from their AI investments . Kyndryl’s 2025 Readiness Report , for example, found that 61% of senior business leaders and decision-makers felt more pressure to prove ROI on their AI investments than they had the prior year. “The era of funding AI is shifting from everything all-in to every project has to have line of sight to some financial value at the end of the day. It’s moving from the experimentation phase to expecting measurable outcomes,” says Jim Piazza , chief AI officer at IT services firm Ensono. As a result, Piazza says companies, both his own as well as those he advises, are more diligent about building business cases that estimate implementation costs, AI run costs, and expected benefits so they’re primed to pursue AI initiatives that will deliver ROI. That strategy seems to be paying off. According to the May 2026 AI Momentum Survey from Dun & Bradstreet , 67% of 10,000 businesses surveyed reported seeing early signs or pockets of ROI, 20% reported multiple projects delivering ROI, and 10% reported strong ROI. That’s a big jump from earlier surveys that found few AI initiatives providing returns. For example, PwC’s 2026 Global CEO Survey , released in January, found that 56% of CEOs saw no significant financial benefit from AI to date, while The GenAI Divide: State of AI in Business 2025 from MIT found that 95% of enterprise generative AI projects failed to show measurable financial returns within six months. 2. The need to harness AI for transformation The No. 1 concern for CEOs this year, according to PwC’s 2026 Global CEO Survey , is whether they’re transforming fast enough to keep pace with technological change, cited by 42% of respondents as their top concern. And 68% of the 1,120-plus C-suite executives surveyed by KPMG for its May 2026 Adaptability Pulse Survey said they feel pressure to accelerate innovation. That in turn is influencing AI strategies. Steve Santana , CIO and head of AI at ETS, the world’s largest private nonprofit educational testing and assessment organization, says his company is “pivoting from working on enterprise efficiencies using AI to figuring out how to deliver assessments,” adding that “AI will enable innovation we couldn’t get to before.” For ETS, that means reimagining how the company delivers its core products, “finding areas to do something you couldn’t do before because it was too big or too daunting,” such as having more interactive tests and assessments at scale, Santana says. And while Santana believes organizations can’t move too slowly, he predicts innovation will trump speed. “The winners and losers in the AI race aren’t always going to be the ones that got there the fastest,” he says, observing that those who move too fast “can drive behaviors that are very dangerous.” He adds, “I’m not advocating for moving slow; I’m advocating moving at pace. It’s better to be measured in your approach.” 3. The black box of AI costs CIOs are struggling to calculate the full cost to run AI for their use cases, with estimates coming in well under what their actual bills will be. Consider the figures from research firm IDC, which found that global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027 . That makes identifying which AI use cases will produce quantifiable value much more challenging, which in turn makes determining a winning AI strategy harder to do. CIOs, however, say they can’t let that stop them from advising their C-suite colleagues on which AI use cases are likely to be winners. “You can’t sit on the sidelines and wait and watch. The general conclusion is you’re going to lose if you do that, so you have to play even though the cost dynamics are not really well understood,” says Mohan Sankararaman , executive vice president and CIO of First Horizon Bank. Sankararaman says he’s devising his AI strategy with that uncertainty in mind. “It’s up to me and my team to figure out how to optimize our use for costs, just like we did with cloud,” he says, noting that part of his strategy is to avoid infrastructure choices that could result in AI vendor lock-in and, thus, getting stuck with that vendor’s bills. “IT has to get the engineering right and not overengineer solutions to make sure the AI strategy we pursue delivers returns,” he adds. Researchers recommend such approaches. In a blog highlighting the IDC research , Jevin Jensen, research vice president for infrastructure and operations at IDC, wrote that “organizations successfully navigating this challenge are ones that effectively share a common trait: they’ve reimagined FinOps as a strategic team, not an after-the-fact accounting exercise. They treat AI economics as a living ecosystem — measurable, visible, and continuously optimized.” 