HR is no longer just about tracking the past—it’s about anticipating the future with precision. The ability to foresee talent gaps before they arise and proactively retain top performers is no longer aspirational—it’s happening now, powered by AI-driven analytics. With real-time insights, hiring managers can predict workforce trends before they unfold, while HR leaders gain the strategic foresight to strengthen retention, optimise talent acquisition, and build a more agile, future-ready organisation.

At the People Matters TechHR Singapore 2025 conference, Yi Ting Lee, Head of People Analytics and Strategic Workforce Intelligence at Micron Technology, joined Hum Whye Seng, Head of Solution Consulting (APAC) at Visier, for a deep dive into AI’s transformative role in people analytics.

In the fireside chat titled, ‘Amplifying HR Impact: Where AI Meets People Analytics,’ they explored how AI-driven analytics is revolutionising workforce planning, talent strategies, and decision-making. Their discussion offered a strategic roadmap for organisations looking to harness AI-powered insights, enhance workforce intelligence, and gain a lasting competitive edge in the evolving world of HR.

The Growing Influence of AI in Workforce Decision-Making

Kicking off the conversation, Whye Seng engaged the audience with an interactive poll, asking where AI analytics would have the biggest impact in HR over the next five years. The responses underscored that talent acquisition and workforce planning as key focus areas, reaffirming the broad relevance of people analytics across HR functions.

Building on this, Yi Ting shared her experiences at Micron, illustrating how AI-powered analytics has revolutionised workforce decision-making. She recounted her early journey in talent acquisition analytics, explaining how organisations increasingly integrate multiple data sources beyond traditional applicant tracking systems (ATS) to gain deeper insights into hiring trends.

“Talent acquisition analytics is one of the most exciting areas in HR due to its diversity and complexity. Organisations rely on multiple data sources beyond ATS, integrating various technologies to enhance sourcing and engagement,” she noted.

She further elaborated that the diversity is multiplied as data is contributed by different parties like hiring manager, applicants, recruiters, interviewers and the complexity is amplified by the massive datasets. 

“At Micron, we don’t just rely on an ATS. We have invested in technology to support sourcing, skills-matching, and candidate engagement,” she explained. “Each of these tools generates valuable data, which needs to be integrated for a holistic analysis.”

Natural Language Processing (NLP) plays an essential role in optimizing the whole process of transforming large unstructured data into valuable insights. The complexity of consolidating data from diverse internal and external sources, processing and standardizing underscores the necessity of AI. Deep learning techniques remove irrelevant information, identify and correct spelling and grammatical errors, and standardize the data for analysis. Algorithms are then used to identify topics, entities, sentiments, and other information from the data to give valuable insights. 

“It’s more than having one source of truth but also enables us to analyze people data in a much more powerful and scalable manner because the eventual analysis can now be applied across the entire talent management lifecycle to support decision making.” Yi Ting added. 

The Shift from Descriptive to Predictive Analytics

Despite the immense potential of AI, many HR functions still operate primarily within the realms of descriptive and diagnostic analytics. Whye Seng pointed out that over two-thirds of people analytics leaders spend most of their time on data integration, grappling with the complexities of predictive modelling.

Yi Ting acknowledged this challenge, referencing Gartner research, which indicates that most HR analytics efforts remain focused on understanding what happened and why something happened by leveraging on historical data and trends. However, such an approach relies heavily on the analyst or decision maker's experience and intuition. 

“In this rapidly evolving world, we are constantly having to react to new market conditions. AI and technological advancement alone have resulted in an unprecedented disruption, and there are geopolitical tensions, economic instability. Many of such disruptions are novel or being combined in different ways that makes it difficult to use past experience to understand why something happens today, let alone using it for future decision making.”

Yi Ting gave an example on predictive attrition, which introduces a new level of strategic decision-making by leveraging multiple data points—engagement scores, promotion rates, salary trends, and more—to model the probability of employee attrition and other key workforce outcomes.

“This requires a multivariate statistical approach, combining signals from various data points to uncover the likelihood of turnover, it is therefore important to leverage AI to ensure data quality across the entire talent management process.” Yi Ting explained.

Data quality is crucial for accurate models and reliable predictions, but it is one of the most common challenges faced in predictive analytics. Machine Learning Techniques help to improve data quality through data profiling, anomaly detection and validation.

AI-Powered Market Intelligence in Workforce Strategy

Beyond internal analytics, the team integrates external market intelligence to inform workforce strategy. Yi Ting highlighted how her team contributes to location strategy evaluations.

“Talent availability and sustainability are crucial alongside other factors like business climate, infrastructure readiness and operational costs,” she explained. “We leverage market intelligence to compare different companies' workforce footprints against industry trends, identifying locations with strong talent pools and assessing competitive pressures.”

Micron relies on talent intelligence tools that employ deep learning AI, which aggregate and validate data from thousands of sources daily for consistent comparison and analysis. This enables the company to conduct comprehensive supply-and-demand analyses of skills and competitors, providing leadership with data-driven recommendations.

“Our intelligence framework consolidates publicly available data into actionable insights. It’s a combination of research, analytics, and business acumen that informs high-stakes decisions,” she added.

Key Takeaways: Start Small, Show Value, Scale Up

For organisations and HR leaders embarking on their people analytics journey, Yi Ting offered practical advice:

Start Small, Think Big – "Don’t try to tackle everything at once. Focus on specific use cases to deliver business impact then scale gradually."

Secure Leadership Buy-In – "Building a business case and demonstrating quick wins are crucial for gaining executive support and investment in AI-driven analytics."

Prioritise Data Quality and Integration – "Data-driven decision making is only as powerful as the data it relies on. Ensuring high-quality, well-integrated data is fundamental to scale towards advanced analytics. AI plays an integral role in optimizing the whole process of transforming large complex datasets into valuable insights."

Balance Long-Term Goals with Near-Term Value – "While AI can provide strategic insights, immediate wins—like improving hiring efficiency—can help build momentum for larger analytics transformations."

The Future of AI in HR: Driving Business Value

As the session concluded, Whye Seng reinforced a key takeaway: “Start small, find your sponsors, celebrate early wins, and gradually scale up. This structured approach lays the foundation for impactful people analytics.”

This engaging fireside chat underscored the transformative potential of AI in HR. As organisations navigate the complexities of workforce analytics, AI provides the tools to move beyond historical data and shape future workforce strategies with precision.

By integrating predictive insights, external market intelligence, and the right technology investments, HR leaders can unlock new levels of business value and competitive advantage.

For those looking to deepen their AI analytics journey, the conversation between Yi Ting Lee and Hum Whye Seng offered a compelling roadmap grounded in practical experience, strategic foresight, and a clear vision for the future of HR analytics.

As companies adapt to an increasingly complex talent landscape, AI-powered people analytics will be a critical enabler of business success, offering a competitive edge in workforce management and beyond.