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Key insights
- Executives can make AI mandatory, but without middle management translating that mandate into actionable guidance, adoption often stalls.
- The gap between what AI can do and what it actually does often stems from a mismatch between the data available and employees’ comfort level with using that data.
- Fear and uncertainty are slowing the introduction of AI. It’s up to leaders to be clear about how they want to use AI in their company and to reassure employees that they won’t be replaced.
The current narrative is that executives who view AI as just another tool are already behind the times. To stay one step ahead, many companies are launching comprehensive AI programs, embedding them into strategic decision-making and embracing the idea of a “digital teammate” who collaborates with their employees. The problem is that while AI is at the forefront of boardroom conversations, that mindset doesn’t consistently reach the rest of the organization.
According to Slingshot’s Digital Work Trends Report, 86% of C-level executives believe the use of AI is necessary in their company operations, but less than half (49%) of middle managers reiterate this expectation to their teams. This gap reveals a greater disconnect between leaders’ ambitions and day-to-day execution. AI may be a part of workplace strategy, but for many employees it still feels optional and unrelated to how their performance is actually measured.
As CEO of Infragistics, I have seen firsthand how a board-agreed strategy can lose weight when passed down if the goals are not properly communicated to teams. Executives invest in technology and have a vision of how it will completely transform their business. But if these priorities are not transparently shared or integrated into how teams actually work, the dream will never become a reality.
Here are three reasons why the AI mandate isn’t sticking – and what companies can do to close the gap.
AI strategy is top-down, but adoption is bottom-up
Executives can make AI mandatory, but without middle management translating that mandate into actionable guidance, adoption often stalls.
For managers who already have so much on their plate, learning a new tool and then not only teaching others to use it but also monitoring them to make sure they’re using it correctly can seem like more effort than it’s worth. Especially if they don’t see immediate results. Likewise, many employees feel comfortable in their own way and reject the use of AI despite its potential.
What managers and employees alike don’t necessarily understand is that AI doesn’t lead to productivity gains overnight. Slingshot’s report found that only 2% of employees believe they can’t do their jobs without AI. And managers don’t want that. The reality is that AI needs to be combined with human intelligence – and training AI to have industry expertise takes time. The 54% of employees who believe AI is helpful but not critical can see its potential; They just need the training to understand how to take it a step further.
This is where senior managers come into play. Before full AI adoption can be scaled across the entire organization, middle managers must be equipped with tailored AI training such as role- or team-specific examples and clear performance expectations. Managers should understand how they can use AI themselves and how they can coach their teams to integrate the tools into daily operations. This includes clarifying what tasks AI should support, how to train AI to achieve optimal results – beyond general prompts – and how AI fits into performance metrics. When that happens, they can properly train and help employees. From there, teams will gain confidence and adoption will spread more organically.
Companies talk about AI, but not about the data behind it
The gap between what AI can do and what it actually does often stems from a mismatch between the data available and employees’ comfort level with using that data. AI can only be as effective as the information it is trained on, yet many employees do not feel confident using data in their daily work. A total of 70% of leaders believe employees constantly rely on data to make decisions, but only 31% of employees say this is actually the case. Many still rely on personal experience (29%) or wait for a data analyst (27%) to provide insights.
Data delivery challenges also extend beyond skills. In some organizations, data is unstructured, spread across multiple systems, or poorly documented. Employees may also not even know what data exists, let alone how to apply it to their workflows.
To fix this problem, companies should start making data literacy a core part of AI adoption. Employees need practical guidance on what data is available, where it is stored, and what framework the AI actually needs access to in order to generate actionable insights. Training should be directly related to real-world workflows, such as how AI can automatically summarize project timelines to identify where resources are overcommitted so employees see tangible benefits and learn by doing.
Fear and uncertainty slow down acceptance
Even younger employees, who tend to be more open to new technologies, see the collaborative potential of AI as a competitive threat. Nearly one in five (19%) Gen Z employees and about one in six (17%) Millennials fear AI could replace them.
Part of this problem is due to mixed signals from leadership. Leaders may talk about AI as teammates, but if they don’t clearly define what the AI should handle and what humans should own, employees will be left in the dark. Without this clarity, some may be hesitant to experiment with the tools, while others may use AI in ways that are inconsistent with team goals or best practices.
The key is to set clear boundaries and expectations. Leaders need to outline which tasks AI supports – such as analyzing and identifying patterns in data – and which tasks should be left to humans, such as strategy and creative decision-making. Organizations should also normalize the discussion around AI deployment, discuss successes and challenges in deployment, and highlight where human judgment is required.
AI transformation will not be achieved through leadership mandates alone. This happens when strategy is paired with company-wide transparency and education. When companies align their leadership vision with the realities of managers’ and employees’ everyday lives, AI no longer feels like an assignment but becomes part of the way work gets done.
Key insights
- Executives can make AI mandatory, but without middle management translating that mandate into actionable guidance, adoption often stalls.
- The gap between what AI can do and what it actually does often stems from a mismatch between the data available and employees’ comfort level with using that data.
- Fear and uncertainty are slowing the introduction of AI. It’s up to leaders to be clear about how they want to use AI in their company and to reassure employees that they won’t be replaced.
The current narrative is that executives who view AI as just another tool are already behind the times. To stay one step ahead, many companies are launching comprehensive AI programs, embedding them into strategic decision-making and embracing the idea of a “digital teammate” who collaborates with their employees. The problem is that while AI is at the forefront of boardroom conversations, that mindset doesn’t consistently reach the rest of the organization.
According to Slingshot’s Digital Work Trends Report, 86% of C-level executives believe the use of AI is necessary in their company operations, but less than half (49%) of middle managers reiterate this expectation to their teams. This gap reveals a greater disconnect between leaders’ ambitions and day-to-day execution. AI may be a part of workplace strategy, but for many employees it still feels optional and unrelated to how their performance is actually measured.
As CEO of Infragistics, I have seen firsthand how a board-agreed strategy can lose weight when passed down if the goals are not properly communicated to teams. Executives invest in technology and have a vision of how it will completely transform their business. But if these priorities are not transparently shared or integrated into how teams actually work, the dream will never become a reality.