Sustainable AI talent is no longer optional: CHRO Rekha Nair on what organisations must get right by 2026

India’s AI talent challenge is no longer about scarcity. It is about design.

As organisations prepare for 2026, Rekha Nair, CHRO at Tredence, a data science company, argues that the biggest risk lies not in hiring too little AI talent, but in building pipelines for a world that no longer exists. “The biggest gap in AI talent pipelines is that they remain designed around static roles, while AI itself is becoming dynamic, agentic, and system driven,” she said.

For years, companies have divided AI capability into two camps: those who build systems and those who use them. “Most companies have focused on the creators of AI, such as data scientists, and the users of AI, such as prompt engineers,” Nair noted. “While both remain important, this model is no longer sufficient.”

The next phase of AI adoption, she said, demands a third category of talent: people who can run, govern and orchestrate autonomous systems inside enterprises. “The next phase of adoption requires a new category of talent: orchestrators of AI,” she said. “These professionals design, govern, and run agentic AI systems that operate autonomously within enterprise workflows.”

This role, however, does not fit neatly into existing hiring frameworks. “The role demands a blend of three main skill areas: deep technical knowledge of AI stacks, strong domain expertise such as retail, banking, or healthcare, and functional know-how in areas like supply chain, customer experience, or finance,” Nair said. “In the current scenario, most talent pipelines build these skills in isolation instead of integrating them into a unified capability.”

Learning models built for the wrong workforce

The structural problem, Nair said, extends beyond hiring into how organisations think about learning. “A major gap we see today is in how organizations approach learning,” she said. “CHROs need to revisit the traditional HR processes to integrate AI-driven talent, especially in areas like learning, performance management and assessment for a workforce that includes both humans and intelligent agents.”

AI, she argued, has already changed how learning works, but organisations have not caught up. “AI has transformed learning to become more customised, continuous, and adaptive, yet most organizations still use standardized, linear, time-bound models,” she said. “Today, we have the tools to personalize learning based on how people think, make decisions, and grow—but most organizations aren’t using them yet.”

The cost of delay is not abstract. “If companies don’t rethink how they hire and develop talent for the age of Agentic AI, they risk building workforces based on yesterday’s skills instead of preparing people for what’s coming next,” Nair said.

Why campus and lateral hiring are no longer enough

The pace of change has also exposed the limits of traditional talent sources. “AI skills are evolving every 12–18 months, making day-one ready talent obsolete faster than ever,” Nair said.

Campus hiring, she explained, often optimises for the wrong attributes. “Campus hiring often overemphasizes task-based skills—the very ones AI automates first—while overlooking learning agility and judgment essential for an agentic AI world.”

Lateral hiring, meanwhile, has become both expensive and restrictive. “Lateral hiring is highly competitive and constrained by a shrinking talent pool, leaving organizations reactive rather than proactive,” she said.

The alternative is a shift in mindset. “The shift must be toward early engagement, focusing not on who is ‘ready now’ but on how quickly they can learn,” Nair said.

Testing judgment, not just knowledge

Early engagement models, she argued, allow organisations to observe what resumes cannot show. “Early engagement models shift the focus from what a candidate already knows to how quickly they can learn,” she said.

Instead of screening for immediate readiness, organisations should create environments where learning happens in real time. “These models use judgment labs like hackathons and internships to simulate real-world problems,” Nair explained. “By placing candidates from both traditional and non-Computer science backgrounds in ambiguous situations with AI agents, employers can move past static resumes to observe real-time learning agility and intent.”

What matters is not task execution, but decision-making. “This approach prioritizes judgment growth over simple task completion, revealing how an individual handles ethical trade-offs and complex workflows,” she said. Over time, “these processes are designed to identify learning velocity, allowing organizations to find adaptable talent capable of evolving alongside AI.”

The CHRO as architect, not buyer, of capability

For Nair, the responsibility for this shift sits squarely with HR leadership. “CHROs are at the center of moving organizations from quick, short-term hiring to building long-term AI capability,” she said.

That begins with redefining work itself. “Their job is to rethink how work gets done—deciding what can be fully automated, what should be handled by AI with human oversight, and what still needs human judgment, creativity, and empathy,” she said. “This creates a clear blueprint for new job families rather than just adding AI onto old roles.”

Hiring alone cannot deliver this transition. “Instead of solely relying on buying talent through lateral hiring, CHROs must pivot to a build strategy,” Nair said. “This involves sponsoring internal academies and career acceleration programs that move employees from ‘sunset’ roles into emerging AI-heavy positions.”

Mobility inside the organisation becomes critical. “CHROs must build internal talent marketplaces that enable talent mobility, reward managers for growing and rotating talent and create a growth ecosystem that continuously builds future-ready capabilities,” she said.

At the same time, governance cannot be an afterthought. “Finally, CHROs must ensure AI is used responsibly by building human oversight and ethics into every talent practice,” Nair added.

Measuring success differently

As skills reset faster than hiring cycles, traditional metrics lose relevance. “To stay competitive, organizations must stop hiring for static skills and shift toward learning velocity,” Nair said.

This requires structural redesign. “This requires CHROs to fundamentally redesign roles, decomposing work into tasks for automation, AI-agent execution, and human-owned judgment while moving from a buy to a build mindset,” she said.

The payoff is a self-renewing workforce. “By replacing premium lateral hiring with internal bridge programs and AI-driven learning platforms, organizations can transition employees from declining roles into new, AI-augmented positions,” Nair said. “This creates a renewable talent engine where success is measured by how quickly people adapt to what the business needs next, rather than what they knew on day one.”

What a sustainable pipeline looks like in 2026

Looking ahead, Nair said sustainability will be defined by movement, not mastery. “By 2026, a strong AI talent pipeline will be about movement & fungibility, not fixed skills,” she said. “A CHRO’s success will be measured less by having the right skill list and more by how quickly people can adapt as tools and roles change.”

Clarity plays a central role. “When employees understand what AI can do and where human judgment is still essential—the fear of being replaced turns into clarity and confidence,” she said. “They start seeing themselves as partners to AI, guiding and working with it, rather than competing against it.”

The end goal, Nair said, is not optimisation but resilience. “A living talent system is the end goal,” she said. “The most resilient organizations treat talent as something that continuously renews & reinvents itself.”

In an AI economy defined by constant change, that ability to renew may become the most valuable capability of all.