Fewer than half of all corporations have created AI policies for their employees to follow. But around half say they’re putting investment dollars into AI in the coming year, a sure sign that most are eager to catch up and take advantage of AI’s benefits.

There’s also a concern, though, that employees are losing focus and getting overwhelmed by the expectation to use AI for work.

AI is touted as a means to make some jobs easier and more efficient, but that’s not proving to be true in every case. Some workers are experiencing what’s being called “AI brain fry”, which is just one of the things leaders got wrong about AI .

People are reporting that they aren’t sure how to use AI software . As a result, AI becomes more of a frustration — or even embarrassment. Employees feel they’ve somehow failed if they aren’t able to leverage AI to improve their productivity and efficiency. This leads to mental exhaustion and an “I give up” attitude that holds them back.

I know how hard the learning curve to adopt AI can be. There are so many types of AI and products on the market, and some are definitely more user-friendly than others. However, I also believe that teams can go the distance with AI (and avoid brain fry) if they put a few strategies into place.

1. Foster an adaptable mindset

One major obstacle to widespread AI use across organizations is a willingness to embrace change. Switching to AI means altering someone’s ordinary and comfortable work habits. That’s a big ask. This is where giving your employees the skills they need to be more mentally adaptable can help. Otherwise, they may have trouble stretching themselves cognitively and end up feeling cognitively depleted.

It’s easy to think of apps as a cost-effective and easily accessible tool. But relying on team members to actually use them and evaluate their impact is another challenge. This is where employers might want to consider shifting their focus on more holistic solutions for performance skill building. For instance, Q Studio offers human performance development training for employees at all levels through its Mind Skills curriculum. The lessons are designed to improve individual performance through cognitive-behavioral training.

If an app is the preferred solution, Clockify is designed to help teams manage their time more effectively, which can create more mental space and reduce the stress that comes from feeling overloaded. When employees have a clearer structure for prioritizing and completing their responsibilities, they’re better positioned to adopt new practices and adapt to change. You can also complement these efforts by encouraging resilience-building practices, such as assigning resilience-focused books or resources to team members.

Not everyone spends their time fiddling with AI platforms like CoPilot or ChatGPT. That’s why you can’t expect your employees to bring an innate understanding of AI best practices into the workplace.

Instead of just assuming everyone in your company is competent in AI, get them all baseline training. (And do it right away if you’re investing in a new technology that’s going to be deployed across your whole organization.) Microsoft offers an AI for Beginners course , but that’s just the beginning of all the training and certificate programs you can find.

A word of caution: Do your homework and survey your teams. You’ll want to make sure you educate them on the AI you’re using because every AI tool is different. Some of them are very niche, such as AI agents programmed for specific use cases or industries. Consequently, the prompts that work for one AI product won’t necessarily work for another. Knowing this upfront will help alleviate employees’ concerns that they’re “doing it wrong”.

As your team gains momentum and starts to see AI as an asset, you may think it’s time to expand into different AI arenas. Before you bring other AI tools into your workforce mix, put them through some tests.

For example, you may want to beta drive a promising piece of AI software within a team to see if it actually adds value. Even if it claims to be the “next big thing”, act with caution and deliberation. Otherwise, you might end up back at square one with employees who aren’t quite ready to bring another tool into their work.

You may also want to run any new AI tools through a litmus test before they make it to the beta test stage. Are they actually different from the AI you currently use at your company? Could your existing AI software be trained to offer similar support to your team? Will they work with your other AI tools? By probing deeply, you can avoid making your situation worse.

I’m a huge proponent of trying AI. At the same time, I encourage leaders to put their people’s needs first, starting with acknowledging the risk of AI-related brain fry and putting mitigating strategies into play.