Bridging the AI Skills Gap: Top Strategies for IT Teams in 2025

Artificial intelligence and machine learning are among the most desired skill sets from IT professionals headed into 2025, as organizations invest more time and resources into the twin technologies.

Based on data from more than 5,100 global IT decision-makers and professionals, the annual Skillsoft report also found that despite their importance, AI/ML skillsets are ranked lowest among existing team capabilities, suggesting a significant skills gap IT decision-makers are eager to address.

Leadership development is also gaining traction, with 58% of organizations offering formal leadership programs and 28% planning to invest in leadership training in the upcoming year.

Cybersecurity and information security remain the most challenging areas for hiring, with 38% of decision-makers citing difficulty in filling these roles.

Meanwhile, skill gaps persist across IT teams, with nearly two-thirds of decision-makers acknowledging deficiencies in their current workforce.

To address this issue, 72% of organizations plan to focus on training their existing talent, a strategy that appears to have proven to be effective, given that 94% of decision-makers reported tangible workforce benefits from training programs.

For IT professionals, the motivation to upskill is driven primarily by a desire to learn new skills (54%), followed closely by the need to stay competitive in the job market (53%) and enhance job security (46%).

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Skillsoft CIO Orla Daly said with this baseline information, organizations can define a multi-faceted approach to close key skill gaps, including AI and machine learning.

"As an area experiencing some of the most rapid development, talent will benefit from a blend of foundational learning and learning paths that are tailored to their specific role — for example, GenAI for marketers or GenAI for sales," she said.

Equally important is fostering a culture of continuous learning and providing the resources for team members to continue to learn and upskill as technology advances.

Daly explained that practical applications are key to learning, and creating cross-functional teams that include AI experts can facilitate knowledge sharing and the practical application of new skills.

"To prepare for 2025 and beyond, it's crucial to integrate AI and ML into the core business strategy beyond R&D investment or technical roles, but also into broader organizational talent development," she said. "This ensures all employees understand the opportunity [and] potential impact, and are trained on responsible use."

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A recent IEEE survey of 355 IT leaders worldwide found 40% of respondents are seeking software development expertise to support and implement AI-driven solutions, with 35% of respondents emphasizing the importance of ensuring responsible AI deployment through ethics training.

Urgent Need for AI Ethics Skills

Kayne McGladrey, IEEE senior member and field CISO at Hyperproof, said AI ethics skills are important because they ensure that AI systems are developed and used responsibly, aligning with ethical standards and societal values.

"These skills help in identifying and mitigating biases, ensuring transparency, and maintaining accountability in AI operations," he explained.

Organizations can provide AI ethics training to relevant personnel, clearly define roles and responsibilities, establish policies, and implement practices that foster critical thinking about AI risks.

"Skills in AI ethics are crucial for preventing harmful outcomes and maintaining public trust in AI technologies," McGladrey added.

Closing the Critical Skills Gap

Yaad Oren, managing director of SAP Labs U.S. and global head of SAP BTP innovation, said as AI and automation continue to reshape the IT industry in 2025, leaders must prioritize championing a culture of continuous learning.

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"While technical expertise is essential for understanding and leveraging emerging technologies, leadership and soft skills are equally important," he said.

He called the AI skills gap "one of the biggest challenges" organizations are facing today.

"For businesses to experience the true benefits AI can offer, employees in all areas of the business need to understand the impact of their role on the company's AI strategy," Oren said.

To achieve this, organizations should implement three strategies:

Daniel Avancini, chief data officer at Indicium, said organizations should adopt a mix of strategies for AI upskilling, understanding what works best for them regarding internal culture and adoption.

"First, AI/ML skill levels need to be assessed for each department," he said.

Specific training programs should be tailored for each area, considering that some teams will need more general foundational training in data, not only AI.

"Finding champions that already have AI for personal or work use cases shouldn't be hard and is also important to drive adoption," Avancini said.

He also recommended creating controlled, sandboxed environments where employees can trial and test AI models in practice, understanding not only the value but also the risks and limitations of AI/ML applications.

"Hiring top AI talent is incredibly challenging for most organizations at this moment, since there is a very hot market for these professionals and a limited supply," he said.

A different strategy is either building an internal AI training program with the help of outside consultancies or outsourcing AI development altogether.

Scott Wheeler, cloud practice lead at Asperitas, said building a culture of innovation and continual learning is the first step in closing a skills gap, particularly for newer technologies like AI.

"Provide access to learning resources, such as on-demand platforms like Coursera, Udemy, Wizlabs," he suggested. "Embed learning into IT projects by allocating time in the project schedule and monitor and adjust the various programs based on what works or doesn't work for your organization."

Wheeler added that "just-in-time mentoring" continues to be the most effective training method he has experienced.

It involves placing one or more experienced mentors on the project team with the team members to be trained.

"The team receives real-time training on a real-world project during the project, which significantly solidifies the training," Wheeler said.