Chanakya Thunuguntla's AI Solution to Rebuild the U.S. Workforce

The United States is grappling with significant labor market challenges that could have far-reaching implications for economic growth. What started as isolated hiring difficulties has evolved into a broader issue, raising concerns about the nation’s future competitiveness in the global market.

The problem goes beyond simple supply and demand. Companies are still using talent playbooks designed for a different era — one where employees worked the same role for decades, job requirements changed slowly, and global disruptions were rare. As these outdated strategies persist, the nation’s workforce challenges become more apparent.

Chanakya Thunuguntla, a people analytics and AI lead at a $150 billion financial technology platform, believes that artificial intelligence (AI) holds the key to solving these issues. With over a decade of experience working with Fortune 500 organizations across tech, healthcare, and finance, Thunuguntla has witnessed the impact of these outdated workforce strategies firsthand.

"These methods are too slow and too generalized to keep up with the evolving needs of a modern economy," Thunuguntla explains.

Indeed, these deficiencies have contributed to a growing talent shortage. According to recent studies, 74% of U.S. employers are struggling to find the skilled talent they need. The potential impact of this shortage is staggering: The country could face a deficit of more than 6 million workers by 2030, potentially resulting in $8.5 trillion in unrealized annual revenue.

However, Thunuguntla believes that AI and people analytics offer a powerful solution by enabling precision at scale. Instead of treating employees as static roles in an org chart, AI can map patterns to offer actionable insights tailored to individuals and teams. This approach can predict issues in performance, engagement, and skill development, helping organizations prevent these problems before they occur.

In his current role, Thunuguntla has put these principles into practice using graph neural networks and transformer-based machine learning models. The result is a robust workforce forecasting model that considers the subtle complexities of human behavior and helps identify potential attrition risks.

One surprising finding was 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," Thunuguntla says.

The model integrates external signals like geopolitical factors, macroeconomic trends, and market volatility, which can influence employee behavior in unexpected ways. For instance, supply chain disruptions or rising inflation might impact retention differently across job types, regions, and business units.

This flexible and adaptive framework for talent forecasting allows 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 also 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. It identified specific roles and skills that were at the highest risk and hardest to replace, 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 a significant competitive advantage. Looking to the future, he predicts 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.