6 ways to prevent AI fatigue

AI tools often claim to make our work (and our lives) easier, faster, lighter. Instead, many of us are feeling in the dark and, at the same time, sprinting to keep up – juggling tools, prompts, and pressure to “leverage” all that functionality, all at once. The result? AI fatigue. That drained-yet-frantic feeling of trying to stay relevant in a system that never stops changing.

Is the solution to stop using AI altogether? Probably not, considering AI can be such a powerful unlock. Instead, the solution lies in how we’re collaborating with it. Just like email overload wasn’t really about the technology itself – it was about how we let it structure our days and trigger our brain’s dopamine-driven urges – AI fatigue comes from letting the tools lead instead of being intentional about when, why, and how we collaborate with them.

As historian Melvin Kranzberg once said, “Technology is neither good nor bad; nor is it neutral.” AI is no different. It reflects our habits, intentions, and decisions. If we don’t pause to question how and why we’re using AI, we risk letting it dictate our priorities – and even our thinking – in ways we never intended. Every interaction with AI is a chance to reinforce or rethink the systems we’re building.

But the fix isn’t just another productivity hack. It’s learning to set up the right systems – and learning to engage with AI intentionally, not reactively.

Here are six dos and don’ts – informed by Atlassian’s latest research – to help reset your relationship with AI, turning it from a source of fatigue into a source of creativity and impact.

1. Do this: Take full advantage of AI to solve real problems

Not this: Use AI as a simple tool

“AI isn’t just a button you press for instant answers,” explains Atlassian AI Evangelist Sven Peters. To get real value, Sven says it helps to treat AI as a sparring partner – someone you can bounce ideas off, challenge your assumptions, and uncover new perspectives.

If AI gives you an unexpected answer, don’t just move on. Instead, ask “why?” and dig deeper. This back-and-forth can reveal gaps in your thinking and help you learn much more than you would by simply accepting the first response.

For example, instead of asking AI, “What’s the best way to run a meeting?” try: “Here’s how my team runs meetings now… what’s missing? What would you change, and why?” Then, challenge its suggestions and dig deeper. That’s how you turn AI into a true collaborator that creates real value.

2. Do this: Save time by making sure AI can access high-quality knowledge

Not this: Feed AI any and all information indiscriminately – you run the risk of getting “slop”-y outputs that you then need to sift through for the answers you’re after

AI can only action what it can access. If you give it incomplete, outdated, or poorly organized information, it will amplify those flaws, degrading decision-making and causing headaches down the road.

According to our research, 79% of knowledge workers say they’d use AI more if it could access the right data. But most are held back by messy, siloed, or low-quality inputs. Teams that see the greatest AI-enabled impact make high-quality, up-to-date knowledge available to AI. Learn how to do this in our AI Collaboration Index 2025.

3. Do this: Protect your focus by assigning AI boundaries (and acknowleding its limitations)

Not this: Let AI dictate your entire workflow or interrupt deep work

At the start of every project, figure out exactly what role AI will play. For example, a team might decide that AI will analyze customer feedback trends, create the first draft of a project plan, and update Jira issues. You might even ask AI to suggest ideas for how it can best help you.

Make it a habit to regularly (monthly or quarterly) review how AI is showing up: Is the way you’re collaborating with it genuinely helping you achieve results, or just adding noise and work slop? Think of AI as a teammate to recalibrate with, not a set-it-and-forget-it solution.

Also, remember that speed isn’t everything: 42% of knowledge workers admit to trusting AI outputs without checking. Take a beat and check AI outputs – your future self and your team will thank you.

4. Do this: Create active communities for hands-on learning and experimentation

Not this: Rely on passive training sessions or static knowledge hubs to get good at AI

Our research shows that nearly 70% of workplaces offer AI training, but it’s largely ineffective. The most impactful learning happens in small, active communities or hands-on workshops focused on solving specific, shared problems.

Hands-on AI workshops, hackathons, community learning sessions, and dedicated Slack channels drive real adoption and confidence – not to mention they’re just more energizing and fun. Meanwhile, one-size-fits-all trainings and self-serve hubs are among the least effective ways to spark strategic AI collaboration. (And they’re kind of a snoozefest.)

5. Do this: Add context to your prompts to get sharper results

Not this: Get lazy when prompting

The words you choose in your prompts matter. A lot. If you add context and constraints to your prompt, you’ll spend less time sifting through fluff. It only takes a few more moments to give more precise direction. For a great prompt, do this:

• Assign AI a role: “You are a content marketing manager”
• Tell it your desired outcome: “I want you to write LinkedIn ad copy. Give me 10 options.”
• Give it constraints: “Make the headline 10 characters max, and the body copy 20 characters”
• Share what info or knowledge it should use to respond: “Pull from this Confluence page that details our core campaign messaging”

6. Do this: Put AI to work on your biggest team challenges

Not this: Just use it to rewrite your emails

Sure, AI can make your Slack message sound better, but that’s just scratching the surface – and ultimately won’t help you achieve your goals. The real magic happens when you work with AI to break down silos, connect teams, and uncover insights that actually move the needle. According to our research, organizations that focus AI on personal productivity are 16% less likely to drive innovation compared to those that use AI for cross-team coordination and mission-critical challenges.

Want real impact? Put AI to work on things like analyzing customer feedback for new ideas, connecting projects across teams, or flagging hidden risks before they become blockers. That’s how you turn AI from a time-saver into a game-changer.