4. Aligning use cases to business strategy There are an overwhelming number of potential use cases, so execs must pick and prioritize those that will help them achieve their strategic goals. That’s easier said than done. Enterprise Strategy Group’s 2025 report on generative AI’s ROI surveyed 1,900 business and IT leaders across nine countries and found that 71% had more potential use cases that they want to pursue than they can possibly fund; 54% said selecting the right use cases based on objective measures like cost, business impact, and the organization’s ability to execute is hard; and 71% acknowledged that selecting the wrong use cases will hurt their company’s market position. Furthermore, 59% of respondents said advocating for the wrong use cases could cost them their job. Longtime CIO adviser Larry Wolff says challenges picking and prioritizing use cases stems in part from boards and CEOs commanding their teams “to do AI.” Such directives, he explains, puts the technology first and business goals second — something CIOs have been trying to avoid for years. “There should not be a technology strategy. There should be a business strategy with a technology component. The same applies to AI,” says Wolff, now CIO of Preferred Travel Group. “We need to talk about business challenges and opportunities first and then talk about how AI can solve for those.” 5. Human readiness to use AI Even as Sankararaman and his executive colleagues build the bank’s AI strategy, he still sees the need to improve the organization’s understanding of the technology . “Everybody has a basic understanding, but AI fluency isn’t where it should be,” he says, noting that a subpar level of fluency “can hamper creativity.” “If the strategy is to become top notch in, say, customer experience, we have to determine how to achieve that. And if you start building the road map but you don’t know what the technology can do, then the strategy will be limited,” he adds. Sankararaman considers running AI boot camps for executives and their direct reports to improve their knowledge of AI and its transformative capabilities. “Not everyone needs to be an AI expert, but we still need to have a level of understanding of, say, what a large language model is and how to apply it and other elementary things like that. The hope is that when we do talk about strategy for business outcomes, everyone will know how to leverage AI,” he explains. According to Jamaal Justice , principal for people consulting at EY, concern about AI fluency is widespread. “One of the biggest challenges that impacts the success of an AI strategy is human readiness,” Justice says. He points to EY research showing “that while 88% of employees use AI at work, only 28% of organizations have positioned employees to achieve transformative business impact from AI. This underscores that the challenge is not access, but adoption and readiness.” Like Sankararaman, Justice acknowledges that it’s OK to have a spectrum of knowledge and use among workers. But success with AI “depends on aligning mindsets, skillsets, and toolsets, by creating the right conditions for both workforce readiness and effective technology use,” he says. “Organizations that integrate human capability with technology and fundamentally rearchitect work using a human-centered and value-oriented approach will unlock value at scale,” he adds. “Those that don’t risk fragmented adoption and limited returns.” EY research confirms as much, finding that productivity gains can fall by more than 40% when AI is deployed on weak talent foundations, including poor learning, culture, and incentives. 6. Data readiness for AI use Data readiness is also lagging at most organizations, further hindering AI ambitions. According to a 2026 report from Cloudera and Harvard Business Review Analytic Services titled Taming the Complexity of AI Data Readiness , 73% of surveyed business leaders said their organization struggles with AI data preparation. The top obstacles are siloed data and difficulty integrating data sources (56%), lack of a clear data strategy (44%), data quality and bias issues (41%), and regulatory constraints on data use (34%). To ensure AI success, “a radical reshaping of the data landscape is needed,” says Steve Prewitt , who as chief data and AI officer at IT services firm Genpact advises clients on AI deployments for their own organizations. That reshaping is more critical today as agentic AI becomes more prevalent, Prewitt observes. Organizations need high-quality well-governed data to enable and trust AI agents to make real-time decisions autonomously. Otherwise, organizations either can’t move forward with deploying agents or, if they do, risk triggering cascading failures. 7. Engendering trust ETS CIO Santana and his colleagues recognize AI’s potential to deliver faulty outputs, whether from problematic data, drift, or other problems. Everyday users recognize that potential, too. That’s why the issue of trust has a significant impact on the nonprofit’s AI strategy. Companies such as ETS that provide critical, high-stakes services know they must earn trust by building AI use cases that can consistently and demonstratively deliver accurate outputs, Santana says. ETS’s strategy is to highlight where AI is making high-stakes decisions and to detail what steps the company must take to ensure that it consistently delivers accurate, trustworthy outputs and that it conforms to established standards and requirements, he says. “You don’t want someone to feel the results may be wrong if you’re using AI to assess a person and their future depends on it,” he notes. “You want to remove any doubts [in such AI use cases], and the strategy should ensure that. The strategy should include all the work needed to have that trust.”