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.