IT services giant HCLTech has warned that nearly 43% of large-scale enterprise AI initiatives are expected to fail as companies struggle to translate aggressive AI ambitions into measurable business outcomes amid shrinking timelines for returns, according to a new report released on May 19.
The report, titled The AI Impact Imperatives, 2026, is based on a survey of 467 senior executives overseeing AI investments at enterprises with annual revenue of more than $1 billion.
It found that while adoption of AI across IT operations, software engineering and business functions has become widespread, organisations are facing mounting pressure to demonstrate measurable value within 18 months.
The findings come at a time when global enterprises are accelerating spending on generative AI and agentic AI systems, pushing technology service providers such as HCLTech, Infosys, Tata Consultancy Services and Wipro to reposition themselves around AI-led transformation deals.
The study highlighted growing strain on legacy application environments, fragmented data ecosystems and operating models that were not built to support autonomous, continuously learning systems. It also pointed to weak coordination between technology teams and business leaders as a major reason why AI programmes fail to move beyond pilot stages.
“AI has moved from being a technology initiative to becoming an enterprise operating reality,” Vijay Guntur, Chief Technology Officer and Head of Ecosystems at HCLTech, said in the report. “The pressure to move fast is real, but without the right investment in people, in helping them understand, trust and work effectively alongside AI, speed can just as easily amplify failure as success.”
The report also underlined rising enterprise interest in agentic AI and physical AI applications spanning manufacturing, engineering and operational environments, marking a shift beyond traditional digital workflow automation. However, it warned that these emerging systems are raising new concerns around accountability, governance and oversight.
A key finding in the report was the lack of investment in change management despite enterprises increasingly embedding AI into core workflows.
Moreover, the study noted that many organisations are deploying AI systems without adequately preparing employees expected to work alongside them, making workforce readiness a primary execution risk.
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