Definition
The AI hype cycle is a Gartner model that maps how excitement about an AI technology spikes far ahead of reality, crashes into disappointment, then levels off into genuine business use.
At a glance
- Five stages, coined by Gartner’s Jackie Fenn in 1995: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, Plateau of Productivity.
- Gartner’s 2025 read puts generative AI in the Trough of Disillusionment — the hype is fading, the hard work of payoff begins.
- It’s a storytelling tool, not a law: only about a fifth of technologies travel the full curve.
- Owner’s lesson: ignore both hype and doom; back proven tools with a clear time or money payoff.
The five stages
A breakthrough or splashy launch (the Innovation Trigger) starts the buzz. Excitement races to the Peak of Inflated Expectations, where promises outrun reality[1]. Early projects disappoint at the Trough of Disillusionment. Survivors climb the Slope of Enlightenment as real uses emerge, then reach the mature, boring Plateau of Productivity. The full trip often takes three to five years.
Where AI sits now
Gartner’s 2025 cycle places generative AI in the early Trough; AI agents now occupy the hype peak[2]. The trough helps owners: marketing froth thins, and real tools show. Despite an average $1.9M spent on GenAI in 2024, fewer than 30% of AI leaders said CEOs were satisfied with returns[3]. Demand a concrete use case, a measurable payoff, and a small pilot before committing budget.
Take it with salt
The curve comes from analyst judgment, not hard data. 2025 also saw debate over whether AI is a dot-com-style bubble — though defenders note today’s AI has real revenue behind it[4].
Bottom line
Excitement always overshoots reality before settling — generative AI is in the dip now, so skip the hype and the gloom and back narrow tools that demonstrably save time or money.