<p>Bengaluru: While AI adoption is now widespread across IT operations, software engineering and business functions, nearly 43% of major AI initiatives are expected to fail, as the risk is not driven by lack of experimentation or access to tools, but by the difficulty of translating ambition into consistent, enterprise‑wide outcomes, IT services firm <a href="https://www.deccanherald.com/search?q=HCLTech">HCLTech</a> warned.</p><p>The technology company in its findings from the latest Enterprise AI Market Report, The AI Impact Imperatives, 2026, highlighted a growing execution gap as enterprises race to scale AI while facing mounting pressure to deliver results within increasingly compressed timeframes. </p><p>The research is based on a global survey of 467 senior executives responsible for AI investments across enterprises with more than $1 billion in annual revenue.</p><p>Expectations around returns are now being tightened. Nearly half of enterprise leaders expect measurable value from AI investments within 18 months, leaving little margin for error as organisations balance rapid deployment with the structural changes AI demands.</p>.Leaders plan to invest an average of $186 million in AI.<p>“AI has moved from being a technology initiative to becoming an enterprise operating reality," said Vijay Guntur, CTO and Head of Ecosystems at HCLTech. "What leaders are grappling with now is not whether AI can deliver value, but how organisations adapt their structures, decision rights and risk tolerance to keep pace with it. 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,” Guntur added.</p><p>The research also pointed to an evolution in how enterprises are applying AI, with growing interest in Agentic and Physical AI use cases that extend beyond digital workflows into real‑world environments such as manufacturing, engineering and operations. While adoption remains early, these models raise new questions around accountability, reliability and oversight, further increasing the leadership burden associated with scaling AI responsibly. </p><p>The report also suggested that many organisations are underestimating the degree of cross‑functional coordination and decision‑making clarity required to succeed.</p>
<p>Bengaluru: While AI adoption is now widespread across IT operations, software engineering and business functions, nearly 43% of major AI initiatives are expected to fail, as the risk is not driven by lack of experimentation or access to tools, but by the difficulty of translating ambition into consistent, enterprise‑wide outcomes, IT services firm <a href="https://www.deccanherald.com/search?q=HCLTech">HCLTech</a> warned.</p><p>The technology company in its findings from the latest Enterprise AI Market Report, The AI Impact Imperatives, 2026, highlighted a growing execution gap as enterprises race to scale AI while facing mounting pressure to deliver results within increasingly compressed timeframes. </p><p>The research is based on a global survey of 467 senior executives responsible for AI investments across enterprises with more than $1 billion in annual revenue.</p><p>Expectations around returns are now being tightened. Nearly half of enterprise leaders expect measurable value from AI investments within 18 months, leaving little margin for error as organisations balance rapid deployment with the structural changes AI demands.</p>.Leaders plan to invest an average of $186 million in AI.<p>“AI has moved from being a technology initiative to becoming an enterprise operating reality," said Vijay Guntur, CTO and Head of Ecosystems at HCLTech. "What leaders are grappling with now is not whether AI can deliver value, but how organisations adapt their structures, decision rights and risk tolerance to keep pace with it. 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,” Guntur added.</p><p>The research also pointed to an evolution in how enterprises are applying AI, with growing interest in Agentic and Physical AI use cases that extend beyond digital workflows into real‑world environments such as manufacturing, engineering and operations. While adoption remains early, these models raise new questions around accountability, reliability and oversight, further increasing the leadership burden associated with scaling AI responsibly. </p><p>The report also suggested that many organisations are underestimating the degree of cross‑functional coordination and decision‑making clarity required to succeed.</p>