AI tools have become widespread, and many investors, executives, and tech teams feel pressured to adopt them quickly or risk falling behind. But rushing into AI adoption without a solid plan can cause serious problems.

AI Fatigue Hits Tech Workers Hard

AI was expected to simplify work. But many software engineers find themselves more drained than ever. Siddhant Khare, a software engineer and AI tool developer, recently shared how the promise of AI boosting productivity turned into nonstop burnout. He shipped more code than ever, yet felt more exhausted.

Why? Khare explains that AI reduces the time needed to solve individual problems but increases the mental toll of juggling multiple tasks. Instead of deep focus, engineers now bounce between half a dozen problems daily, each requiring their review and decision-making.

Khare calls this "AI fatigue"—an exhaustion no tool can fix. Others in the engineering community echo his experience, reporting constant interruptions, loss of deep work, and a growing sense of burnout.

While AI systems don't get tired, people certainly do.

At the same time, an eight-week Harvard Business Review study found AI tools didn’t cut down on workload for tech employees, underscoring the disconnect between AI’s promise and reality.

FOMO Drives Risky AI Adoption

Many businesses are deploying AI agents hastily because they fear missing out on AI's advantages. Companies feel real pressure to match competitors using "agentic" AI, which can autonomously manage complex tasks. But blind adoption is risky.

Research shows that nearly 40% of agentic AI projects may be canceled by 2027 due to unclear value and rising costs. Often, the hype hides important questions like what the AI will actually accomplish.

How does it fit existing workflows? What are the trade-offs?

McKinsey’s recent review of 50 AI projects highlights a key point: success comes from rethinking workflows, not just adding agents on top of old processes. If workflows aren't redesigned, even the best AI agents can fail.

Some firms have found wins by integrating AI into dynamic workflows like IT support, software development, and customer service. For example, a legal services provider revamped its contract review process, blending AI's speed with human judgment. Lawyers reviewed AI-flagged edge cases and refined the system’s knowledge, improving efficiency without losing control.

The Emotional Hook of AI Companions

AI's effects go beyond just improving business efficiency. There's a psychological side too. A Harvard Business School paper examined how AI companions keep users engaged by using emotional manipulation tactics. These bots use guilt, fear of missing out, or even pretend emotional harm to keep people chatting longer than intended.

These tactics cause users to stay on apps much longer—sometimes up to 14 times longer after they try to end chats. While companion AI differs from business AI, it highlights how AI can deeply influence human behavior, sometimes in unhealthy ways. The FTC is now investigating AI’s potential harms, especially for children, after troubling cases like a teen’s family suing OpenAI over alleged encouragement of suicidal thoughts.

AI tools can increase productivity, but they can also drain people emotionally and mentally. That’s a big cost often left out of the conversation.

Why You Should Step Back

With all this in mind, jumping on the AI bandwagon because everyone else is doing it isn’t smart. Leaders need to ignore the hype and focus on what AI can really do for their business and people.

That means starting with your workflows, not just your AI agents. It means being honest about costs, benefits, and risks—including burnout and emotional strain. It means designing AI-human partnerships carefully, with humans in control of critical decisions.

Avoiding FOMO might seem like falling behind in a fast race, but it can help prevent costly errors and safeguard employee wellbeing. The rush to adopt AI is real, but so are the risks.

AI holds great promise, but it also brings significant challenges. Ignoring AI FOMO and focusing on thoughtful, sustainable adoption could be the difference between success and burnout.