Research Drop in Brief:
- 39% of workers' existing skillsets will become transformed or outdated over the next five years, emphasizing the need for organizations to keep up with skill demands.
- Despite the rise in AI use, 70% of organizations report struggling to equip their workforces with the necessary AI skills, and 62% of leaders believe their organization has an AI literacy skill gap.
- When employees feel adequately trained in AI, they are 1.9x as likely to report realizing the value of AI, such as improved decision making.
- To close the gap, organizations need to focus on providing equitable access to training opportunities and helping their employees dedicate time for upskilling and continuous learning.
With rapid workplace changes, 39% of workers’ existing skillsets will become transformed or outdated over the next five years1, making it imperative for organizations to keep up with the demands of this revolutionary workforce change. One of the significant macrotrends impacting work is the introduction of generative AI to the workplace, such as Microsoft Copilot. The integration of AI tools and technologies continues to grow across industries and roles, directly altering the way employees across the planet execute their daily tasks.
Three in four organizations (77%) are planning to reskill/upskill their workforce in the next five years in order to better work alongside AI1. Leaders acknowledge that equipping employees with the skills they need to fully optimize AI at work can be tricky – 31% of leaders report a main barrier to reskilling/upskilling their workforce is not knowing where to start with data and AI training2, which can cause lag in scaled organizational development. And we see consistently that employees’ excitement for AI at work outpaces the ability for their organizations to roll out organization-sponsored AI tools3, which can lead to inconsistent adoption and misaligned AI usage. For this reason, now is the time for organizations to invest in enterprise-wide AI training and learning opportunities.
This month’s research drop explores the current AI skill gap, the impact of AI-specific training, and strategies for organizations to better equip their employees to succeed in the era of AI.
The reality of the AI skill gap
The AI skills gap is increasingly prevalent – 70% of organizations report struggling to equip their workforces with the skills they need for future success4, 62% of leaders report their organization has an AI literacy skill gap2, and 39% of CEOs report low levels of confidence about their employees having the right skills to fully maximize AI benefits5.
The demands of leading through AI transformation can feel overwhelming, involving high-effort experimentation and comprehensive organizational strategies for deployment. The pace of change in the current workplace continues to be faster than ever, with employees simultaneously feeling the challenge of keeping up and excitedly leaning in to experiment with AI before their organization officially adopts. Leaders are in a difficult situation of needing to support employees encompassing a wide variety of stages in their AI journey.
And employees are craving more opportunities to learn. Nearly half of employees feel they are receiving moderate or less support for AI capacity building at their organization6, which can directly impact their ability to dedicate time for learning and upskilling. A strong organizational investment in training and learning opportunities is needed to help bridge the gap.
Differentiating value of AI-specific training
48% of employees rank training as the most important factor for AI adoption6. And for good reason – at organizations with enterprise-wide data and AI literacy programs, the results are striking. Within these organizations, 90% report faster decision-making, 81% report increased revenue, and 81% report better employee retention2.
Training is a key input to the adoption and usage of AI. We found that while 43% of employees who feel adequately trained are high frequency users of AI (at least once per day), less than 1% of employees who don’t feel adequately trained are high frequency users of AI. This means nearly no one fully adopts what they don’t feel equipped to use, and employees missing that foundational training may not be trying or leveraging AI at all. We’ve seen in previous research that upskilling can improve confidence, encourages play and experimentation, and drives behavior changes, further underscoring that adequate training is a key ingredient in driving AI use, adoption, and ultimately, transformation.
Training also impacts employees’ conviction in their future in an AI-first organization. Employees who feel adequately trained in AI are more likely to agree that they have irreplaceable skills (79%), compared to employees who don’t feel adequately trained (68%). When employees are confident and comfortable with AI technology, they better see the vision for how they can collaborate with AI, rather than compete with it.
And once employees are trained and leveraging AI, they also become more likely to realize the benefits of using AI at work. In our data, we see that employees who feel adequately trained in AI are 1.9x as likely to report realized individual value of AI (RIVA) outcomes.
Our RIVA scale includes positive AI outcomes such as employees reporting reduced stress, better decision making, and higher quality work output. These outcomes are critical to not only fully maximizing and seeing the ROI of AI at work, but also to reinforce the utilization of AI to complete daily work tasks.
The lagging state of organization-wide AI-specific training programs
Unfortunately, although we see the benefits training can bring, training opportunities can be hard to come by. One report shows that only 49% of employees have received training in AI and that 57% of employees report feeling “behind” in keeping up with AI7. Employees worry that this might negatively impact their organization’s future – 1 in 3 employees report that a lack of AI-specific training is the biggest barrier to their workforce being prepared to leverage AI4. So, what’s going on? In our dataset, two main obstacles to training confidence emerged: accessibility of training and capacity to engage in training.
