The AI Talent Crunch: Should You Build or Buy?

As technology continues to redefine competitive advantage across nearly every industry, one issue has become increasingly urgent for investors—particularly private equity and venture capital firms: the AI talent crunch. In this installment of our "AI Investment Red Flags" series, we examine a core decision for those seeking to capitalize on AI's growth potential: build AI capabilities in-house or buy them through acquisition and external hiring?

Below, we explore the latest research findings, outline strategic considerations, and offer expert commentary on balancing these two approaches. Our goal is to help you make informed decisions that mitigate talent risks and position your portfolio companies for AI-driven success.

1. Understanding the Talent Deficit

According to a recent McKinsey Quarterly report (Accelerating Skills Transformation in the Digital Age, February 2024), 55% of workforce roles worldwide will require substantial upskilling or reskilling within the next three years. Global digital skills training investments are projected to exceed US$4 trillion by 2027, highlighting the depth and urgency of this skills gap.

From an investment perspective, this skills gap raises a critical question: can you afford the time and resources it takes to nurture talent from within, or is rapid external acquisition the only way to keep pace with the competition?

Key Takeaways:

Ongoing Skills Gap: Demand for advanced AI, data science, and machine learning skills is skyrocketing.

Strategic Imperative: Upskilling is no longer optional—it's an essential pillar of any long-term AI strategy.

Cost & Time Pressures: Building internal AI talent capability requires sustained commitment and cultural buy-in, while acquiring ready-to-go teams can quickly bolster your competitive advantage.

2. The Case for Building: Growing Teams Internally

Building AI teams internally offers a foundation for deeper organizational knowledge, cultural cohesiveness, and sustained innovation:

Cross-Disciplinary Teams: Harvard Business Review (2024) notes that thoughtfully formed cross-disciplinary teams can boost innovation by up to 20%. Diverse functional expertise spurs creativity and faster problem-solving for complex challenges.

Improved Engagement & Retention: McKinsey's 2024 analysis highlights that organizations proactively optimizing for technical, strategic, and cultural competencies see a 15% gain in operational efficiency and 10% reduction in turnover.

By nurturing talent in-house, you retain valuable institutional knowledge and foster employee loyalty—an especially critical factor given how quickly AI fields evolve. However, this approach requires significant upfront investment in training programs, mentorship, and culture-building. For investors with long-term horizons, these benefits can pay off handsomely.

3. The Case for Buying: Acquisition and Rapid Hiring

On the flip side, acquiring teams or recruiting external talent delivers immediate skill sets and faster time-to-impact:

Time-to-Hire Advantage: Another McKinsey Quarterly study (January 2024) found that organizations using AI-driven recruitment methods cut hiring times by 20%, with a 68% improvement in matching candidates to roles.

Cost & Early Retention Benefits: Bain & Company's research (March 2024) identifies end-to-end digital hiring strategies as a driver of a 15% increase in early retention and 18% reduction in overall hiring expenses.

From an investment standpoint, "buying" AI talent can accelerate product roadmaps, support rapid scale-up for new market opportunities, and mitigate the lengthy timelines often associated with internal build-out.

4. The Hybrid Model: Striking the Right Balance

Investors need not choose between building and buying. A hybrid model, harnessing each approach's strengths, can deliver the most optimal and sustainable results:

Strategic Acquisitions: By purchasing proven AI teams, startups, or key hires, you address immediate talent gaps and reduce time-to-market.

Sustained Internal Growth: Ongoing learning initiatives—such as reskilling pathways, mentoring programs, and collaborative team structures—build long-term innovation capacity and cultural resilience.

Ultimately, the most successful companies strike an intentional balance, using external hires to fill urgent needs and retaining a robust internal pipeline that supports continuous AI development.

5. Investor Checklist: Structuring Your AI Talent Strategy

Deploy Data-Driven Recruitment Tools

Use advanced analytics and AI-driven candidate sourcing to quickly close skill gaps in portfolio companies.

Acquire Strategic Assets

Look for specialized AI teams or startups that can accelerate product development and complement existing resources.

Build Culture & Capability from Within

Run continuous skill assessments and create personalized learning pathways to foster a culture of innovation.

Optimize Cross-Functional Teams

Combine technical, strategic, and cultural competencies to form high-performing, nimble units that can adapt quickly to market changes.

Evaluate Timing & ROI

Weigh speed-to-market benefits against the journey of internal development to align talent strategy with short- and long-term goals.

Expert Commentary

"Many firms overlook the cultural and operational advantages of building a strong AI team from within, focusing solely on the speed of external hires. In reality, blending both approaches sets the foundation for meaningful, sustainable AI innovation. The crucial question is not 'build vs. buy' but rather how to smartly layer them."

— Dr. Andrea Lim, Head of AI Talent Strategy at FutureVision Partners

"In private equity, deal timing often pushes us toward rapid hires or acquisitions. While that speeds up go-to-market, it doesn't replace the institutional knowledge and skills you gain by fostering an internal AI culture over time."

— Martin Huang, Managing Director, GreenLight Capital

Conclusion

The AI talent crunch is real—and investors must address it with strategies tailored to both immediate market pressures and long-term innovation. Most often, a hybrid approach (buying specialized AI capabilities when speed is critical and building ongoing internal competency) offers the greatest ROI. By integrating data-driven recruitment practices, disciplined acquisition strategies, and robust internal development, you can future-proof your portfolio in an increasingly AI-centric landscape.

Sources

Harvard Business Review, 2024.

McKinsey Quarterly, "Accelerating Skills Transformation in the Digital Age", February 2024

Bain & Company, March 2024.