In my blog post “Four Categories of AI Solutions” (https://birkholm-buch.dk/2024/04/22/four-categories-of-ai-solutions), I outlined different approaches to building AI solutions, but it’s becoming increasingly clear that the decision on how to approach AI hinges on the talent and capabilities within the organization. As AI continues to evolve at lightning speed, companies everywhere are racing to adopt the latest innovations. Whether it’s generative AI, machine learning, or predictive analytics, organizations see AI as a strategic advantage. But as exciting as these technologies are, they come with a less glamorous reality—people skills are the make-or-break factor in achieving long-term AI success.
The Talent Crunch: A Major Barrier to AI Success
Reports from both McKinsey and Gartner consistently highlight a serious shortage of skilled AI talent. McKinsey’s latest research suggests that AI adoption has plateaued for many companies not due to a lack of use cases, but because they don’t have the talent required to execute their AI strategies effectively. 60% of companies cite talent shortages as a key risk in their AI adoption plans. (McKinsey State of AI 2023: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year).
This talent crunch means that organizations are finding it difficult to retain and attract professionals with the skills needed to develop, manage, and scale AI initiatives. 58% of organizations report significant gaps in AI talent, with 72% lacking machine learning engineering skills and 68% short on data science skills (McKinsey AI Tech Talent Survey: https://www.mckinsey.com/capabilities/quantumblack/our-insights/new-mckinsey-survey-reveals-the-ai-tech-talent-landscape).
The New Frontier of AI Engineering Talent
Given the rapid pace of change in AI tools and techniques, developing an internal team of AI Engineers is one of the most sustainable strategies for companies looking to stay competitive. AI Engineers are those with the expertise to design, build, and maintain custom AI solutions tailored to the specific needs of the organization.
By 2026, Gartner predicts that 30% of new AI systems will require specialized AI talent, making the need for AI Builders even more urgent (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).
The Challenge of Retaining AI Talent
The retention challenge in AI isn’t just about compensation—it’s about providing meaningful work, opportunities for continuous learning, and a sense of ownership over projects. Diverse AI teams are essential for preventing biases in models and creating more robust, well-rounded AI systems. Offering inclusive, supportive environments where AI engineers can grow professionally and personally is essential to keeping top talent engaged (McKinsey AI Tech Talent Survey: https://www.mckinsey.com/capabilities/quantumblack/our-insights/new-mckinsey-survey-reveals-the-ai-tech-talent-landscape).
Skills Development: A Competitive Advantage
AI is only as good as the team behind it. As Gartner highlights, the true value of AI will be realized when companies can seamlessly integrate AI tools into their existing workflows. This integration requires not just the right tools but the right people to design, deploy, and optimize these solutions (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).
Companies that prioritize skills development will have a competitive edge. AI is advancing so quickly that organizations can no longer rely solely on external vendors to provide off-the-shelf solutions. Building an internal team of AI Builders—engineers who are continually learning and improving their craft—is essential to staying ahead of the curve. Offering employees opportunities to reskill and upskill is no longer optional; it’s a necessity for retaining talent and remaining competitive in the AI-driven economy.
Looking Ahead: The Evolving Role of AI Engineers
As organizations continue to explore AI adoption, the need for specialized AI models is becoming more apparent. Gartner predicts that by 2027, over 50% of enterprises will deploy domain-specific Large Language Models (LLMs) tailored to either their industry or specific business functions (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).
I believe the role of AI Engineers will continue to grow in importance and complexity. We might see new specializations emerge, such as AI ethics officers ensuring responsible AI use, or AI-human interaction designers creating seamless experiences.
In my view, the most valuable AI Engineers of the future could be those who can not only master the technology but also understand its broader implications for business and society. They might need to navigate complex ethical considerations, adapt to rapidly changing regulatory landscapes, and bridge the gap between technical capabilities and business needs.
Conclusion: Investing in People is Investing in AI Success
The success of any AI initiative rests on the quality and adaptability of the people behind it. For organizations aiming to lead in the AI space, the focus should be on creating a workforce of AI Engineers—skilled professionals who can navigate the ever-changing landscape of AI technologies.
The lesson is clear: to succeed with AI, invest in your people first. The future of AI is not just about algorithms and data—it’s about the humans who will shape and guide its development and application.
