With every week bringing a new AI tool or feature to the market, leaders are increasingly looking at their AI adoption and return on investment. Nobody can disagree with the potential of AI, after all, the market is projected to grow from $347 billion USD in 2026 to $1.68 trillion USD by 2031. Yet 93% of global AI and data leaders identify human factors as the primary barrier to AI adoption in their organisations. And there is no profession better suited to resolving human factors than learning and development.

After all, since the rise of the profession during the 19th-century Industrial Revolution, L&D has been tasked with empowering individuals to work alongside technology, adapt to market changes, and keep their skills up-to-date with the latest business needs. The AI-powered revolution is no different, albeit, on a much wider and transformative scale than all the Industrial Revolutions that came before.

Learning leaders will be the main drivers of AI adoption in their workforces and, as such, can benefit from tactics that technology and product leaders have used for years to boost the adoption of their software and new features.

 

Three tactics to increase adoption

1. Allow people to get hands-on with technology

Look at any major technology launch and you’ll see people exploring the new products first-hand. That’s because product leaders understand that getting your hands on an innovation is the easiest way to learn about it. The same applies to AI, with learners having to experience it in realistic scenarios to truly understand how to work with it. Especially as AI products become more advanced and able to complete more tasks, with human employee responsibilities then shifting to higher cognitive, governance-type activities.

The International Monetary Fund (IMF) suggests that humans will need a mix of “cognitive, creative and technical skills that complement AI and help them use it rather than compete with it.” The only way you can build this effectively is to allow people to practice with the AI technologies they’ll be using, in the context of their role, through scenarios and challenges they’ll likely face.

 

2. Foster a safe environment for (some) AI failure

In the early stages of technology adoption, product leaders expect some roadblocks and errors. The key is mitigating the impact and risk of this as much as possible, while learning from it.

When being taught a new AI skill, a learner will make mistakes. They will misinterpret results, fail to spot a hallucination, give a prompt that doesn’t achieve the right outcome or feed their AI model incorrect data. These errors are fine, if they happen in a safe environment with no operational, data or reputational consequences. To encourage experimentation and give "permission" to fail, provide your learners with sandboxes and virtual IT labs where they can test AI solutions on non-live data and in non-production environments.

 

3. Test performance and proficiency

Ultimately, you want your learners to be able to perform on the job, with the right AI tools, achieving the right business outcomes. Learning how to use, govern and collaborate with AI is not a tick box or content completion exercise. It is performance driven.

When testing your workforce’s AI skills, look at their proficiency and skill application. To do this, set your learners a specific challenge – for instance, using an AI co-pilot to analyse research findings. Then assign pass/fail or skill-level scoring based on specific criteria such as AI model used, time taken, critical steps (using the right research papers), if they followed corporate governance and results. This approach provides a highly accurate indicator of their ability to do the same tasks with an AI solution in the real world.

 

Rapid AI adoption starts with training

Your organisation’s AI adoption hinges not only on the technology itself, but also on how you – as the foremost experts on skills and learning within your company – equip people to use AI. By prioritising hands-on practice, fostering safe environments for mistakes and rigorously assessing skills, L&D professionals can drive meaningful change and close the gap between AI potential and performance.

 

Sarah Noe-Danzl 

Chief Marketing Officer at Skillable