So where does artificial intelligence sit on the curve? And how can we use it to lead technology adoption processes that generate real business value in the organization?
If youβd like to read more about it, hereβs a link to the model on Gartnerβs website.
How to Use the Hype Cycle in Practice
The value of the curve lies not only in understanding where a technology stands, but in making better management decisions: Is now the right time to experiment, to wait, or to deploy at scale? When it comes to AI, many organizations jump straight to purchasing tools while skipping fundamental questions β who are the users, which process is being improved, and how is value measured?
A useful way to apply the model is to divide AI initiatives into three categories: rapid experiments, processes that are already ready for deployment, and ideas worth monitoring but not yet worth investing in. This connection between technology adoption and genuine AI-driven transformation helps avoid empty hype and enables building a roadmap grounded in business value, organizational maturity, and execution capability.
In practice, it is worth revisiting this model every time a new AI tool surfaces within the organization. Instead of asking only βIs this tool impressive?β, the better questions are: Does it solve a real pain point? Are users ready to adopt it? Is there sufficiently high-quality data? And what will count as success after a month or a quarter? These questions transform the curve from a theoretical framework into a working tool that prevents impulsive decisions.
The right way to use the Hype Cycle, therefore, is to combine technological curiosity with product discipline, measurement, and a deep understanding of users.
This approach makes it possible to decide whether to move forward with a pilot, stop, or wait for the tool and the market to reach greater maturity.