Enterprise readiness for AI remains a growing concern, with CTO confidence in scaling the technology falling for the third year in a row, according to a report by a global digital engineering and consulting company.
In its latest “What CTOs Think” report, which is based on insights from 500 CTOs, Akkodis found that CTO confidence in their organizations’ ability to implement and scale AI has slipped to 48% in 2026, down from 62% in 2025 and 82% in 2024.
“Many organizations have moved past the question of whether they can access AI,” said Akkodis CEO Jo Debecker. “The biggest challenge they now face is whether they can make AI work inside the complexity of the enterprise — across legacy systems, fragmented data, risk controls, governance processes, and human workflows.”
“The ability to scale AI in a meaningful way matters because that’s how enterprises can see the technology’s value,” he told TechNewsWorld.
“Pilots can prove what is possible, but scalability is what turns AI into better decisions, faster innovation, and measurable business impact,” he continued. “To get there, organizations need more than technology. They need workforce transformation, clear governance, and trust from the people expected to use AI every day.”
“Organizations have spent two years running proofs of concept,” added Eric Hulse, director of research at Command Zero, a cyber investigation automation company in Austin, Texas.
“The ones stuck in pilot mode are stacking up costs without capturing value,” he told TechNewsWorld. “The pressure to scale is real. But the Akkodis data shows confidence in the ability to scale fell from 82% to 48% in three years. That makes sense. The more CTOs grapple with what scaling actually takes, the clearer it gets that most organizations aren’t built for it.”
Stuck on Scaling
Scaling is where many AI programs are getting stuck, observed Ryan McCurdy, vice president of marketing at Liquibase, a database-change automation company in Austin, Texas.
“Companies can get access to capable models, run demos, and show productivity gains. The harder part is turning that into work the enterprise can trust every day,” he told TechNewsWorld.
When agentic AI is added to the mix, it raises the stakes, he continued. “It is not just answering questions. It can write code, generate schema changes, update pipelines, and trigger work across the business,” he explained. “That requires a different operating model. Teams need to know what agents can do, where humans stay involved, and how AI-driven changes are reviewed, traced, and controlled.”
“A lot of organizations have not figured that out yet,” he said. “So they either keep AI boxed into experiments, or they move too fast and create risk in production. Neither path scales.”
“The companies that get this right will not just buy more AI tools,” he added. “They will build the structure around them, such as trusted data, governed workflows, and proof of control. That is how AI moves from interesting experiments to something the enterprise can actually run.”
AI Readiness Gaps Persist
The report explained that as organizations move beyond pilot programs, execution complexity increases across leadership alignment, governance, and workforce trust. It found that fewer than half the CTOs (44%) believe leadership teams have sufficient AI understanding, and only 36% express satisfaction with workforce trust levels.
In addition, the CTOs said that AI progress was being limited by barriers, such as a lack of in-house technology skills (32%), uncertainty around return on investment (31%), and a lack of urgency at the business level (27%).
“Many organizations are adopting AI because they feel pressure to do so,” noted John Strand, owner of Black Hills Information Security, a penetration testing company in Sturgis, S.D.
“They’re constantly seeing headlines, LinkedIn posts and social media content claiming AI is changing everything, and they don’t want to be left behind,” he told TechNewsWorld. “The danger is that AI can become a solution looking for a problem.”
“AI absolutely has the potential to create value, but organizations need to be strategic about identifying specific pain points and business challenges it can solve instead of spreading it across the enterprise without a clear purpose,” he said.
Data Problems Undermine AI
Steven Swift, managing director of Suzu Testing, a provider of AI-powered cybersecurity services in Las Vegas, asserted that the technologies used to accomplish business goals don’t matter that much. “What matters is that the business is achieving its objectives,” he told TechNewsWorld. “If a business can shift a bunch of legacy costs to a bunch of AI models, it rarely meaningfully changes business capabilities.”
“AI labs are selling shovels and shouting to everyone who will listen that there’s a gold rush,” he added. “Technically, it doesn’t mean your organization won’t find gold. But most of the money to be made in a gold rush is in selling shovels, not gold.”
The plethora of AI tools available to companies is problematic, acknowledged Bob Brauer, founder and CEO of Interzoid, a San Francisco consultancy and provider of enrichment solutions for enterprise data systems.
“Teams are faced with figuring out how to connect those AI tools to real and correct systems,” he told TechNewsWorld. “The reason this is a pretty big deal is that enterprises typically have data spread across various systems, and different systems maintain different ‘versions of the truth.'”
“Effective AI depends on the quality of the data it is working from, so when teams are faced with messy systems, with issues like old records, duplicate records, or missing fields, the AI then makes bad decisions,” he explained. “Because AI is always fast-moving, those bad decisions scale incredibly fast, and companies don’t typically find data issues until after they’ve scaled enough to have negative impacts.”
“Ultimately, before an AI integration can connect and scale, companies need cleaner, more consistent data so that the AI is working from information the business can trust,” he added.
Innovation Overtakes Efficiency
The Akkodis report also revealed a fundamental shift in how organizations define the value of digital transformation. For the first time, it noted, CTOs cite innovation, not efficiency, as the primary driver of digital investment, signaling a move from cost-focused optimization toward growth, differentiation, and new business models.
As AI capabilities mature, the marginal gains from efficiency are diminishing, increasing the importance of innovation as a source of competitive advantage, it explained. While the shift is global, it continued, priorities vary by industry — from workforce development in aerospace to innovation acceleration in life sciences to resilience in energy — underscoring the need for sector-specific approaches to scaling AI.
“This shift shows that digital transformation is becoming a growth agenda, not just a cost agenda,” Debecker said.
“Enterprises are moving beyond using technology to optimize existing processes and are beginning to use it to create new products, services, workflows, and business models,” he added. “This change is significant because it changes how leaders measure success, not only by how much cost they remove, but by how quickly they can turn technology into new value for customers, employees, and the business.”
“In practice, trust is built through transparency, clear governance, and defined decision rights — ensuring employees understand how AI is used, where accountability sits, and how outcomes are validated.”
Growth Becomes the Goal
This shift is a fundamental evolution, declared Josh Stanaland, a partner and CTO of Shark AI Solutions, a product development, AI, and client account management company in St. Petersburg, Fla.
“Previously, digital transformation focused on cost-cutting and doing the same work faster or cheaper,” he told TechNewsWorld. “Now, with AI maturing, the top driver is innovation — creating new value, products, business models, and growth opportunities.”
“It is significant because it signals a move from defensive optimization to offensive differentiation,” he said. “Companies that treat AI as an innovation engine will pull ahead, while those focused only on efficiency risk falling behind.”
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