The rise of generative and agentic AI is forever changing the skills needed for tech jobs. More than three-quarters of IT departments now include AI skills. In one year, GenAI job openings increased by 170%, demand for AI governance skills increased by 150%, and AI ethics increased by 125%. As a result, companies are facing a severe shortage of AI engineering talent.
The demand for AI talent reflects the need for companies to find new paths beyond traditional hiring to close the AI engineering gap and remain competitive. For example, GenAI’s management consultant title, which was virtually non-existent in 2024, topped the Indeed list of GenAI roles in 2025, representing the growing demand for roles in AI implementation beyond the direct creator of the technology.
There are approximately 1.3 million jobs in the U.S. that require AI skills, but half of those jobs could remain unfilled by 2027, according to a study by Bain & Company. According to a report by the World Economic Forum (WEF), 94% of 1,010 executives surveyed currently face an AI-critical skills shortage, with 33% reporting gaps of 40% or more in key roles.
Lack of AI skills threatens the fate of projects
The lack of AI talent in business settings directly impedes AI adoption in a number of ways:
- Project abandoned. Pluralsight’s 2025 AI Skills Report found that 65% of organizations abandoned AI projects due to lack of skills.
- Development is slow. According to research from McKinsey & Company, companies without the right AI skills develop AI tools too slowly.
- Implementation and expansion challenges. Skills shortages also impact the ability to deploy and scale AI projects. Gartner reported that less than half of AI projects go into production, while McKinsey noted that only 1% of companies scaled AI across the enterprise.
The cost of talent: Hiring vs. contracting
Demand for AI talent exceeds supply, creating a seller’s market and increasing the cost of acquiring qualified engineers. Companies need to assess whether their AI needs justify employment, contracting, or a combination of the two.
employment
Hiring skilled AI engineers in-house requires a significant financial commitment beyond a base salary.
- compensation package. The average base salary for a senior AI engineer is $185,000, and can be significantly higher in some cases.
- Hidden costs. Hiring for these roles is very expensive. Costs such as procurement fees, interview time, and new hire ramp-up periods can be significant before the first line of production code is written.
- Employee benefits. Companies may be required to offer a wide range of financial and other benefits to prospective employees.
- Maintenance challenges. Employee retention is difficult due to high demand and competition for AI skill sets. Competitors may offer better compensation to lure new employees.
Under contract
Using a consultancy or agency to “rent” talent can meet your immediate needs, but the hourly markups can be high and can blow your budget if not closely managed.
- Premium price. Popular consultants can charge upwards of $300 to $500 per hour, while junior consultants are often charged $100 to $150 per hour. For critical short-term interventions, it has been reported that “surge pricing” can increase rates to as much as $900 per hour.
- Agency vassal. For ongoing support, recruitment agencies and contracted agencies may charge a monthly fee or initiation fee.
- Institutional knowledge. Companies run the risk of contractors leaving the company and taking their knowledge with them to another organization.
How to close the AI engineering gap
Although implementation has been inconsistent, companies are pursuing several strategies to close the AI engineering gap, including:
- Full-time employment. According to General Assembly’s State of Tech Talent 2025 survey, 63% of recruiting leaders say sourcing candidates with the right AI skills is more difficult than for other technology roles, and companies are allocating more funds to finding and hiring engineering talent. Additionally, 75% of HR professionals say they hire too quickly without building a viable pipeline.
- Skill-first recruitment. HR leaders are increasingly adopting a skills-first hiring approach that focuses on professional qualifications rather than university degrees.
- Contract talent. IT organizations have been using contract and freelance talent to supplement in-house resources for decades, and they’re taking a similar approach to AI skills..
- Improve in-house skills. Companies are upskilling and reskilling their workforces to better understand, leverage, and build on AI.
- Educational Partnership. Companies are recruiting directly from training facilities such as AI bootcamps.
- AI productivity tools. To improve overall productivity, companies are using AI to learn AI.
Four steps to closing the AI engineering skills gap
Consider the following steps when developing an effective strategy to address the AI engineering gap:
1. Assess the AI skill level of your entire workforce
Conduct a skills inventory across your business. As part of your assessment, determine which roles need to use AI tools and which roles need to build AI systems.
2. Define your company’s AI goals and requirements
Identify what your business wants to build and achieve with AI. For example, is the company deploying an AI chatbot for customer service, building predictive models for inventory management, or implementing AI-assisted coding across its engineering team? Each goal requires a different skill set. For example, implementing customer service AI may require rapid engineering and integration skills. Alternatively, AI models typically require data scientists and machine learning experts. Document the specific competencies needed and compare them to your company’s current AI skill levels to identify talent gaps.
3. Choose a strategy based on realistic constraints
While upskilling your workforce in-house is the best approach to building a long-term competitive advantage in the AI space, it’s important to understand that there are limitations to having the best possible AI talent:
- time constraints. When organizations want to achieve short-term results, immediate adoption of AI productivity tools is often the fastest way to achieve results. For example, AI-powered coding assistants can improve developer productivity.
- Budget constraints. Outsourcing AI skills can potentially reduce costs compared to hiring engineers. Contracts can specify delivery dates and payment terms that meet certain criteria, such as budget.
- Capability constraints. If you need specialized AI skills without a long-term contract, contract talent offers the highest AI proficiency. According to an Upwork study, 54% of freelancers surveyed reported “high AI proficiency,” compared to 38% of full-time employees.
4. Combine approaches to acquiring AI skills
The most effective strategy is to build multiple approaches simultaneously. For example, deploying AI tools now while implementing a long-term upskilling program. It is important to evaluate, iterate, and continually evaluate which approaches are working and which are not.
Sean Michael Kerner is an IT consultant, technology enthusiast, and tinkerer. He is known for pulling Token Ring, configuring NetWare, and compiling his own Linux kernel. He consults with industry and media organizations on technology issues.
