Generative AI has quickly become a must-have in many businesses. In just under two years, it shifted from a novelty to a core part of how companies operate. Now, most organizations are facing a big challenge: not enough employees know how to use AI tools effectively in their daily work. It’s not just about hiring AI specialists anymore. Companies across industries like IT, travel, staffing, and manufacturing are trying new ways to get their teams ready for an AI-first future. Their experiences reveal five key ideas on how to close this skills gap.

Focus on Mindsets Instead of Just Skills

At UST, a global digital services company with over 32,000 employees, the leadership sees AI as a game changer. Krishna Prasad, the CIO and strategy chief, says that expertise used to be UST’s main value. Now, basic knowledge is easily accessible online for free. Clients don’t want to pay for simple skills anymore. What really matters now is the ability to solve problems creatively.

Instead of only teaching technical skills, UST emphasizes developing a mindset of curiosity, critical thinking, and creativity. These are skills that AI can’t easily replace. The company has built an internal sandbox where employees can safely experiment with AI tools like GitHub Copilot, Google Gemini, and Cursor. This environment encourages trial and error without risking real projects. It helps employees learn by doing, not just watching.

This experimental culture is crucial. It pushes staff to get comfortable with making mistakes and learning from them. UST’s “Take Flight with AI” program guides employees through three stages—taxi, takeoff, and cruise—focused on applying AI at work. Success isn’t just about learning theory, but about actually using AI in client projects, participating in hackathons, and creating real value. The main goal is to make each person responsible for their ongoing learning journey.

Learning in the Flow of Work

Jill Busch from ManpowerGroup highlights that traditional training methods are too slow for the fast pace of AI development. Instead of pulling employees out of their daily tasks for training sessions, she recommends embedding learning directly into the tools they use. Digital adoption platforms like Whatfix provide real-time tips and guidance right inside the apps recruiters and other staff are working with.

For example, when using a recruiting system, employees see helpful hints about candidate sourcing or resume analysis without leaving their workflow. This kind of in-the-moment support has dramatically reduced support questions—by as much as 95%. Busch stresses that quick, iterative “micro-learning” modules—short lessons integrated into daily work—are more effective. They help keep skills fresh without interrupting productivity.

The training also includes AI ethics and responsible use, which are now part of onboarding for new hires. The key is to make learning seamless and relevant, so employees can pick up new skills while doing their jobs. This approach makes AI adoption more natural and less intimidating.

Scaling Structured AI Training Programs

At Lexmark, an office equipment company, Sudhir Mehta leads a comprehensive AI training initiative called the AI Academy. Starting with just five data scientists, it now includes over 5,000 employees. This is made possible through modular courses, mentorship, and partnerships with universities and tech giants. Graduates become mentors themselves, helping to sustain the momentum.

The company uses a mix of self-paced online courses, live workshops, and longer programs like a 20-week AI Excellence track. They also focus heavily on responsible AI use, with ethics and governance embedded into the curriculum. To prove the value of these efforts, Lexmark runs pilot projects first, then scales successful initiatives across the organization. Measuring results through project ROI and tool adoption rates helps justify the investment.

The main takeaway is that a structured, leadership-aligned training program can reach thousands of employees and make AI skills widespread.

Building an AI-First Culture

At eSky Group, a global online travel company, AI is no longer optional. Tomasz Lis, the engineering manager, explains that AI is now central to how the company works. With around 800 staff, eSky promotes a grassroots approach. Employees have access to top AI tools like Gemini Pro and GitHub Copilot, and more than half use them daily.

The company encourages a culture where using AI is as normal as sending emails. They have AI ambassadors who mentor colleagues, a Slack group for sharing best practices, and “Demo Fridays” where teams showcase their AI-driven projects. Training combines vendor content, external resources, and peer learning, creating a supportive environment for innovation.

The results are impressive. Marketing teams can produce videos in minutes, customer service can analyze all interactions automatically, and developers collaborate more effectively. Building this culture requires not just training, but peer-led initiatives that make AI part of everyday work.

Embedding AI Throughout Software Development

Globant, a large IT services firm, takes a different approach. Juan José López Murphy explains that the way software is built has fundamentally changed. To stay competitive, they need to upskill their teams fast.

Their strategy is to embed AI into every part of the software development process, from planning and testing to deployment. Developers are trained to be fluent in multiple large language models (LLMs) like ChatGPT, Claude, Gemini, and Bedrock. They also learn to use proprietary AI agents that help automate and improve their work.

This comprehensive approach ensures that AI is not just an add-on but a core part of how Globant creates software. It reduces the risk of falling behind and helps the company stay innovative in a rapidly evolving tech landscape.

In summary, these companies show that closing the AI skills gap isn’t just about technical training. It’s about shifting mindsets, integrating learning into daily work, scaling structured programs, fostering a culture of innovation, and embedding AI into core processes. The organizations that succeed will be those that treat AI readiness as a continuous journey, not a one-time effort.

Inspired by

• https://www.computerworld.com/article/4055753/5-ideas-to-help-bridge-the-genai-skills-gap.html

Sources

• gmpg.org
• ust.com
• scaledagile.com
• globant.com
• cio.com