AI Transforming U.S. Workforce Planning: Chanakya Thunuguntla's Vision

Published Date : 30/06/2025 

Chanakya Thunuguntla, a leading expert in people analytics and AI, is transforming the U.S. labor market by leveraging advanced technologies to predict and prevent talent issues. His innovative solutions are helping organizations adapt to the rapidly evolving workforce landscape. 

The U.S. labor market is facing unprecedented challenges that could have long-term implications for economic growth. What started as localized hiring difficulties has evolved into a broader issue, raising concerns about the nation’s future competitiveness in the global market. The problem runs deeper than simple supply and demand. Companies are still using talent playbooks designed for a different era — one where employees would work the same role for decades, job requirements changed slowly, and global disruptions were rare exceptions rather than quarterly realities. The more companies resist this evolution, the more apparent the nation’s workforce challenges become.

Chanakya Thunuguntla believes that artificial intelligence (AI) holds the key to solving these issues. After more than a decade working with Fortune 500 organizations across tech, healthcare, and finance, he has seen the impacts of outdated workforce strategies firsthand. Now, as the people analytics and AI lead at a $150 billion financial technology platform, Thunuguntla has developed a solution that not only predicts talent problems but also helps organizations prevent them from happening in the first place.

The cracks in traditional HR approaches became apparent to Thunuguntla early in his career. When he joined a global consulting firm as an associate in 2011, he expected to help clients build databases and basic technology solutions for HR. Six months in, he was tasked with helping one of the world’s largest investment banks with its employee performance and talent practices. Despite his junior tenure, his work was integrated into HR frameworks across the bank’s London and Singapore offices. This experience inspired Thunuguntla to explore how technology and data could shape the future of HR and workforce planning.

However, it also revealed early signs of a problem that would only continue to grow in the following years: Companies were too reliant on traditional annual reviews, static job descriptions, and outdated training programs that couldn’t keep pace with rapidly changing skill requirements. “These methods are too slow and too generalized to keep up with the evolving needs of a modern economy,” Thunuguntla explains. Sure enough, these deficiencies have contributed to a growing talent shortage that continues to challenge the nation. According to a survey, 74% of U.S. employers are struggling to find the skilled talent they need. The eventual impact of this shortage is significant: The country could face a deficit of more than 6 million workers by 2030, potentially resulting in $8.5 trillion in unrealized annual revenue.

But this is where Thunuguntla believes AI can turn things around. “AI and people analytics offer a powerful solution by enabling precision at scale,” he says. “Instead of treating employees as static roles in an org chart, AI can map patterns to offer actionable insights tailored to individuals and teams.” AI can predict issues in performance, engagement, and skill development, and help organizations prevent these problems before they occur.

In his current role as people analytics and AI lead at a major financial technology platform, Thunuguntla has had the opportunity to put these principles into practice. He leverages graph neural networks, which excel at understanding relationships between different data points, and transformer-based machine learning models, which are particularly skilled at identifying complex patterns across time. The result is a robust workforce forecasting model that considers the subtle complexities of human behavior and helps identify potential attrition risks.

For example, Thunuguntla found that social connectivity often predicted staying power better than factors like advancement opportunities. “Teams with stronger peer networks and collaboration patterns showed significantly lower turnover, even when facing high workloads or slower advancement,” he says. This was a surprising finding that revealed that a sense of belonging — something HR traditionally considered unmeasurable — was actually one of the strongest precursors of retention.

Rather than just relying on internal workforce data, Thunuguntla’s model integrates external signals like geopolitical factors, macroeconomic trends, and market volatility. Each of these can influence employee behavior in unexpected yet dramatic ways. For instance, supply chain disruptions or rising inflation might impact retention differently across job types, regions, and even individual business units. Thunuguntla calls this approach “a flexible and adaptive framework for talent forecasting,” allowing organizations to plan ahead with greater confidence and take targeted actions to mitigate risk before it becomes disruption.

At his employer, the model informed adjustments to the company’s hiring approach, contributing to talent planning efforts and supporting decisions involving approximately $2.3 billion in workforce-related budgeting. Beyond attrition reduction and cost savings, the model improved the accuracy of workforce planning and better-equipped HR to identify where investments in retention, upskilling, or succession planning would have the greatest impact. The model also proved useful in identifying specific roles and skills at the highest risk and hardest to replace, steering talent prioritization and transforming what had been a reactive scramble for talent into a data-informed approach to workforce stability.

Thunuguntla believes that workforce planning can no longer be treated as a strictly administrative function. He is confident that the ability to foresee and respond to workforce trends will become an even greater competitive advantage. Looking to the future, he predicts that the nation will undergo a fundamental transformation from rigid job hierarchies and static role definitions to dynamic systems that connect people to opportunities based on capabilities, potential, and cultural fit. “In the next 5 to 10 years, AI will shift the U.S. labor market from a reactive hiring model to a predictive talent ecosystem,” he claims. “Skills, not roles, will become the currency of workforce value, and AI will power real-time talent marketplaces.”

As economic and global dynamics continue to evolve, efficiency is no longer optional—it’s essential. In Thunuguntla’s view, companies that leverage AI HR solutions won’t just stay competitive; they will build the resilience necessary to secure lasting success. 

Frequently Asked Questions (FAQS): 

Q: What are the main challenges in the U.S. labor market?

A: The main challenges include a growing talent shortage, outdated HR practices, and the inability of traditional methods to keep up with rapidly changing skill requirements. These issues are contributing to a potential deficit of more than 6 million workers by 2030.

Q: How does AI help in workforce planning?

A: AI and people analytics enable precision at scale by mapping patterns and offering actionable insights tailored to individuals and teams. AI can predict issues in performance, engagement, and skill development, helping organizations prevent these problems before they occur.

Q: What are the key findings of Thunuguntla's workforce forecasting model?

A: Key findings include the importance of social connectivity in predicting employee retention and the integration of external signals like geopolitical factors and macroeconomic trends. These insights help organizations plan ahead and take targeted actions to mitigate risk.

Q: How does Thunuguntla's model improve workforce planning?

A: Thunuguntla's model improves workforce planning by enhancing the accuracy of forecasts, identifying where investments in retention, upskilling, or succession planning would have the greatest impact, and transforming reactive talent searches into data-informed approaches to workforce stability.

Q: What is the future of workforce planning according to Thunuguntla?

A: Thunuguntla predicts a shift from rigid job hierarchies and static role definitions to dynamic systems that connect people to opportunities based on capabilities, potential, and cultural fit. AI will power real-time talent marketplaces, making skills the currency of workforce value. 

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