How to upskill your workforce for AI success

“Like a lot of companies, we’re looking to improve our training in AI as we begin to implement more AI tools across the board. What should we keep in mind as we work on this “upskilling” effort?”

As more and more companies adopt AI tools, the need for “upskilling” is huge. But how do you do it in a way that’s effective, that equips people with the skills they need in a way that’s timely and relevant and without wasting time?

A report by BCG released last fall called upskilling “a major bottleneck” for companies that are trying to scale AI across their organizations. “Even though corporate leaders know it’s important, many have been slow to “provide people with opportunities to learn the skills required to use it,” the researchers wrote.

A survey of CEOs by PWC last year sounded a big alarm. The survey found that some 69 percent of CEOs believe that GenAI will require most of their workforce to acquire new skills over the next three years and some 74 percent said they are worried about whether their teams can “upskill fast enough” to keep pace with the innovations in AI.

“Without the right skills behind the scenes, even the most sophisticated AI deployments risk failure, either through underuse, misalignment with business goals, or erosion of trust within teams,”  wrote Kevin Dean, chief executive officer of an AI company, Manobyte, and an AI expert.

It’s not enough to think that AI expertise is supposed to live primarily within IT and engineering, he writes. “Today, AI touches every part of the organization. Executives must understand AI’s strategic opportunities and risks so they can lead AI-driven initiatives intelligently.”

“Managers need to know how to integrate AI into workflows and processes without overwhelming their teams. And frontline teams must be equipped not only with AI tools, but also with the training to use them confidently, responsibly and creatively.”

So how should you think about organizing training and upskilling efforts going forward? Kelly Heuer, vice president of learning for the Project Management Institute, suggests you keep three key principles in mind. The first, she says, is accessibility.

“Recognize the diverse skill levels within your organization,” she says. “Design training that avoids jargon and provides a clear, foundational understanding of AI concepts.”

It will pay to invest in digital learning platforms that will allow employees to access content at their own pace. “At PMI, we created separate AI e-learning modules to help project professionals sharpen their prompt engineering skills and use data responsibly, she says. “These are core skills that all AI users need to build but we customized them for project-based work.”

The second principle is to think about where AI is actually creating value. “Train for outcomes, not just awareness,” she says. “Skip the AI theory, and zero in on where it actually creates value. Whether it’s speeding up reporting, strengthening decision-making, or improving customer insights, tailor your training to high impact use cases that your teams care about. That’s where you’ll see the biggest return.”

And the third principle is key. “Integrate real-world relevance and foster initiative,” Heuer says. “Generic AI training often falls flat. Instead incorporate practical case studies that demonstrate how AI is being used within your company and in similar organizations within your industry.”

Take this opportunity to demonstrate how AI tools can enhance daily work through “self-directed learning and experimentation” in different departments, including marketing, project management, human resources, sales and finance, she says.

To add to your analysis, Allison Stevens, senior director of HR Solutions at Paychex, suggests that you take a look at how AI will be used to drive business outcomes in your organization. “This helps guide the identification of the specific skills needed to support the adoption of AI.”

To support all this, Stevens suggests that you consider upskilling your learning and development teams first to conduct a “gap analysis” to evaluate your company’s needs for upskilling more effectively.

Along with analyzing the gaps, she suggests doing a “skills inventory” to assess current capabilities across teams; doing a “role-based analysis” to determine how AI tools “will augment or transform specific functions and integrating AI competencies into development plans for employees to ensure that any upskilling efforts are aligned with individual and organizational plans going forward.

As you design or enhance an upskilling program, you’ll want to take care to avoid the pitfalls. One common one, Stevens says, is treating AI training as a “standalone” rather than integrating it into a broader business and talent strategy. “To avoid this, ensure that upskilling efforts are aligned with organizational objectives,” she says.

“Upskilling initiatives are like training for a marathon; you can’t go weeks in between training sessions because the muscle memory will be lost or diminished. “

Remember, too, that employees will need support, not only in learning AI tools, but in adapting to new ways of working. “The most common concern is employees being fearful that AI will replace their role vs. enhance it,” Stevens says.

The training that you implement needs to be integrated into your organization’s work, says Jaime Eisenhauer, chief people officer at Rochester-based Innovative Solutions. “Regardless of whether it’s technical or human centered training, a common mistake is not creating the space for the initiative to permeate into the organization,” she says.

“Allowing time for reflection, discussion, or trial and error after a training or workshop keeps the conversation going and doesn’t allow the training to ‘die on the vine.’”

Obviously, technical competence, including advanced programming skills, data modeling and machine learning concepts are incredibly valuable. But so too are the human capabilities which fall into two categories: relationships and resilience, Eisenhauer says.

“First, nothing in the workplace replaces positive and productive relationships. Skills like listening, collaboration, conflict resolution, self-awareness, giving/receiving feedback, and demonstrating empathy are critical to relationships both inside and outside the workplace. AI isn’t going to build a positive team culture or help a customer through a creative workshop process.”

The second category, resilience, requires such skills as “problem-solving, critical thinking, adaptability and embracing change,” Eisenhauer says. “Resilient individuals view challenges and changes around them as opportunities for growth and continuous learning, which is beyond the capabilities of AI.”

It takes strong leadership skills to help teams through all of this. It’s not easy to assess human capabilities but leaders who give meaningful feedback and provide opportunities for team members to grow and develop their skills are more likely to be successful. “Leaders who set expectations of specific behaviors required for their teams and then consistently demonstrate those behaviors themselves, will yield stronger, more resilient teams.”

Marisa Zdroik, head of people and organization consulting at NTT Data, agrees, saying their company believes in a blended approach, developing “soft skills” and technical skills in tandem. “While technical proficiency (especially in AI, data literacy and digital tools) is critical, it’s the soft skills – empathy, resilience, communication and collaboration – that determine how well those technical capabilities are applied in real-world contexts.

“We advise leaders to design programs where technical training is immediately followed by exercises that build human skills, such as problem-solving in diverse teams or navigating ethical dilemmas in AI use. That’s the future of holistic talent development.”

Managers at Work is a monthly column exploring the issues and challenges facing managers. Contact Kathleen Driscoll with questions or comments by email at [email protected]