Genpact CEO BK Kalra: The Time for AI Experiments Is Over
BK Kalra says scaling AI now depends on fixing the data, process, talent and governance gaps pilots can hide.
Generative AI has been a topic of experimentation for many enterprise leaders over the past few years. BK Kalra, the CEO of Genpact, believes that phase has come to an end. Speaking at Newsweek's AI Impact Forum webinar on September 17, Kalra stated that the focus should now shift to scaling use cases. This transition from experiments to scale brings new challenges, such as ensuring data usability, consistent processes across business units, employee fluency with the tools, and accountability for the actions of intelligent agents.
Kalra argued that the success of AI should be measured against business performance, specifically in growing faster, operating more efficiently, or converting more transactions to cash. Research conducted by Genpact and HFS Research surveyed 2,002 enterprise executives across 16 industries and found that only 6% of organizations were proven debt remediators—meaning they had established and measured initiatives to address these issues.
Kalra warned that technology debt, data debt, process debt, and talent debt can hinder the effective use of AI. These debts can lead to complex issues due to the varied rules, exceptions, and practices across different regions or business units. He emphasized that AI agents depend on usable data and specific company knowledge to function correctly.
Kalra highlighted the importance of integrating IT and governance early in the deployment process. He recommended involving the CIO or CDO partner from the beginning to build confidence and address security concerns and responsible AI frameworks. As AI agents take on more execution tasks, Kalra stressed the need for workforce readiness and training, as workers must understand the capabilities of AI tools before participating in redesigning work processes.
He categorized the skills needed into AI builders—combining technical expertise with business domain knowledge—and AI practitioners, who need deeper expertise in specific areas but must also develop AI and data command. Kalra concluded that while the aspirations for AI are high, the readiness for its implementation is low.
Written by urgent.news from Newsweek's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
