Leaders are looking for the right AI training for their teams to take advantage of what AI can do for their business. According to a survey by S&P Global in August 2025, eighty-eight percent of businesses say their workforce will need new technology upskilling within the next 12 months. However, for leaders at these companies, it can be challenging to find the right training in an endless sea of AI course offerings and “LinkedIn AI experts.” As of November 2025, there are 3,415 AI classes on Coursera alone.
With tight budgets and ongoing layoffs, leaders are thinking about how they can ensure any training is worth the time and money. This is what every leader considering AI training should know before investing in AI education for their team.
Set Clear Goals For AI Training
Before starting a search for AI classes for your team, it’s important to hone in on the goals and outcomes you’re looking to achieve. “Success is tied to business impact,” Mariena Quintanilla, CEO and AI Educator at Mellonhead, shared with me in an interview. “How those experiments actually move the organization forward. There’s more clarity around aligning AI education with real outcomes, not just usage.”
Pari Katyal, Director of Product at Bola AI, explained how he assesses if an AI class is right for his team. “The content is actionable and will help drive a difference.” He also said that a good AI class “improves how [you] think about quality, reliability, and decision-making in AI products.”
Defining the goals and the measurable impact you expect will help filter out the classes that aren’t aligned and give you a clearer picture of which classes could give you the outcomes you aim for. It will also help you set expectations with your team for what you want them to gain from the class.
Hands-On AI Training
Secondly, it’s incredibly important to build “AI muscle memory” by getting your team actively engaged in doing, not just taking notes. Helen Kupp, Founder, Women Defining AI, stated that a lot of companies are looking for a more hands-on approach. “Companies come to me because they hear about how hands-on the program is, and my focus on doing rather than lecturing or telling, and want to bring that learning to the workplace,” Kupp shared in a conversation with me.
“The best training came from speaking to technical experts, actual AI experts/builders, not ‘LinkedIn-only AI experts’“ Edwin Trebels, Founder LangOptima, told me in an interview. Working with trainers who use the tools themselves allows them to teach beyond theory and get deeper into your team’s level of AI fluency and workflows.
Role Centered AI Training
Getting hands-on helps teams experiment and learn first-hand, but it doesn’t do much good if what they learn isn’t focused on their role and workflows. Quintanilla noticed that more frequently “companies recognize that technical skills alone don’t lead to adoption. People want to know how AI applies to their specific role.”
Getting specific on the type of training needed for the specific roles on the team and desired outcomes compounded even after training for Trebels’ team. Trebels said that training for his team also “led us to other associated tactics and tools which were complementary and necessary to put AI into our production workflows.”
Kupp emphasized the importance of understanding roles and workflows. Before any training, she focuses on “what types of repetitive work do they often do, before suggesting places to start our hands-on workshops together.”
Supporting And Measuring Success After AI Training
It’s important to note what types of AI tools employees will be using during the workshop. In a conversation, Mike Russo, Head of Product at Auction.com, told me, “if you send someone to class to learn, will that person have the tools to apply when they learned to produce an outcome or will they be blocked by lack of tooling?”
Once the class is over and your employees have the skills and the right tools, it’s important to ensure they have time to continue experimenting with those tools to get the most out of them and learn new ways to power their workflows.
Lastly, it’s important to measure the difference from before the class and after the class. Trebels’ team saw measurable changes after training. Trebels said his team was able to automate many of their processes and save 40-60 hours a week. “We saved time translation work due to a higher quality level of machine translation…we also save a tremendous amount of time with transcription, AI voice narration, automatic CRM updates.”
Leaders should focus on bringing in AI training that is aligned with their goals and outcomes and targeted towards the roles and workflows of their team. While results may take time to manifest, they should be tangible and measurable, and start to show and evolve after the training is over. Choosing the right AI upskilling programs for your team will lead to transformation in your business.