AI adoption alone is no longer the differentiator. The real advantage lies in having a workforce, workflows, and operating models that are ready to put AI to work. 

As Rudrashish Sen, Global Director, Ensono, put it: “AI adoption is no longer the differentiator. AI-ready workforce and deployment-ready operating models are.” 

 He summed up AI readiness through WORDS: 

• Workflow and process integration 
• Ownership and leadership alignment 
• Risk and governance controls 
• Data foundation and platform readiness 
• Skills and capability maturity 

The future is human + agent, not human vs. agent

Organizations are beginning to rethink how work gets done. Agents can create, execute, and accelerate work, but human oversight remains critical. 

Hanumesh VJ, Associate Vice President, Infosys, shared: “We are spending time on what agents are creating — required learning paths, assessments, all those requirements are created by agents, but they are verified by humans.” 

 The opportunity isn’t to replace human capability, but to redesign how human expertise and AI capabilities work together. 

 Roles will evolve as agents become part of the workforce

As AI agents take on more executional work, the nature of technical roles will change. Professionals will increasingly need to orchestrate, supervise, evaluate, and make decisions alongside AI. 

 Santhosh Rebello, Global Workforce Development, Head – Higher Education, Salesforce, captured this shift: “We used to be proud saying that we were engineers, but we are no more engineers. We are going to be engineering managers because we are going to manage agents.” 

The implication is clear: AI is not just changing tasks. It is changing roles.

AI literacy needs to move beyond awareness

 Preparing people for Agentic AI requires more than introducing them to GenAI tools. Employees need to understand where AI can create value, how to work with agents, and how to apply AI to real business problems. 

 That means building AI capability around business outcomes, workflows, roles, and real-world application, not simply tool familiarity. 

Human capabilities become even more valuable

As AI takes on more tasks, distinctly human capabilities become increasingly important. These capabilities help people question AI outputs, define the right problems, make informed decisions, and take accountability for outcomes. 

AI capability building needs to reflect the realities of each industry

The discussion also brought perspectives from the automotive industry, where AI is already changing both engineering capabilities and customer experience. 

Raghuprasad Shantharam, Director – Engineering, HARMAN, shared how his organization is building AI capabilities across its engineering workforce: “We started a training called AI for every engineer. It includes Machine Learning, GenAI, plus what is required for automotive. Around 90% of my business unit is already part of this training.” 

He also highlighted how Agentic AI and advanced AI capabilities are shaping the next generation of automotive experiences, where autonomy and personalization are becoming increasingly important. 

L&D itself needs to evolve as fast as AI

AI is changing faster than traditional learning models can keep up. For L&D teams, this means moving beyond fixed programs, periodic needs analysis, and one-size-fits-all learning journeys. 

Rebecca Ireland, Associate Director – Research Labs L&D (India), Eli Lilly and Company, captured the challenge: “The most competent, most plugged-in people also wake up in the morning and they’re like, something’s changed in the AI world itself.” 

She highlighted how the boundaries between formal and informal learning are increasingly blurring — and why personalization and continuous, on-the-job learning are becoming essential. 

AI, therefore, isn’t simply creating a need for new learning content. It is challenging L&D to rethink how skills are identified, learning is personalized, and capability is built continuously.