From AI FOMO to an AI Hangover: What Leaders Need to Do Next

It feels a little like the morning after a wild party. Companies spent a staggering $2.5 trillion on AI in 2026 – a 47 % jump from the year before – and now the lights are flickering on the aftermath.

Why the binge? Over the past twelve months I chatted with hundreds of CEOs, CIOs and line managers. Almost every single one admitted they were terrified of being left in the dust, that a competitor might sprint ahead with a shiny new chatbot or a self‑driving analytics engine. In other words, they were caught in a massive case of AI‑FOMO.

Fast forward to today, and the excitement has faded. In its place sits an uneasy “what did we just do?” feeling that’s being called an AI hangover. It’s not just a buzzword – there are three very real symptoms surfacing across boardrooms:

• Surprise (and a touch of dread) at the volume of push‑back from employees who feel forced into using tools they don’t trust.
• An aching anxiety because the promised boost to the bottom line is still nowhere in sight.
• Worry that many workers seem to be churning out more material, yet the quality feels flatter – the exact opposite of what leaders hoped to buy.

The knee‑jerk reaction for many executives has been to double‑down: “If you’re not using the AI you’ve already bought, you’re wasting money.” As someone who spends his days studying how brains behave at work, that advice feels like telling a hungover person to drink another espresso. It rarely ends well.

The core problem is a mismatch in mental models. Most companies treat generative AI (GenAI) like any other technology rollout – install it, train people, and expect instant ROI. In reality, it’s more of a cognitive overhaul, a shift in how people think, decide and collaborate. Trying to force adoption without addressing that deeper shift only amplifies the hangover.

So, what should leaders actually do? The research points to three practical moves that go beyond “use it more.”

1. Re‑position GenAI – from a thinking shortcut to a thinking partner

When GenAI is marketed as a way to skip the hard parts of a job, it triggers a dangerous shortcut. Employees – especially the average and struggling performers who make up the bulk of any workforce – start offloading everything to the bot: meeting notes, email drafts, slide decks, even strategy outlines. Their output volume spikes, but the depth of insight often stays shallow. In short, you get a lot of “average” work, because the AI is only as good as the prompts it receives.

Instead, frame AI as a tool that expands thinking. It can surface angles you hadn’t considered, help you test assumptions, or crank up the creativity dial. When people see AI as a partner that nudges them to think deeper, they’re more likely to engage critically rather than treat the output as a finished product.

2. Reduce the threat perception of adoption

Change is scary, especially when it feels like your job might be replaced. Leaders need to demystify the process: be transparent about what AI will (and won’t) do, give ample time for experimentation, and celebrate small wins rather than pushing for immediate scale.

Think of it like introducing a new teammate. You’d introduce them slowly, let the existing crew get to know their strengths, and set clear expectations about collaboration. The same principle applies to GenAI – give people space to learn, make mistakes, and adjust.

3. Build the scaffolding for deep, human‑centric thinking

GenAI can surface data in seconds, but making sense of that data still requires human judgment. Companies must invest in the “human in the lead” model: equip workers with critical‑thinking training, create forums for peer review, and put safeguards so that AI‑generated content is always vetted by someone with domain expertise.

Our own studies show that roughly five percent of employees who get access to GenAI – usually top performers – naturally evolve into this role. They treat the tool as an accelerant, not a crutch. The challenge for leaders is to replicate that mindset across the organization, not by mandating more usage, but by nurturing the skills that let people use AI wisely.

In practice, that could mean:

• Setting up “AI‑review” checkpoints where a human verifies the output before it goes out.
• Running regular workshops on prompt engineering so people learn how to ask better questions.
• Rewarding insight generation, not just output volume.

When the narrative shifts from “AI does the work for you” to “AI helps you do better work,” the hangover begins to lift. Employees feel less like they’re being handed a cheap cheat sheet and more like they’ve been given a powerful research assistant.

Bottom line: The frenzy to buy every new AI tool is over. The real work now is figuring out how to integrate those tools into the very way people think and collaborate. It’s a messy, sometimes uncomfortable process – exactly the opposite of the “plug‑and‑play” promise that sold many of these platforms in the first place. But if leaders can accept that discomfort, they’ll not only recover from the hangover, they’ll set the stage for a smarter, more resilient organization.