HCLTech’s recently released 2026 Enterprise AI Research Report, The AI Impact Imperatives, 2026, shows how quickly enterprise AI expectations are changing.

Among the clearest signals is that 62% of respondents say business leaders are frustrated by the perceived sluggishness of IT in delivering high-profile AI projects. The same research also shows that 78% expect all competitors to be using AI for mission-critical activity this year and that 83% believe CEOs and boards underestimate the existential risk of underinvesting in AI.

Together, those findings suggest that AI is no longer sitting at the edge of the enterprise. It is moving into the core, and the pressure is now coming as much from the business as from technology functions.

In a recent interview on the report’s findings, Alan Flower, Executive Vice President, CTO and Global Head, AI & Cloud Native Labs at HCLTech, argued that this shift is changing the balance of power inside enterprises. AI is no longer simply an IT modernization theme. It is becoming a business urgency issue, one that is accelerating collaboration, exposing governance tensions and pushing organizations to redesign how work gets done.

The business is now in the driving seat

For Flower, the report’s finding on business frustration points to something deeper than delivery dissatisfaction.

“Business leaders have become very confident about the ability of AI to bring real impact to their business,” he said.

In his view, that confidence is now visible both in HCLTech’s AI Labs and across the broader market, where leaders are leaning in “with not just confidence, but enthusiasm” about AI’s potential to reshape outcomes.

That matters because it changes the nature of demand. AI is no longer being advanced only as a technology-led agenda. Business leaders increasingly see it as central to growth, competitiveness and operating-model change.

Flower suggested that this is one reason expectations are becoming more intense.

“Maybe business leaders are just pushing too hard right now, and IT is struggling to accommodate the rate of change that business leaders now believe is possible,” he said.

He also linked that urgency to competitive pressure. The expectation that most competitors will be AI-enabled in the near term helps explain why business leaders are pushing so hard. They can see both the upside for their own organizations and the risk of being left behind if rivals move faster.

Why business speed and IT caution are colliding

The business is moving faster because it sees AI as central to future competitiveness. IT is more cautious because it is carrying the burden of risk, governance and safe implementation.

That tension is visible in another of the report’s findings: 68% IT leaders believe the business is often advancing too quickly, without fully considering governance, safety and control. For Flower, this does not suggest that one side is right and the other wrong. It shows that both are responding to real pressures from different parts of the enterprise.

The answer, in his view, is not to slow the business down or sideline IT. It is to create tighter partnership between them. “You need IT and tech collaborating with the business,” he said. That collaboration matters because AI is now moving directly into core business functions, where organizations expect gains in operational efficiency, workforce productivity and process automation. The business imperative may have to lead, but it must do so with IT as a scaling partner.

What separates productive alignment from stalled decision-making

Organizations that are further along in their AI journeys have usually moved beyond isolated experimentation and are focusing on core value streams that span the whole enterprise. He pointed to examples such as order to cash, corporate finance and supply chain, where benefits can only be realized if multiple functions work together. In those cases, collaboration is not optional. It is built into the nature of the transformation itself.

By contrast, organizations that struggle with alignment are often still “nibbling at the edges.” They are testing individual use cases or innovating in isolation rather than treating AI as a strategic, cross-enterprise initiative. That tends to produce slower decision-making, fragmented priorities and more limited results.

Flower also emphasized the importance of executive sponsorship. In the organizations making the most progress, AI transformation is often being driven from the top, with a chief executive or another senior leader taking visible responsibility for the journey. That level of sponsorship changes the conditions for collaboration because it makes clear that AI is not an optional experiment. It is a corporate priority.

Operational efficiency and productivity are now moving together

The report highlights operational efficiency (49%) and employee productivity (46%) as two of the biggest drivers of AI adoption, and Flower argued that they should be understood together rather than separately.

For him, operational efficiency is most meaningful when organizations apply AI to end-to-end value streams rather than individual tasks. “The majority of our global activity is from clients who are applying Agentic AI to those core value streams at the heart of their organization right now,” he said. That is where the impact becomes more predictable and more material.

At the same time, he sees employee productivity as a critical part of the same story. Not everything will become fully autonomous, and a large part of AI’s value comes from allowing people to delegate the more repetitive, less interesting parts of their work. Flower argued that this has implications beyond efficiency. It can also improve job satisfaction by giving employees more time to focus on work they are better at and more motivated by.

Employees are no longer waiting to be told

One of the most interesting points from the discussion was Flower’s observation that employee-led adoption is becoming a major part of the AI story.

He described the emergence of what he called a “two-tier workforce,” where a more curious, creative and growth-minded group of employees is already adopting AI tools on their own to improve the way they work.

In his view, this is becoming one of the clearest signs of maturity. People are no longer just waiting for formal corporate rollouts. They are using these tools directly to improve productivity, automate burdensome tasks and even support higher-value work such as proposal writing.

That is a significant shift. It suggests that AI adoption is no longer only top-down. In some organizations, it is also bottom-up, driven by employees who can already see how these tools improve the quality and speed of their own work. For leaders, that creates both an opportunity and a challenge: encouraging this energy while ensuring it is supported by the right guardrails and governance.

The next model is business-led and enterprise-enabled

Asked what a more mature AI model looks like, Flower pointed again to executive leadership and whole-company commitment. Across the large enterprises HCLTech supports, the strongest signal of maturity is visible senior sponsorship and a clear view of how AI will create business value, whether through new products, revenue models or operating efficiencies.

That is one of the bigger implications of the research. The most effective model is not one where the business drives AI alone or where IT controls it in isolation. It is one where the business shapes outcomes and urgency, while IT and technology functions provide the foundations, controls and scalability that make those outcomes sustainable.

Flower highlighted that AI has moved past the stage where enterprises can afford to treat it as a side project. The business urgency is real. The competitive pressure is real. And the organizations that move ahead will be the ones that turn that urgency into coordinated action rather than fragmented experimentation.