Accessibility of training
As many organizations are still in the planning and early implementation stages of AI, their training programs are as well. Many employees report that training feels exclusive to certain organizational groups. Only 16% of employees report that their organization offers enterprise-wide AI training, while 77% report that their organization either offers no training or only offers it to a selective group of employees4. Some of this may be aligned with pilot programs in the early stages of AI transformation, where training might be limited to those with early access licenses. These trainings may also be limited to groups the organization have selected due to their outspoken excitement, hand raising, or opt-in processes.
When organizations take a proactive approach to providing AI tools to their employees, employees are far more likely to feel equipped to use them. We found 80% of employees with high access to company-provided AI tools agree that they are adequately trained in AI, compared to just 60% of those with low access to company-provided AI tools who are left to navigate AI on their own – a 20-percentage point difference.
The groups most likely to be given access are those teams that are more likely to be closely involved in the AI transformation initiatives. In our dataset, the departments with the highest access were Product Development and IT. While it is important that those directly involved in the transformation strategy have tool and training access, the rest of the organization may feel left out and fall behind, resulting in a lack of cross-functional use cases and deepened skills gaps throughout your organization.
Capacity to engage in training
It’s not only the accessibility of training that impacts whether an employee feels skilled in AI, however. There are individual restrictions that may hinder upskilling opportunities. A key blocker can be that training takes time. For an employee to engage in training, they need to have the capacity to dedicate work hours to participate, to expend focus time on learning, and to commit to experimentation to practice the learnings. When an employee’s workload is too high, or their leader doesn’t support dedicated learning time, they may not benefit from available training.
We also find that employee burnout had a direct impact on training confidence. When burnout is high, training confidence plummets. While 83% of employees with no burnout indicators feel adequately trained, that number drops to 55% for employees that have three or more burnout indicators – an almost 30-percentage point difference. Burnout indicators include overwhelming workload, little or no support from managers/peers, and unclear job responsibilities.
Employee burnout is a sign that something needs to be addressed in the workplace to best set employees up to be happy and successful. Employees who feel burnt out have a hard time feeling prepared and trained to integrate AI into their work. If you know your organization already struggles with burnout or dedicating time for learning, consider how you can work AI training into role or function-specific time. When leveraged properly, AI can reduce burnout by reducing time spent on tasks or by helping employees make informed decisions. If an employee is already burned out, they don’t have the capacity to attend training or to learn and that generates a negative feedback loop. Therefore, it’s important for organizations to understand that it’s not only about providing training opportunities that lead to upskilling/reskilling but also supporting employees who want to invest their time and capacity to engage in the training.
Strategies for organization-driven AI upskilling/reskilling
Employees are craving training opportunities to help them close the AI skills gap, so how can organizations be better equipped to meet this need? Our research shows that organizations can help employees “future-proof” themselves by ensuring equitable access to training and by helping employees invest in training opportunities.
If the goal of your organization is enterprise-wide AI adoption, then training also needs to be scaled throughout the organization, whether through network-based sharing or democratized access to training resources. Consider where you may have gaps in developmental opportunities or accessibility of learning. Are there departments who haven’t been provided with training yet? Some employees may even be unaware of what opportunities are open to them. Focus on clear company communications that share the location and availability of training, learning paths, and skill-building sessions. While some employees may proactively look for these opportunities, others may need more direct instructions on how to upskill themselves.
It's also helpful to brainstorm scalable strategies for training. For example, can you leverage AI champs to conduct training? Perhaps they can help create a “training-in-a box” kit which can be adapted by function or region to leverage the learning communities that already exist in your organizational network. AI-specific training should slot into your continuous learning strategies, where an emphasis on cross-team sharing, learning, and experimentation helps your workforce keep up with the pace of change.
Employees also need leadership and managerial support to engage in training. Workloads can be high and bandwidths tight, so employees may not feel as though they have the capacity to focus on learning. Consider a monthly calendar hold for your team to dedicate the time to engage in learning. Include training or structured L&D programming as a team-wide goal to measure for the quarter, raising its importance to that of other team outputs. Employees need to feel safe and supported to prioritize their development and are likely to look to their leaders to model this behavior. This can also be a key moment where leaders can help connect the dots between individual roles and organizational strategy, further helping employees to invest in the vision and future of AI at your organization.
To conclude, organizations should deeply invest in AI training opportunities and support employees in dedicating time to upskill. By promoting continuous learning and addressing factors such as burnout, companies can prepare their workforce for the future. A well-trained and supported team is essential for successful AI transformation.
Stay tuned for our May Research Drop to keep up with what the Microsoft People Science team is learning!
This month’s research drop analyzed 1,800 global employees from the Microsoft People Science AI Readiness Survey from April 2024. Note: participants were asked to respond to questions around “generative artificial intelligence” which has been shortened to “AI” for the sake of this blog.
1 World Economic Forum. (January 2025). Future of Jobs Report 2025.
2 Datacamp. (2024). The State of Data & AI Literary Report 2024.
3 Microsoft People Science. (April 2024). The State of AI Change Readiness.
4 i4cp. (2025). Workforce Readiness in the Era of AI.
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