According to TCS's CEO and MD, the emergence of GenAI signifies a fundamental change
As generative artificial intelligence (GenAI) rapidly reshapes how organisations think and operate, a wide gap has emerged between its promise and actual performance, Tata Consultancy Services (TCS) Chief Executive Officer and Managing Director K Krithivasan said in an article published on the World Economic Forum (WEF) website.
While GenAI represents a fundamental shift in how enterprises make decisions and take action, nearly 95% of enterprise AI pilot projects have failed to deliver measurable business value, Krithivasan noted. He said the shortfall highlights the need for companies to rethink not only which decisions are made using AI, but also how decision-making itself is designed.
“To move beyond the promise of AI and turn potential into performance, leaders must rethink how humans and AI collaborate,” he wrote, adding that new frameworks are required to ensure continuous, informed interaction between people and intelligent systems, while keeping humans firmly in the loop.
Looking ahead to 2026, Krithivasan said a new form of organisational intelligence is beginning to emerge, driven by combinations of human judgment and machine intelligence that shape how choices are developed, evaluated and discussed. To unlock AI’s full value, he emphasised the need for ecosystem-wide collaboration, deep domain expertise, robust governance and responsible scaling through co-innovation models. AI, he said, should ultimately improve organisational decision-making by presenting better, data-backed choices.
Krithivasan outlined five core principles that organisations must adopt to bridge the gap between AI experimentation and real-world impact.
First, he said trust must be built gradually. Managing risk and building confidence requires incremental implementation, allowing stakeholders to develop trust in how decision environments are framed, not just in outcomes. An iterative approach, he noted, enables continuous learning and smoother adoption of new decision architectures.
Second, visibility is critical. Organisations need to clearly track how high-stakes decisions are made and maintain a holistic view of both internal operations and the external environment. Without accurate and comprehensive data, AI-driven decision frameworks risk producing flawed or misleading recommendations.
Third, Krithivasan stressed the importance of open-mindedness. AI-driven systems often challenge human intuition rather than reinforce it. Companies must foster cultures where employees are comfortable questioning assumptions and learning from mistakes as part of the transition to more intelligent decision systems.
Fourth, decision-making hierarchies must evolve. Legacy structures that limit decision authority to a small group of experts can undermine the value of AI insights. To fully benefit from AI agents, organisations must remove such barriers so that high-quality recommendations are not dismissed due to rigid hierarchies.
Finally, workflows need to change. Cultural shifts must be supported by flexible processes that allow AI-generated insights to be implemented effectively. This, Krithivasan said, is less about improving AI literacy and more about building internal systems that make human-machine collaboration visible, understandable and actionable.
He concluded that realising AI’s potential will depend on organisations’ ability to redesign decision-making frameworks, align culture and governance, and integrate AI into everyday workflows in a responsible and scalable manner.
