Study finds two hours a week could close the AI skills gap

Despite high projections and strong investments, many organizations that went all-in on AI are seeing stalled adoption rates and poor results across the organization. According to a recent Study.com survey, 89% of workers reported using AI at work. Despite the widespread AI adoption, only 19% of respondents saw excellent results from AI use.

Executives may be quick to blame employee reluctance for the lack of growth on AI initiatives, but that might not be the full story. According to the survey, workers are eager to grow their AI skills but lack organizational support. Only 18% of workers say that their AI training prepared them to work with AI independently, and 49% say they are only prepared for basic AI tasks or not prepared at all.

The AI skills gap isn't an employee issue; it's an organizational one. Forty-six percent of workers say their AI skills were self-taught or learned through trial and error, while only 33% received formal training. Without a strong AI enablement strategy in place, organizations can waste money on AI initiatives that will continue to deliver poor results. Here's what CIOs can do today to help close the AI skills gap and prioritize AI enablement.

Employees need less time than leaders think

• Lack of dedicated learning time and insufficient training resources leave employees feeling underprepared to implement AI.
• Unclear expectations around AI use increase the likelihood of miscommunication and cause employee hesitation about when and how to use AI. According to the survey, 30% described their company's AI policies as slightly clear or not clear at all.
• Employee fear of making mistakes or violating policies makes employees feel it's safer to avoid AI than risk making mistakes. Only 29% of employees are confident they're using AI safely for sensitive, regulated work, according to the Study.com survey.
• A disconnect between AI strategy and day-to-day workflows leads to confusion about how to implement AI into daily operations.

AI adoption needs to be intentional, staggered and strategic to ensure that AI can be implemented and maintained for long-term success. "Trust is one of the biggest drivers of adoption, and trust is built when employees can see how AI helps them solve real problems in their day-to-day work," said Ashutosh Garg, CEO at Eightfold.ai. "When organizations create opportunities for people to learn, experiment, and apply AI in meaningful ways, adoption tends to accelerate naturally."

The cost of ignoring AI enablement

• Wasted resources. Organizations may pay for AI technology and infrastructure that remains idle when AI investments are underused.
• Stalled productivity. The right AI initiatives can create productivity gains that benefit employees and the business's bottom line, but without proper enablement, those gains go unrealized.
• Skill disparities. As AI use grows across industries, organizations that neglect AI enablement widen skill disparities across teams, which will only become more pronounced as AI continues to expand.
• Competitive risk. Competitors that invest in AI enablement can develop AI capabilities faster, putting organizations that ignore enablement at a disadvantage.

"The biggest risk is mistaking access for progress," said Orla Daly, CIO at Skillsoft. "Rolling out tools creates activity, but it does not guarantee better outcomes, better decisions, better work, or lower risk. Without the right training and guardrails, teams use AI inconsistently … That difference shows up in quality, rework, speed, and risk."

What CIOs can do right now

Organizations that don't invest in AI training and exploration now risk falling behind competitors and losing the adaptability and innovation that will help future-proof the business.

Here's what CIOs can do right now to help implement effective AI training for employees that satisfies leaders and meets employee needs.

• Create protected learning time. Formalize a few hours a week for AI exploration. Learning time should be integrated into the workday and treated as part of everyday work, not an extra task for employees. "Block time or stop calling it a priority. If AI training isn't on the calendar, it isn't real," said Schulze. "We run IT Focus Fridays every two weeks. Protected time to experiment, certify and build. No deliverables, no exception."
• Focus on practical use cases. Ensure training is relevant to employees by tying it to real job responsibilities and encouraging them to experiment with approved AI tools in their daily duties.
• Build champion networks. Identify employees who are ahead of the game with AI and create peer-to-peer learning opportunities between power users and employee groups. "When employees see peers using AI to solve real business problems, adoption becomes less theoretical and more tangible," said Garg. "Trust grows when people can see practical examples of success in action."
• Measure more than access. Track metrics that go beyond the number of licenses and users, such as usage, productivity improvements and skill development.

"Most employees are willing to experiment with AI, but they need opportunities to learn, test, share knowledge, and understand where it fits into their daily work," said Nina Tatsiy, CIO at Quadient. "We recently introduced dedicated self-development time for our technology teams, including protected hours for education and experimentation. Even a couple of hours each week can make a meaningful difference in helping employees stay current and become more comfortable applying AI in practical ways."

Organizations don't necessarily need larger training budgets. Instead, CIOs should allocate more time and attention to AI training to turn employee enthusiasm into measurable business value.