The race to harness the power of AI continues, and companies are setting bold targets for the coming years. But there’s a catch: Globally, the number of job postings for positions with AI skills has surged 21% annually since 2019, far outpacing supply. A reflection of the demand, compensation for AI professionals has climbed 11% annually in the same period, well above the global nominal wage growth of around 4%.
Talent scarcity is now the biggest barrier to progress in implementing AI. In a recent Bain survey, 44% of executives identified the lack of in-house expertise or resources as their top obstacle to moving fast with generative AI—outranking data security and readiness. In the US alone, the number of people who currently have or are expected to have AI skills is only about half of what Bain projects companies will require by 2027, necessitating the rapid reskilling of an estimated 500,000 to 750,000 workers.
The talent shortage is real, but it isn’t insurmountable. Attracting, cultivating, and retaining the right talent will require a shift away from legacy thinking. I believe there are three imperatives for companies engaging in this transformation.
Strategic alignment and focus
Top tech prospects are inspired by the opportunity to solve complex, high-impact problems. Leading organizations get this, and they design tech roles with specific, high-value use cases in mind. Consider recent listings on LinkedIn: Target posted for a principal data scientist specializing in advertising technology (adtech), while Best Buy was searching for an advanced analytics director focused on network design and fulfillment solutions.
These companies recognize that their tech and business needs are deeply intertwined. By aligning tech recruitment and skill development with strategic business priorities, they will not only attract the right talent but also keep employees engaged and invested.
Too many organizations take a one-size-fits-all approach to hiring for tech roles, applying generic recruitment practices and offering subpar compensation. This creates friction between business and tech teams, hurting employee satisfaction and pushing away valuable hires.
Evolution of tech capabilities
Top-tier talent isn’t flocking to companies with legacy tech stacks. Outdated capabilities are not only an operational hurdle but also a reputational risk.
Traditional companies in traditional industries can overcome this barrier by communicating their commitment to modernizing their tech capabilities. Competing for talent against start-ups and tech giants demands a multiyear evolution. This can include mapping out a bold investment plan, hiring a star chief technology officer, and/or adding tech thought leaders to the CEO’s direct reports.
Even job descriptions send a signal. Consider a machine learning engineer role in financial services. Capital One, an industry leader in tech capabilities, emphasizes expertise in cloud computing, data pipelines and unit testing, full-stack development, and NoSQL databases—all nods to its modern, scalable approach to data architecture. Meanwhile, traditional financial services firms are still prioritizing SQL and data modeling to fill the same role, hinting at a legacy database approach. For highly skilled job seekers, the choice is obvious.
AI integration
Enticing top talent with strategic alignment and strong tech capabilities is only half the battle. Organizations must also fight to retain their best players by shaping a culture that values cross-functional collaboration and change.
In lagging organizations, AI-related initiatives remain siloed within the IT department, adoption is fragmented, and new ideas stagnate. Companies that are leading the way in AI take a different approach. They promote and integrate AI across functions, training and empowering nontechnical teams to experiment, iterate, and contribute. This fosters a companywide culture of continuous innovation—where the best minds want to stay and solve the next big challenge.
Bridging the gap by 2027
Getting ahead of the AI talent crunch doesn’t require perfection. Companies can still respond to shifting priorities, even with a mix of legacy and modern elements. Leadership in AI is not a finite destination but rather a set of patterns and principles that continue to evolve with the technology. Right now, the guiding principle is clear: Invest in the people who will shape the future.