Eighty-four per cent of executives surveyed by Tata Consultancy Services (TCS) rank productivity improvement as the leading source of value from AI investments, but only 10% allow AI agents to execute decisions without human approval across multiple functions or business areas.
TCS’s Enterprise AI Study: Intelligence that is Real highlights a widening gap between the value enterprises already derive from AI-assisted work and their readiness to give AI agents greater autonomy. Just 9% of executives said their organisations have AI agents capable of securely orchestrating workflows across enterprise applications.
Security shapes deployment
Security, risk and governance are now the leading considerations shaping how and where enterprises develop and deploy AI, ahead of cost, speed and ease of deployment. TCS said this emphasis is becoming more consequential as organisations give AI systems greater access to applications, workflows and decision-making processes.
Amit Kapur, chief AI and services transformation officer at TCS, said enterprises are progressing from using AI to assist employees towards allowing it to take more autonomous action with deeper access to enterprise systems.
“Productivity is clearly where enterprises are seeing the greatest value from AI today. What seems to be holding organisations back – and rightly so – are concerns around security, governance, and resilience.” Amit Kapur
Asia’s operational challenge
The report found that 44% of respondents said important business knowledge remains embedded in employees or fragmented documentation, potentially limiting AI’s ability to access consistent enterprise context.
TCS also found that 39% of enterprises are still assessing whether AI initiatives generate sufficient value relative to token and compute costs. This suggests that, alongside governance, Asian business leaders must build clearer measures of AI return on investment as deployments move from pilots to enterprise-wide operations.
Model dependence concerns
Concern about dependence on individual AI providers is growing faster than organisations’ ability to manage it. Ninety per cent of respondents said model independence is an important factor in AI investment decisions, while 34% identified AI sovereignty requirements — including control over data location, model choice and compliance — as a high or very high organisational risk.
Yet only 7% of global respondents reported having organisation-wide provider-switching continuity plans. For enterprises in Asia, where data-residency rules, cross-border data flows and sector-specific compliance requirements can vary significantly, this gap may complicate multi-market AI deployment.
Workforce uncertainty
The report also points to growing uncertainty over AI’s impact on jobs. The proportion of leaders saying it is still too early to determine AI’s effect on employment rose from 4% in 2024 to 19% in 2026.
TCS said the next stage of enterprise AI will depend on a “Human+AI” operating model, in which AI takes on greater responsibility only where appropriate controls are in place. The company positions this as essential to scaling AI responsibly while preserving security, resilience and business value.
