Empowering the People Function Through AI: Key Takeaways from the Webinar

• Artificial Intelligence

As organizations face growing expectations to link workforce decisions to measurable employee experience and business outcomes, People Analytics is increasingly being used to support more timely, confident decision-making. Advances in AI are helping organizations move faster, work at scale, and apply insights more consistently across the employee lifecycle.

These themes came through clearly in our fireside chat, “Empowering the People Function Through AI – From Insights to Timely Decision-Making,” where I was joined by Ujjwal Sehgal, Global Head of People Analytics at Mars, and Bill Jones, Strategic Client Director – Consumer Goods at Microsoft. Together, we discussed perspectives from across enterprise, platform, and analytics, grounded in real-world experience rather than theory.

Below are the key takeaways from the fireside chat.

1. The shift from hindsight to foresight is what makes People Analytics strategic

Ujjwal explained that the driving force behind People Analytics at Mars was the need to move away from a rear-view understanding of the workforce. In a business operating across multiple markets and segments, looking only at what has already happened was no longer sufficient. Leaders needed predictive signals, scenario planning, and forward-looking recommendations they could act on.

Operating across multiple segments and geographies added complexity, making intuition alone unreliable. People Analytics became the function that brought a cohesive, data-driven view of workforce risks and opportunities, helping leaders make better decisions faster, with greater confidence.

2. Strong data foundations determine whether AI can scale

Building on Ujjwal’s point, Bill emphasized that People Analytics only becomes decision intelligence when it is supported by sound, trusted, and accessible data. Data is the lifeblood of decision-making, and without a strong foundation, organizations struggle to move beyond reporting.

Drawing from Microsoft’s own long journey in People Analytics, Bill highlighted the importance of intentional investments in data infrastructure, listening systems, and analytics tooling. These foundations are what make organizations ready to take advantage of AI, rather than running disconnected experiments that fail to scale or drive adoption.

3. Treating human capital as a strategic asset changes the conversation

From my perspective, meaningful progress happens when organizations start treating human capital with the same rigor as revenue, market share, or supply chain. Once leaders view workforce decisions as strategic, analytics is no longer optional.

From C5i’s perspective, this shift requires strong insights across talent quality, onboarding, capability development, and workforce readiness. AI and analytics help bring structure and evidence to these decisions, ensuring that investments in people are deliberate, measurable, and aligned to long-term business goals.

4. Standardization is a prerequisite for scale

Ujjwal mentioned that one of the most important enablers for scaling People Analytics at Mars was the willingness to standardize. Early on, even basic metrics like headcount, attrition, or performance were defined differently across teams and regions, making it impossible to tell a consistent enterprise story.

By aligning on common definitions and scaling them through analytics and AI, Mars was able to create a single, trusted view of the workforce. This standardization laid the groundwork for advanced use cases and helped ensure that insights could be applied consistently while still allowing room for local context.

5. Predictive analytics delivers value when tied to action

One of the most tangible examples Ujjwal shared was around retention. Historically, turnover insights were available only after employees had already left. By introducing predictive models, Mars was able to identify retention risks early, understand the drivers behind them, and intervene proactively.

This approach helped Mars reduce turnover by nearly 50%, demonstrating the measurable business impact that People Analytics can deliver when insights are directly linked to action. Ujjwal emphasized that the value came not from identifying hotspots alone, but from understanding why people were at risk and enabling meaningful conversations and interventions.

6. Workforce planning decisions are expanding in the AI era

The discussion also explored how workforce planning is evolving. Ujjwal described how analytics and AI support decisions around skills, capacity, redeployment, and hiring, moving beyond traditional “build, buy, or borrow” frameworks. With AI, automation itself becomes part of the workforce planning equation.

I believe the real goal is to convert insight into leadership action by clearly defining decision pathways. Analytics becomes valuable when leaders can see how today’s skill mix aligns with future demand and industry trends, and where interventions are needed to close gaps.

7. Trust, privacy, and governance drive adoption

Across the conversation, trust emerged as a recurring theme. Ujjwal spoke about the importance of transparency around assumptions, data usage, and limitations, especially when working with sensitive people data. Without clarity, leaders hesitate to act on insights.

Bill reinforced that enterprise-grade governance, role-based access, and security must be built into platforms from the start. From my perspective, operationalizing trust requires privacy-by-design, bias mitigation, explainability, and strong change management. Together, these elements ensure AI-driven People Analytics is adopted responsibly and sustainably.

8. The “AI translator” role is becoming critical

When discussing the idea of HR becoming “AI translators,” Ujjwal explained that this role is about bridging business context and analytical capability. An effective AI translator understands the business problem, knows what analytics and AI can realistically deliver, and can work closely with data science teams to translate insights into actions leaders can take.

Importantly, this role is not about coding. It is about asking the right questions, understanding limitations, and helping leaders apply insights responsibly. At Mars, this capability is being built through targeted data literacy and learning programs.

Looking Ahead: From Foundations to Enterprise-Scale Impact

What stood out to me from this discussion is that the future of People Analytics lies in its ability to support confident, evidence-based workforce decisions at scale. Organizations that succeed will be those that align analytics with business priorities, invest in AI-ready foundations, and embed trust and governance as core principles.

Three priorities stood out for organizations looking to move forward:

• Anchor People Analytics tightly to real business decisions, not just reporting
• Invest in AI-ready foundations, including standardized data and scalable platforms
• Embed trust, governance, and responsible AI practices from the start

When these elements come together, People Analytics moves from insight generation to true decision enablement, as demonstrated by outcomes such as Mars’ measurable reduction in attrition.

Thank you to everyone who joined the session. For those who were unable to attend live, the recording can be accessed here.

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