Building an Effective AI Skilling Program: Five Proven Strategies from Akkodis and Microsoft

Generative AI technology is creating unprecedented opportunities to improve business efficiency, fuel innovation, and gain a competitive edge. Given these benefits, equipping your workforce with the skills to effectively use AI-powered tools has become a high priority for organizations across industries.

However, the rapid evolution of AI technology, along with the potential impacts on established processes, means that matching people with the right skills requires a deliberate approach. Organizations need strategies that can keep pace with technological change while delivering measurable business outcomes.

Akkodis and Microsoft's Partnership in AI Skilling

Across the Asia-Pacific region, global engineering and digital solutions company Akkodis helps organizations design, build, and operate technology-driven solutions. Through its Akkodis Academy, the company integrates Microsoft technology and training, including industry-recognized Microsoft Credentials (role-based Microsoft Certifications and scenario-based Microsoft Applied Skills) that cover AI, into its customized learning and consulting programs.

These programs help customers build learning cultures that keep up with the increasing pace of technological change. By combining Microsoft's technical expertise with Akkodis's industry experience, organizations can develop AI capabilities that directly support their business objectives.

Five Strategies for Building a Successful AI Skilling Program

Based on extensive experience working with organizations across various sectors, Akkodis has identified five strategies that can help you build or sustain a successful AI skilling program.

1. Start Small, Learn Fast, Iterate

AI capabilities and business needs evolve quickly. With iterative training cycles, you don't need a perfect blueprint to get started. Instead, short, focused training sprints let you try ideas, collect feedback, and quickly improve.

This approach lowers the intimidation factor for newcomers and helps build confidence and momentum with staff and stakeholders. By starting with manageable pilot programs, organizations can test different approaches, measure results, and scale successful initiatives.

2. Tie Skilling Directly to Business Goals

The most successful skilling programs align training with technology and business goals. They also secure sponsorship from company leaders to help establish priorities and reinforce adoption efforts.

Anchor training efforts to concrete business outcomes, like productivity, time-to-market, operational costs, or other real-world business metrics. When employees understand how AI skills connect to organizational objectives, they're more likely to engage with training and apply new capabilities in their daily work.

3. Establish a Culture of Continuous Learning

The traditional skilling model of "take a course, and you're done" doesn't work well for rapidly evolving technology. Establishing an always-on learning culture using webinars, tech talks, and collaborative community learning can move your teams from a "know-it-all" to a "learn-it-all" mindset.

This approach keeps teams' skills fresh and curiosity growing. Regular knowledge-sharing sessions, internal communities of practice, and access to ongoing learning resources help organizations maintain momentum and adapt to new developments in AI technology.

4. Combine Technical Skills with Business and Governance Knowledge

Organizations that realistically evaluate the business utility of AI tools tend to adopt this technology more effectively and efficiently. This means AI skills need to be complemented by business and operational knowledge.

Alongside technical instruction, your teams should understand and apply your organization's governance policies related to data privacy, ethical AI use, and other regulatory requirements. This holistic approach ensures that AI adoption is both technically sound and aligned with organizational values and compliance needs.

5. Provide Practical, Applied Learning

Focus on real-world skills that show tangible application—not only what AI is but also how to effectively use it. Bootcamps, role-based labs, and an emphasis on practical scenarios can help bridge the gap between theory and real-world use.

This approach can directly correlate with productivity gains and other key outcomes. When employees can immediately apply what they've learned to their actual work, they're more likely to retain knowledge and see the value of AI tools in their daily tasks.

Real-World Success Stories

Commonwealth Bank of Australia

Commonwealth Bank of Australia invested heavily in AI skilling, equipping employees to effectively adopt AI tools. As a result, 84% of its 10,000 Microsoft 365 Copilot users report that they wouldn't go back to working without it, and developers are adopting approximately 30% of GitHub Copilot code suggestions.

This demonstrates how comprehensive AI training can drive significant adoption and user satisfaction, leading to measurable productivity improvements across the organization.

Adecco Group

Adecco Group's AI skilling strategy has increased productivity for recruiters by 63%. Additionally, the company's AI-driven CV Maker generated 200,000 résumés, and 35,000 employees completed responsible AI training, driving better client interactions.

These results show how AI skills can transform not just individual productivity but also core business processes and customer engagement.

Next Steps for Your Organization

An effective AI skilling program is about more than technology—it requires a workforce that can adapt and thrive as AI reshapes the business world around them. Successfully building AI fluency across your organization can accelerate technology adoption, create improved business outcomes, and lead to tangible competitive advantages.

Ready to grow your team's AI skills? Read Create an AI Learning Culture for more detailed guidance on implementing these strategies in your organization.

By following these proven approaches and learning from successful implementations, organizations can build AI capabilities that deliver real business value while preparing their workforce for the future of work.