Enterprise Transformation with Generative AI

Enterprise leaders have extensively tested generative AI capabilities over recent years. Balkrishan “BK” Kalra, president and CEO of Genpact, asserts that this experimental phase is concluding. Time of proof of concepts is gone. Time of experiments is gone, Kalra stated during Newsweek’s webinar, “AI Impact Forum.” He stressed the shift toward scaling use cases.

Scaling AI requires addressing issues not resolved in initial tests. Companies must ensure data usability, consistent processes across business units, and employee fluency with AI tools. Accountability remains crucial when AI acts autonomously.

Kalra emphasized that the success of scale use cases should directly impact business performance. Companies need to grow faster, operate leaner, or convert more to cash. Achieving these outcomes becomes challenging as AI integrates into daily operations.

Research from Genpact and HFS Research surveyed 2,002 enterprise executives across 16 industries. Only 6% of organizations qualified as effective at addressing technology debt. Kalra highlighted the importance of overcoming technology, data, process, and talent debt, which hinder AI potential.

Technology debt includes outdated systems and patches; it represents a small part of broader issues. Data debt, process debt, and talent debt further limit AI’s effectiveness. Agents, reliant on specific data and context, expose these limitations.

Kalra pointed out regional variations in global operations. Knowledge to navigate differences often resides in the enterprise and is sometimes undocumented. There is no artificial intelligence, no gains from artificial intelligence, if it is not coupled with process intelligence, Kalra remarked.

Involving IT and governance early is essential. Kalra advised bringing the CIO or CDO into discussions from the start for early buy-in. Understanding and addressing security concerns is also vital, especially as AI handles finance and supply chain tasks with possible financial or regulatory implications.

Agentic operations transition from human-processed and validated work to machine-processed and human-validated processes. People maintain responsibility for exceptions and overall accountability.

Workforce readiness is crucial for implementing AI. Kalra noted the importance of exposing workers to AI tools so they can engage in redesigning work processes. At Genpact, thousands of employees access these tools.

Tinaikar stressed giving employees ample time to develop new skills amidst competing daily demands. It is not a privilege, it is an imperative, Tinaikar stated.

Kalra categorized skills into two groups. AI builders mix technical expertise with business domain knowledge. AI practitioners start with deep expertise in areas like finance or supply chain and gain proficiency in AI and data.

Kalra mentioned tasks and roles will evolve as machines handle more execution work. Tinaikar compared this shift to the smartphone era, which fostered new business opportunities despite initial concerns about displacement.

Kalra agreed, adding that new technology introduces new business models and roles. He referenced Jevons paradox, where efficiency increases lead to greater overall usage and demand.

His warning to workers was clear. Your job will not be taken by AI, but your job can be taken by somebody who knows AI better, Kalra said. Effective scaling requires data, process integrity, security, and prepared employees.

Kalra summarized, Aspirations are really high. Readiness is low.