Enterprise leaders have explored generative AI for several years. Balkrishan “BK” Kalra believes experimentation is over. He stated this during Newsweek’s Sept. 17 “AI Impact Forum” webinar, hosted by Dr. Ranjit Tinaikar. The focus shifted from testing concepts to scaling use cases.

From Testing to Scaling

Scaling generative AI presents challenges that tests don’t fully address. Data must be usable and processes stable. Employees need fluency to work with tools and accountability is necessary when AI acts. Kalra emphasized evaluating scale use cases by business performance. Growth, efficiency, and cash conversion are key metrics.

Industry Research

Tinaikar cited Genpact and HFS Research. They surveyed 2,002 executives in 16 industries. Results found only 6% qualified as proven debt re-mediators, indicated by initiatives addressing and measuring technology issues. Kalra discussed tech debt involving outdated systems and infrastructure.

Hidden Debts

Technology debt is visible. Data debt, process debt, and talent debt are less apparent but limit AI usage. Usable data and specific enterprise context are crucial for AI agents. Differences in processes across regions add complexity. Kalra noted that a general AI model can’t navigate these without specific knowledge.

Process Intelligence and AI

Kalra asserted that AI gains require process intelligence. IT and governance discussions should occur early. He emphasized involving CIOs and CDOs from the start. Without their involvement, IT departments might resist deploying agentic systems.

Security and Governance

Security concerns should be addressed alongside responsible AI frameworks. As agents handle finance and supply chain execution, mistakes can lead to financial or regulatory issues. Agentic operations shift from human-processed to machine-processed systems, maintaining human responsibilities for exceptions.

Workforce Preparedness

Kalra stressed workforce readiness. Employees must understand AI tools to redesign workflows. At Genpact, many employees have access to these tools. Tinaikar emphasized balancing skill development with day-to-day duties. Training is imperative, not optional.

Developing AI Skills

Kalra outlined skills into AI builders and AI practitioners. Builders mix technical expertise with business knowledge. Practitioners start with domain knowledge like finance or procurement and develop AI expertise.

Changing Roles

Technology changes roles as machines handle more tasks. Tinaikar cited the smartphone era. Predictions of displacement missed new ecosystems and opportunities they created. Kalra echoed this sentiment, referencing Jevons paradox: efficiency increases can boost overall activity.

Immediate Challenges

Kalra warned workers about AI skills. Your job isn’t taken by AI, but someone with better AI knowledge might replace you. Expanded automation requires proper data, processes, security, and employee readiness.

Kalra concluded with high aspirations contrasted by low readiness.

Attend our next “AI Impact Forum” webinar. On October 22, Dr. Ranjit Tinaikar and Firdaus Bhathena from S&P Global will discuss enterprise technology and organizational changes for business results. Register for free today.