Artificial intelligence adoption is accelerating across global enterprises, with businesses rapidly integrating AI into software development, IT operations and core business functions. But despite the widespread enthusiasm, a new HCLTech report suggests that scaling AI successfully may be far more difficult than deploying it.

According to HCLTech’s latest Enterprise AI Market Report: The AI Impact Imperatives, 2026, nearly 43% of major enterprise AI initiatives are expected to fail, raising concerns around whether companies are moving faster than their organizations are prepared to handle.

The findings point to a growing disconnect between AI ambition and execution, even as companies increase investments and race to capture business value.

AI adoption vs challenges

The report is based on a global survey of 467 senior executives responsible for AI investments across enterprises generating more than $1 billion in annual revenue.

It found that AI experimentation and adoption are no longer the problem. Organizations now have access to AI tools, technologies and deployment frameworks. However, many are struggling to convert early success into sustainable, enterprise-wide outcomes.

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Instead of technology limitations, the biggest challenge appears to be execution.

According to the study, organizations are increasingly facing structural constraints across applications, operating systems and data environments that were originally designed for traditional technology ecosystems rather than autonomous AI systems.

As AI initiatives move deeper into business operations, failures are also becoming more visible and potentially more expensive.

Pressure to deliver results

Another major concern highlighted by the report is the shrinking timeline for returns.

Nearly half of enterprise leaders expect measurable value from AI investments within 18 months, leaving little room for implementation delays or experimentation cycles.

This compressed timeline is creating pressure on leadership teams to move quickly while simultaneously redesigning internal processes and workflows around AI adoption.

The report suggests that the tension between speed and preparedness is becoming one of the biggest challenges in enterprise AI strategies.

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Leadership and alignment

According to HCLTech, many enterprises may be underestimating the level of coordination required to successfully scale AI.

The report warned that AI programs launched without sufficient alignment between business teams and technology leadership are significantly more likely to stall or underperform.

Rather than being purely technology projects, AI deployments are increasingly becoming organisation-wide transformation initiatives involving governance structures, decision-making processes and accountability systems.

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Change management

One of the strongest findings from the report is that change management has become a critical determinant of AI success, yet it remains among the most underfunded areas of enterprise AI programs.

The study found that many organizations are introducing AI into workflows without adequately preparing employees expected to work alongside these systems.

Vijay Guntur, Chief Technology Officer and Head of Ecosystems at HCLTech, said the challenge for leaders has evolved beyond proving AI’s value.

“AI has moved from being a technology initiative to becoming an enterprise operating reality,” he said.

The report concludes that as AI becomes embedded into critical business functions, success may depend less on adoption rates and more on whether enterprises can align leadership, people and execution at scale.

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