The digital winds of change are sweeping through Human Resources, transforming it from a support function into a strategic engine room of business innovation.

At the heart of this transformation are two forces: Artificial Intelligence (AI) and People Analytics. Together, they’re not only redefining how HR delivers value but are also helping leaders build workplaces that are agile, ethical, and future-ready.

A 2025 McKinsey study on state of AI, offers evidence that large organisations are forging ahead with generative AI (gen AI), redesigning workflows and reshaping talent strategies to unlock competitive advantage. 

Workflow redesign: unlocking value through AI

Gen AI’s ability to drive efficiency is now closely tied to how well organisations redesign their workflows. Simply layering AI onto existing processes won’t move the needle. Organisations that reimagine tasks and restructure teams around AI capabilities are seeing the greatest gains.

For HR, this redesign is playing out in real time. Hiring trends show a pivot: while demand for data visualisation and design roles is softening, the appetite for AI data scientists remains robust. In fact, half of business leaders expect to need even more of them in the coming year. At the same time, newer roles are emerging—AI compliance officers (13% of respondents) and AI ethics specialists (6%)—signalling a shift toward responsible innovation.

Once seen as a niche capability, People Analytics has stepped into the limelight. According to Insight222’s 2024 findings, 68% of companies now view AI as a strategic HR priority.

People Analytics teams are no longer just crunching engagement numbers—they’re informing critical business decisions and contributing directly to commercial outcomes.

In organisations with over 100,000 employees, the average People Analytics team has grown from 25 members in 2020 to 40 in 2024. The best-performing teams across leading companies are embedded across departments, working shoulder-to-shoulder with business leaders to interpret complex workforce dynamics and shape long-term strategy.

And while historically, only 10% of organisations have connected human capital data to business metrics in a meaningful way (as per Josh Bersin’s 2024 analysis), AI is rapidly closing this gap.

AI: turning scattered data into strategic insight

A persistent challenge in HR has been data fragmentation. With some large organisations juggling as many as 40 HR systems, deriving actionable insights has been akin to assembling a jigsaw puzzle in the dark.

This is where AI earns its stripes. Various HR tech tools are transforming this tangled web into a unified view. Now, HR teams can swiftly correlate training histories, compensation, performance, and tenure to pinpoint the drivers of turnover or productivity.

This evolution marks the dawn of “Systemic Analytics”—the ability to explore how multiple, interconnected workforce variables impact business performance. As Bersin aptly puts it, “AI frees People Analytics staff to spend less time cleaning and modelling data and more time understanding the big problems to work on.”

AI is reshaping the very experience of work. Generative AI is being deployed to craft personalised learning journeys, decode employee sentiment, and offer real-time HR support through intelligent chatbots.

Another study by Insight222 shows that 63% of companies are now actively measuring improvements in employee experience tied to these innovations.

Alex Alonso, Chief Data & Analytics Officer at SHRM, sums it up well: “The future of HR isn’t just about managing people—it’s about making data-driven decisions that drive business impact.”

However, not all firms are equally prepared. A concerning 58% of HR leaders cite a lack of data literacy in their teams, while 56% blame insufficient infrastructure. This digital divide must be bridged—through targeted education, incentivised innovation, and strategic partnerships with technology providers.

Some forward-thinking organisations are offering cash rewards for employee-developed AI solutions, proving that culture and capability can grow together when nurtured thoughtfully.

From adoption to impact: embedding AI at scale

According to McKinsey, there are 12 key practices linked to achieving meaningful EBIT impact from gen AI. These include setting up dedicated adoption teams, rolling out role-specific training, establishing clear KPIs, and crafting compelling change stories.

Yet fewer than a third of companies report using most of these practices.

Larger firms are taking the lead—they’re more likely to build trust among customers, model AI adoption at senior levels, and integrate AI into core business processes. Tracking clearly defined KPIs stands out as the most impactful practice of all.

Time saved by AI is being repurposed in a variety of ways. In many firms, employees are spending it on value-added tasks rather than simply reducing headcount. That said, some large companies do report workforce reductions, particularly in areas like supply chain and service operations. Meanwhile, roles in IT and product development are expected to grow.

The financial services sector is an outlier—here, a reduction in workforce size is more commonly expected.

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Ethical guardrails: taming AI’s wild side

With great power comes great responsibility, and nowhere is this more evident than in the ethical use of AI. SHRM rightly warns, “One of the foremost ethical concerns surrounding AI is data bias.” AI models inherit the imperfections of their training data, making thoughtful governance critical.

While many organisations are addressing cybersecurity and IP infringement risks, fewer are tackling challenges like AI explainability or bias.

Notably, only 58% of companies had completed a preliminary AI risk assessment by mid-2024, leaving a significant risk exposure on the table.

Interestingly, the presence of the CEO in AI governance correlates with stronger outcomes. In fact, CEO oversight had the greatest EBIT impact from gen AI in large firms, suggesting that ethics and performance are two sides of the same coin.

To build trust, companies must adopt clear principles:

• Develop strong AI policies in collaboration with legal and tech teams.
• Ensure transparency by communicating openly about how AI influences decisions.
• Audit regularly for bias and inaccuracy.
• Keep humans in the loop, especially for critical calls on hiring, compensation, and terminations.