How to Turn AI From Threat to Teammate — 3 Proven Ways to Align Your Vision with What Employees Actually Need

Word on the street right now is that the executives who see AI as just another tool are already behind. In an effort to stay ahead of the game, many are jump-starting wider company AI programs, embedding it into strategic decision-making and embracing the idea of a “digital teammate” that works alongside their employees. The problem is, while AI may be positioned at the center of boardroom conversations, that mindset isn’t consistently reaching the rest of the organization.

According to Slingshot’s Digital Work Trends Report, 86% of C-suite executives believe AI usage is required in their company operations, yet fewer than half (49%) of middle managers are reinforcing that expectation with their teams. This gap reveals a broader disconnect between executive ambition and day-to-day execution. AI may be a part of workplace strategy, but for many employees, it still feels optional and disconnected from how their performance is actually measured.

As CEO of Infragistics, I’ve seen firsthand how a strategy that is agreed upon by the executive board can lose weight when passed down the line if goals aren’t communicated to teams properly. Leaders invest in technology and have an image of how it will completely transform their company. But if those priorities aren’t transparently shared or woven into how teams actually work, the dream will never become reality.

Here are three reasons the AI mandate isn’t sticking — and what organizations can do to close the gap.

AI strategy is top-down, but adoption is bottom-up

Executives can declare AI mandatory, but without middle managers translating that mandate into actionable guidance, adoption often stalls.

For managers who already have so much on their plates, learning a new tool and then not only teaching others how to leverage it but also monitoring them to make sure they are using it correctly may feel like more trouble than it’s worth. Especially if they aren’t seeing immediate results. Similarly, many employees feel comfortable in their ways and, as a result, aren’t leaning into AI use despite its potential.

What managers and employees alike don’t necessarily understand is that AI won’t show productivity gains overnight. Slingshot’s report found that only 2% of employees believe they can’t do their job without AI. And executives don’t want them to. The reality is that AI needs to be combined with human intelligence — and training the AI on industry expertise takes some time. The 54% of employees who believe AI is helpful but not critical can see its potential; they just need the education to understand how to take it a step further.

That’s where higher executives come in. Before full AI adoption can be trickled down to the entire organization, middle managers need to be equipped with tailored AI training, like role- or team-specific examples, and clear performance expectations. Managers should understand how to use AI themselves and also how to coach their teams on integrating the tools into daily routines. This includes clarifying which tasks AI should support, how to train AI for optimal results — going beyond generic prompts — and how AI fits into performance metrics. When that happens, they’ll be able to properly educate and help employees. From there, teams will gain confidence and adoption will spread more organically.

Companies talk about AI, but not about data behind it

The gap between what AI could do and what it actually does often comes down to a disconnect between available data and employee comfort using it. AI can only be as effective as the information it’s trained on, yet many employees don’t feel confident using data in their day-to-day work. A total of 70% of executives believe employees are constantly relying on data to make decisions, but only 31% of employees say they actually do. Many still lean on personal experience (29%) or wait for a data analyst (27%) to provide insights.

Data readiness challenges also go 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, organizations should start by making data literacy a core part of AI adoption. Employees need practical guidance on what data is available, where it lives and which sets AI actually needs access to for actionable insights. Training should connect directly to real workflows, like showing how AI can automatically summarize project timelines in order to identify where resources are over-allocated, so employees see tangible benefits and learn by doing.

Fear and ambiguity are slowing adoption

Even younger employees, who tend to be more open to new technology, see AI’s collaborative potential as a competitive threat. Nearly 1 in 5 (19%) Gen Z employees and about 1 in 6 (17%) millennials worry that AI could replace them.

Part of this problem comes from mixed signals from leadership. Executives may talk about AI as a teammate, but if they don’t clearly define what AI should handle versus what humans should own, employees are left guessing. Without that clarity, some may hesitate to experiment with the tools, while others may use AI in ways that aren’t aligned with team goals or best practices.

The key is setting clear boundaries and expectations. Leaders need to spell out which tasks AI supports — like analysis and identifying patterns in data — and which should be left for humans, such as strategy and creative decisions. Organizations should also normalize the conversation around AI use, discuss successes and challenges when using it, and highlight where human judgment was necessary.

AI transformation isn’t achieved through executive mandates alone. It happens when strategy is paired with organization-wide transparency and education. When organizations align leadership vision with the realities of managers’ and employees’ everyday realities, AI stops feeling like a mandate and starts becoming part of how work gets done.