How to Build Human-Centric People Practices in an AI-Driven World

By: Achal Khanna, CEO – SHRM India, APAC & MENA | Tuesday, 26 May 2026

India is rapidly emerging as one of the world’s fastest adopters of workplace AI, with organizations increasingly integrating artificial intelligence across functions. But as automation accelerates, businesses face a larger question: how can they embrace AI without losing the human connection that drives trust, growth, and performance?

HR expert, Achal Khanna believes that organizations mustn’t treat AI adoption and human-centric values as competing priorities. Instead, people practices must evolve to put human dignity at the center of every technology decision.

Achal Khanna brings over three decades of global leadership experience, driving growth, building inclusive workplaces, and shaping HR and business ecosystems across APAC and MENA.

In conversation with Women Entrepreneurs Review, Achal shares how businesses can balance technology adoption with empathy, trust, continuous learning, and ethical decision-making. From redesigning performance management to building transparent AI-driven cultures, the discussion highlights why the future of work must remain deeply human even in an increasingly automated world.

For deeper insights on the evolution of people practices in an AI-driven world, read the full conversation below.

Q1: In an AI-driven world, how do people practices need to fundamentally evolve to balance technology adoption with human-centric workplace values?

Achal Khanna: The evolution required here isn't primarily technical -- it's philosophical. Organizations need to stop treating AI adoption and human-centric values as competing priorities, because that framing creates a false choice that leads to poor decisions on both fronts.

People practices must evolve to put human dignity at the center of every technology decision. That means involving employees in conversations about automation before implementation, not after. It means measuring impact on people with the same rigor applied to measuring operational efficiency. And it means designing AI systems around human strengths rather than simply replacing human tasks.

The companies that get this balance right will discover that technology and humanity aren't in tension -- they're genuinely complementary when the intent behind adoption is honest and the process is inclusive.

Q2: How can organizations redesign talent strategies to ensure continuous learning while keeping employees relevant alongside rapidly advancing AI capabilities?

Achal Khanna: Static talent strategies -- the kind built around fixed job descriptions and annual training cycles are structurally incompatible with the pace at which AI capabilities are advancing. Organizations need to move toward dynamic skill architectures: models that map not just what people currently do, but what they're capable of learning and how quickly adjacencies can be developed.

Microlearning, internal mobility programs, mentorship structures, and project-based stretch assignments all contribute to this.

But the most important redesign is cultural. Shifting from a mindset where training is a cost to be minimized toward one where capability development is genuinely understood as a competitive investment worth protecting even under budget pressure.

Q3: What are the biggest cultural shifts leaders must drive to build trust and transparency when AI starts influencing people-related decisions?

Achal Khanna: Trust, in this context, is not given -- it's built through consistent, observable behavior over time.

Employees need to know when AI is involved, what data it draws from, and critically, who is ultimately accountable for the outcome. That last point matters enormously.

AI cannot be a shield behind which leadership hides difficult decisions. Paired with transparency must be genuine recourse -- real mechanisms for employees to question or appeal decisions influenced by algorithmic outputs. Without that, transparency becomes theater, and trust erodes faster than it was built.

Q4: How should HR leaders rethink performance management systems as AI tools increasingly shape productivity, outcomes, and employee expectations?

Achal Khanna: Performance management was already broken in many organizations before AI entered the picture. Rigid annual reviews, rating scales disconnected from actual behavior, and feedback loops too slow to be useful -- these were well-documented problems.

AI doesn't fix any of that automatically. What it can do, if implemented thoughtfully, is provide more continuous, data-informed signals about performance enabling more frequent, more specific conversations between managers and employees.

The rethinking required is this: performance management should shift from a documentation exercise to a genuine development conversation, supported by better data but driven by human judgment. AI generates signals; managers create meaning from them. That distinction must be preserved, or organizations risk reducing complex human performance to metrics that flatten what actually matters.

Q5: In your experience, how can organizations ensure ethical AI use while maintaining fairness, inclusion, and unbiased decision-making in people practices?

Achal Khanna: Ethical AI use in people practices requires deliberate structural investment, not goodwill alone. Organizations need to audit the data their AI systems are trained on because biased historical data produces biased outputs, regardless of how sophisticated the model is.

Diverse representation in the teams building and governing these systems is non-negotiable; homogeneous groups consistently miss the blind spots that affect underrepresented employees. Clear accountability frameworks must exist -- someone must own the ethical implications of every AI-influenced people decision.

Regular third-party audits of outcomes across demographic groups help identify disparate impact before it compounds. And employees must have accessible, credible channels to raise concerns. Ethics in AI isn't a feature to be added at the end of implementation -- it must be architected into the beginning.

Q6: What advice would you like to share with industry leaders to keep people practices simple, human, and effective while adapting to an AI-driven workplace?

Achal Khanna: The most important reminder is one that gets crowded out by the noise of technology: your people don't primarily want sophisticated systems -- they want to feel seen, heard, and valued in their work. As AI tools multiply and processes grow more complex, the leaders who stay closest to that truth will build organizations that actually function well. Simplify wherever possible. Eliminate the performance rituals that consume time without creating meaning. Have real conversations instead of relying entirely on engagement survey scores. Trust managers to lead, and give them the time and training to do it well. AI should reduce administrative burden so that human energy goes toward human things -- connection, development, purpose. That is the whole point. Don't lose it chasing the sophistication of the tools.

Achal’s 5-point Checklist for Human-centered AI Adoption:

1. AI adoption should strengthen human-centric workplaces rather than replace human value and judgment.
2. Organizations must redesign talent strategies around continuous learning, adaptability, and dynamic skill development to stay relevant in an AI-driven world.
3. Building trust requires transparency about how AI influences hiring, performance, promotions, and other people-related decisions.
4. Ethical AI depends on accountability, unbiased data, inclusive governance, and systems that allow employees to question decisions.
5. The future of leadership lies in simplifying processes, strengthening human connection, and using AI to create more meaningful employee experiences.
    

As AI continues reshaping the future of work, the conversation around technology is no longer just about efficiency; it is about intention. Achal’s insights reinforce that organizations that succeed in the AI era will not necessarily be those with the most advanced tools, but those that use technology to create more transparent, inclusive, and deeply human workplaces.