From agility to AI: Building a talent strategy that lasts

 

The HR function is at a significant inflexion point. The changing business expectations, along with the advent of AI, have resulted in increased challenges for the HR function. Compounding these challenges are the talent needs, which are shifting faster than what traditional hiring and upskilling cycles can manage. AI is moving from just being an experimental initiative to an everyday tool used for everything, from writing better-worded emails to making real-life agents that interact with employees. What remains unchanged is the fact that the employees want experiences that feel seamless, responsive, and relevant.

Against this backdrop, the pertinent question is not how to react faster, but how to build a talent strategy that can hold up under today’s uncertainty.

For me, that begins with a simple principle: agility is not simply about doing things faster. It is about being clear on what truly matters and building capability around that core with clear intention and discipline.

Agility begins with clarity, not speed

When the conversation turns to agility, the instinct is often to talk about speed. In practice, speed without clarity usually creates noise. Real agility begins with a sharper understanding of what is core to the business and where depth of personal capability is essential.

That is where talent strategy has to become more deliberate. It cannot be spread too thin. It has to strengthen the areas that create the greatest business value.

In my experience, one of the most important shifts in HR has been to protect the role of HR business partnering from getting bogged down by transactional work. If HR business partners are spending too much time on coordination, follow-ups, and routine execution, they are not able to operate at the level the business increasingly needs from them.

This is where the role of external partnerships becomes meaningful. A partnership model such as Recruiter-on-Demand can create operating leverage in hiring by bringing speed, process discipline, and candidate management capability, while allowing HR partners to focus more deeply on workforce planning, talent strategy, and assessing behavioural and cultural fit. The value of such a model lies not only in execution support, but in enabling sharper role clarity within HR itself.

The friction is in the experience, not just the process

A great deal of operational friction today sits in employee experience. That experience is shaped not necessarily by one policy or one platform, but by the combined effect of systems, workflows, and partnerships that have evolved over time.

Each layer may have earlier been introduced with a valid rationale. But employees do not experience them in parts. They experience them as one unified system. If that system feels fragmented, the experience feels fragmented too.

One common challenge across organisations is that legacy platforms and newer workflows do not always integrate smoothly with the core HRMS. Each additional process introduced without proper integration and alignment adds manual effort and complexity. Over time, what should have become a more seamless experience instead becomes harder to navigate.

The answer is not simply to keep adding new platforms. It is time to rethink architecture from the standpoint of simplicity, integration, and usability. The question is no longer whether a process exists. The question is whether the employee experiences it as coherent. Understanding the platform capabilities and reimaging your policies to adapt to the HR platform (rather than the other way round) has helped to provide a seamless experience. 

Learning is going through a paradigm shift as well. Employees increasingly expect support in the flow of work, not only through structured learning journeys and static learning modules. With Gen AI and conversational tools, that expectation is becoming stronger. People want contextual answers, immediate relevance, and practical support linked to real problems. That does not reduce the importance of learning. It simply means that learning design has to evolve from being programme-led to being more contextual, immediate, and embedded in work.

Skills are shifting. Judgment is becoming more valuable

Skill obsolescence is now a reality, not a future possibility. Capabilities that were considered premium even a few years ago may no longer hold the same value today. That makes upskilling and reskilling necessary, but it also requires us to think more carefully about the nature of future contribution.

As AI-enabled systems begin to handle more repetitive and rules-based work, the role itself starts changing. In many cases, value will shift from execution to judgment. Employees will increasingly be expected not merely to do the task, but to manage, guide, interpret, and troubleshoot systems that are doing the task.

This makes problem-solving, learning agility, and the ability to work through ambiguity more important than before. At the same time, the distinctly human capabilities do not diminish. In fact, they become more critical. Empathy, curiosity, developmental conversations, contextual judgment, and people leadership remain central. Technology can support the process. It cannot replace the quality of human judgment that many leadership situations demand.

AI integration requires a foundation first

There is understandable excitement around AI, but the starting point for meaningful integration is not deployment alone. It is the architecture itself.

If systems are fragmented and data is spread across platforms that do not connect well, adding AI on top of that does not solve the problem. It often increases complexity. The first discipline, therefore, is to build a stronger data foundation - structured data being captured, cleaner integration, and a more unified environment across systems, including partner platforms.

Once that foundation is in place, AI becomes more practical and more credible. It can support areas such as workforce planning, learning insights, career recommendations, and employee query resolution in ways that are genuinely useful.

One early and visible example of this is employee support through AI-led query handling. When such systems are designed well, they can respond in a way that reflects policy context and individual need, reducing dependency on manual intervention for routine questions. Over time, these systems can begin to function as digital coworkers - not as substitutes for judgment, but as practical supports in day-to-day work.

ROI has to be defined more broadly

In a fast-moving environment, ROI cannot be viewed only through the lens of immediate financial return. It also has to be understood in terms of readiness, adaptability, and capability creation.

One of the strongest indicators of a healthy talent system is the extension of the leadership pipeline, especially the ability to fill critical roles from internal talent. That says a great deal about succession quality, development seriousness, and long-term organisational strength.

Alongside that, two lenses continue to matter. The first is efficiency: turnaround time, SLA adherence, and reduction of friction. The second is effectiveness: whether the value chain itself is designed well enough to produce better business outcomes.

Platform readiness is part of this equation too. Systems must evolve in line with user expectations, integration needs, and compliance requirements. In many cases, platform upgrades or rewrites are not optional –they are foundational to future capability.

The people dimension is equally important. Organisations need teams that can operate confidently in an AI-enabled environment, with the right mix of technical capability, cross-functional collaboration, and willingness to reskill. In that sense, ROI is not just about cost optimisation. It is also about building the organisational muscle to adapt continuously.

Trust is not a feature. It is a discipline

As AI becomes more embedded in HR processes, trust moves to the centre of the conversation. Candidates and employees are asking important questions around transparency, fairness, data usage, and authenticity. Those questions cannot be addressed through communication alone.

The starting point is clear adherence to data privacy requirements. But trust also depends on transparency in how systems are being used, what they are intended to do, and where their limitations lie. People need clarity, not abstraction.

Beyond this, trust is shaped by governance. Responsible use requires the right stakeholders, the right oversight, and a clear framework for decision-making. Over time, trust is built less by what organisations say and more by how consistently these systems are implemented in practice.

The real question is what human time is freed for

As AI takes on more transactional work, the more important question is not simply what can be automated. It is what human time should now be used for.

The real opportunity lies in shifting time and energy toward problem-solving, judgment, advisory work, stronger stakeholder engagement, and more thoughtful leadership. That is where redesigning work becomes meaningful. And that is where long-term value will be created.

This article is written by Shivin Tikoo, CHRO, Mahindra First Choice. Disclaimer: Views expressed are in a personal capacity and not reflective of any organisation.