The AI skills gap is organizational, not individual. Only 18% of workers feel prepared to use AI independently, with 46% relying on self-teaching rather than formal training.
Employees need minimal time investment. Research shows that67% of workers need just 2 hours or less per week to effectively develop their AI skills.
Ignoring AI enablement is costly. Without proper training, organizations waste resources, limit productivity, widen skill gaps and face competitive disadvantages.
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
Many executives believe organization-wide AI training is too costly, time-intensive and complex to make a meaningful investment in it. However, 67% of workers say just two hours per week or fewer would help develop their AI skills.
"One of the most common mistakes I see is organizations declaring AI a priority without creating any structural space for learning," said Trevor Schulze, CIO at Genesys, a software development company. "You can't ask people to develop new skills in between meetings and expect it to stick. On my team, we've created dedicated days focused on AI exploration, where people step away from deliverables, meetings and inboxes to experiment, learn and apply AI to real business problems."
For most organizations, two hours per week of tailored AI training can help future-proof the workforce by building the right AI skills. According to the survey, employees want training on real-life scenarios and implementation, guidance on checking AI accuracy and clear rules for safe use.
Leaders may view AI training as a costly, overly complex strain on an already tight budget. However, training doesn't have to be time-intensive. A few hours of practical AI training is a relatively modest investment for an organization when compared to the risks of not training employees on AI usage.
Why AI adoption stalls despite growing interest
Although the survey shows that employees are willing and able to build their AI skills and integrate AI into their daily work, and despite leaders' desire to implement AI across workflows and operations, AI adoption still stalls in many organizations.
"In my experience, lack of skilling and enablement is a much bigger barrier than outright resistance," said Schulze. "When people understand how AI can augment their work, eliminate repetitive tasks, and help them make better decisions, adoption tends to accelerate. When organizations simply hand out licenses and hope people figure it out, usage stalls and value remains isolated in small pockets of the business."
According to a recent TalentLMS report, 83% of HR managers believe their companies actively support employees in learning AI. However, only 64% of employees agree. Without a meaningful AI adoption strategy, delays in implementation and adoption can occur due to challenges, such as:
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
Ignoring AI enablement can be costly, leading to reputational, legal, and operational consequences.
Without the right infrastructure and safeguards, allowing AI use in the organization without structured enablement can lead to costly mistakes and disruptions, including:
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."
One of the most common mistakes I see is organizations declaring AI a priority without creating any structural space for learning.
Trevor Schulze, CIO at Genesys
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.
Alison Roller is a freelance writer with experience in tech, HR and marketing.
