Summary: The problem of slow AI adoption often lies in four common training mistakes: unclear usage guidelines, overly theoretical content, insufficient collaboration, and missing career context. Effective AI training requires clear guardrails, personalised content rooted in real ROI, collaborative learning formats, and connection to career development. When training addresses these elements, teams engage more readily with AI tools.
Despite investment in AI, many companies are experiencing stalled or slow adoption. According to EY’s Work Reimagined Survey from November 2025, 88% of employees are using AI at work, but only for basic tasks. Only 5% report ‘maximizing AI to transform their work.’
Despite increased spending, there’s no experimentation, no fresh ideas, and a distinct lack of enthusiasm for emerging technology. This is not an environment where AI adoption thrives, and it’s leaving organisations lagging.
If this sounds familiar, the root of the problem may not lie in the amount of training provided, but in four common mistakes.
1. No clear guardrails
It may feel counterintuitive to expect more rules to lead to increased adoption, but clear limits allow for better experimentation and ease of use. Employees feel reassured that they’re not inadvertently breaking rules, which raises their confidence in the tools they’re using.
For example, the sensitive nature of employee data makes HR teams cautious. Rules that explicitly ban entering personal employee information into AI tools, while encouraging their use for policy drafting, job descriptions, and training materials, allow HR to benefit from AI without putting confidential information at risk.
AI training should equip employees with the knowledge they need to make judgment calls about usage. Such as:
- Which data is safe to input into which tools
- Which tools should be used internally only, versus customer-facing
- When human approval is needed
- When generative AI is acceptable
- Which workflows/tool configurations can be amended
- Who the owner of each tool is
Providing teams with a clearer sandbox to play in builds confidence and fosters a culture of experimentation where adoption thrives.
2. Too theoretical
‘AI for the sake of AI’ is a major pitfall, and one that employees tend to be suspicious of. Training that focuses on theory and ‘big picture’ thinking, but fails to get into day-to-day impact and ROI, doesn’t encourage engagement.
To fix this, offer training that’s as personalised as possible, by role, department, or management level. AI-powered learning management systems can assist with delivering personalised training, without adding significant administrative lift.
For many employees, especially those in non-technical roles, AI may still seem abstract and mired in myth. Rooting the training in reality by aligning what people are learning with real-world results provides key context and boosts engagement. It also makes the tools far more attractive and more likely to be used after employees have completed their training. This is why talking about ROI is key.
For marketing teams, that might look like reduced campaign turnaround time, higher content output per marketer, improved conversion rates, and lower cost per acquisition. For engineering teams, it could be faster cycle times, fewer production defects, and reduced rework.
Human impact can be just as inspiring as quantitative metrics, and should also be included in AI training materials. For example, at Deel, AI-powered petition drafting decreased the processing time of certain types of US visas from 30 days to just 5. The human impact of speedier visas is what makes this a use case worth highlighting as much as the efficiency boost.
3. Not collaborative
Learning content needs to be personalised, but the experimental nature of AI means that teams benefit most when learning is a collaboration. While there are straightforward aspects of AI (such as usage policies, data privacy, AI literacy, and prompt engineering), training that consists of strict how-tos limits its potential. Offering collaborative learning formats that encourage experimentation is a good way to foster an AI-enabled culture in the long term.
Blending formal and informal learning allows teams to learn the basics while also benefiting from shared group experiences. This could be a self-guided course within an LMS, coupled with more open-format office hours. Or specific enterprise training, followed by an in-house hackathon. This creates opportunities for people to ask questions.
4. Missing career context
It’s not just beneficial for organisations to train employees in AI for the short term. With AI being as transformational as it is, workers need to learn at least the basic skills to stay competitive in the job market. Deel platform data, which includes employees and contractors from over 35,000 global companies, shows a 585% increase in ‘AI’ job titles since 2023, and that median AI salaries are now 120% higher than all other roles.
By putting these new skills into the broader context of the shifting careers landscape, teams will bemore likely to learn and use them, rather than seeing training as an obligatory tick-box.
Fostering an environment of learning for AI
The final key to AI learning is creating an environment where people feel comfortable asking uncomfortable questions. AI is a loaded topic, prompting ethical, environmental, and job security concerns. Rather than shying away from these issues, addressing them head-on creates clarity and builds trust.
A culture of psychological safety is paramount for ensuring everyone can engage with the learning materials, including skeptics. Feedback loops, anonymous reporting systems, and the active welcoming of different opinions contribute towards this. Ultimately, AI learning only succeeds in environments where people feel safe to question, challenge, and learn without fear of penalty.
Key takeaways
If your AI training isn’t driving adoption, consider these approaches:
- Establish clear guardrails. Define which data is safe to input, which tools need human approval, and when generative AI is acceptable. Clear limits build confidence and encourage experimentation.
- Root training in real-world impact. Move beyond theory by showing role-specific ROI. Share quantitative metrics like reduced turnaround times alongside human impact stories that make the benefits tangible.
- Blend formal and informal learning. Combine structured courses with collaborative formats like office hours or hackathons. This allows teams to master basics whilst experimenting together.
- Create psychological safety. Address uncomfortable questions about ethics, environmental concerns, and job security head-on. Welcome scepticism through feedback loops and anonymous reporting to build trust and genuine engagement.
Support education with Deel
Delivering personalised training to teach a wide range of skills in a fast-changing technology environment is no small feat. With Deel’s LMC, you get:
- AI-Powered Course Creation: Build, manage, and track engaging training courses in minutes.
- Comprehensive Tracking & Reporting: Monitor completion rates, engagement, and alignment with organisational competency frameworks.
- Custom Course Development: Create training tailored to your AI adoption strategy, including your usage policies, data privacy rules, and in-house tools.
- Integration with Career Paths: As AI shapes career paths, your training can link directly with performance and career growth efforts.
