A new global study by Tata Consultancy Services (TCS) reveals that 84% of executives view productivity improvement as the leading benefit of AI investments. However, only 10% of global enterprises permit AI agents to make decisions autonomously. The research underscores a gap between current AI value realization and enterprise readiness for greater AI autonomy, with security, risk, and governance cited as primary concerns.
TCS Enterprise AI Study Highlights Productivity Gains
A comprehensive global study conducted by Tata Consultancy Services (TCS) indicates that a significant majority of businesses, approximately 84% of executives surveyed, identify productivity improvement as the primary value derived from their artificial intelligence (AI) investments. This finding comes from a survey across 25 countries, where security, risk, and governance were highlighted as crucial factors influencing AI deployment.
AI Autonomy and Governance Concerns
Despite the strong emphasis on productivity benefits, the study reveals a cautious approach to AI autonomy. Only about 10% of global enterprises allow AI agents to execute decisions without direct human oversight across multiple functions. This suggests a prevailing concern regarding the responsible and secure integration of AI into core business processes. Furthermore, only 9% of executives reported their AI agents being capable of securely orchestrating workflows across enterprise applications, pointing to a gap in operational readiness.
Human+AI Operating Model Evolution
The findings suggest an ongoing evolution towards a Human+AI operating model. As enterprises increasingly leverage AI for greater responsibility and autonomy, ensuring robust security, governance, and control mechanisms will be paramount to deriving maximum value. This includes defining clear boundaries for human oversight and determining where AI can be granted greater decision-making authority.
Key Findings from the Study
Additional key insights from the TCS Enterprise AI Study, titled “Intelligence that is Real,” include:
- Security as a Defining Factor: Security, risk, and governance considerations are shaping the next phase of enterprise AI development and deployment, taking precedence over cost and speed.
- Platform Independence: Ninety percent of respondents consider model independence critical for AI investment decisions, while 34% highlight AI sovereignty requirements (data location, model choice) as a high organizational risk.
- Workforce Impact: The proportion of leaders finding it too early to determine AI’s effect on jobs has risen from 4% in 2024 to 19% in 2026.
- AI Economics: 39% of enterprises are still assessing whether AI initiatives provide sufficient value relative to their costs.
- Enterprise Knowledge Fragmentation: 44% of respondents noted that vital business knowledge remains embedded within employees or scattered documentation, hindering AI’s ability to access context.
- Open-Source AI Adoption: 46% of companies are deploying open-source AI for specific use cases.
The study surveyed 1,333 senior business and technology leaders across 25 countries and 12 industries.
Source: BSE
