Here’s How Leaders Can Manage AI Fatigue


In a world where digital transformation is as crucial as the air we breathe, there’s a quieter, more insidious transformation happening within our organizations. It’s not the tech that’s failing or the systems we’re building—it’s us, the individuals within these structures. We’re facing AI fatigue, a quiet revolution of weariness towards technology that, if ignored, could sabotage even the best-laid digital strategies. How do leaders address this fatigue and rejuvenate their teams? Here are five actionable approaches to consider.

1. Acknowledge Change Fatigue

The endorphin hit that comes from completing a new system roll-out is real, but so is the crash. As organizations navigate multiple, rapid-fire changes—many of which are digital— the enthusiasm to support these shifts is eroding. In 2022, an average employee encountered 10 enterprise changes, compared to just two in 2016. One could argue that this increase wasn’t solely due to workload or market conditions, but rather the perception of technological competitive need surpassing actual business necessity.

The impact is concerning. In workplaces permeated by change fatigue, enthusiasm and involvement in new initiatives plummet. If anyone doubts the burnout tech proliferation can cause, consider this: the willingness of employees to embrace changes, which stood at 74% in 2016, now hovers around a dismal 43%. The term AI fatigue becomes more prescient in this context—employees are tired from the constant push towards technological innovation.

Why It Matters: Acknowledging change fatigue is not the exercise in finding a convenient scapegoat, it’s crucial because it’s real and it’s having a tangible impact on our workforce.

2. Start With The Problem, Not The Technology

When leaders green-light AI initiatives, they often put the cart before the horse. Technology becomes the driving force behind the initiative rather than a tool to execute the solution. This approach is not only wrong, it’s costly. Out of the 90% of companies that have run tech cost programs in recent years, three out of four didn’t achieve their cost productivity targets. Nearly half missed their targets by more than 50%, bringing into question the value proposition of these digital deployments.

What if we flip the script and begin with the problem? By identifying genuine business needs first, leaders can then assess which technologies genuinely serve the solutions. Companies that buy into technology for technology’s sake are at risk of experiencing a different kind of fatigue: the “tech debt” that accumulates when IT infrastructure falls behind the curve.

Why It Works: Starting with the problem ensures that technological investments are just that—investments with a clear, articulated return. This approach doesn’t just alleviate fatigue, it prevents its development.

3. Embrace Strategic Consumption

The abundance of AI and machine learning startups—roughly 67,199 as of this year—has led to a gold rush of sorts, with companies trying to get their hands on as many shiny new objects as possible. But the reality is that not every tool or system is the right fit for every company. In an effort to prevent the oversaturation of technology, a strategic and selective approach is necessary.

Data backs this claim. An estimated 37% of projects fail due to unclear project objectives. When an organization embraces strategic consumption, it’s due diligence at work. It involves looking beyond the marketing glitz to the core functionality that aligns with organizational goals.

Why It’s Important: By being discerning in tech adoption, leaders demonstrate a commitment to the long-term success and well-being of their organization.

4. Set Realistic Goals

The sky is the limit, as long as you don’t forget how to craft a ladder. In AI, as in any domain, setting unrealistic goals can lead to disappointment. In fact, the failure of innovation initiatives often comes down to the fact that expectations were set impossibly high from the get-go.

For every AI success story, there are countless failures. But the difference between the two isn’t just the underlying technology, it’s the management of expectations. When McKinsey’s research found that organizations with the strongest AI adoption rates displayed better top-line gains, it was because those organizations set realistic expectations from the outset.

Why This Is Key: Realistic, achievable targets do more than direct effort, they also foster morale. And in fighting AI fatigue, morale is everything.

5. Foster A Culture Of Agility

So, we’ve selected our tools, set our goals. Now what? Well, here’s the secret ingredient that brings it all together: culture. A tech-forward culture that values agility can absorb new technologies without breaking a sweat. This isn’t about tech savviness. It’s about an organizational mindset more than IT capabilities.

Agile cultures adapt, learn and evolve as part of their everyday operations. In fostering this environment, leaders create a space where innovation can thrive while ensuring that employees feel invested in ongoing transformation rather than overwhelmed by it.

Why Culture Trumps Tech: A culture that embraces change is a culture immune from fatigue. It’s a self-sustaining, self-improving ecosystem that can support AI and beyond.

AI fatigue is an unequivocal challenge that demands immediate attention. Leaders must not only recognize its existence but take tangible steps to address it from every angle—from the human to the technological. By acknowledging the very real weariness that technological overhauls can provoke and responding with a thoughtful, strategic approach, we can ensure that the digital future for our organizations is one of thriving, not just surviving.



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greg@ainewsbeat.com

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