Hype cycle

Gartner introduced the Hype Cycle in 1995 as a visual account of how expectations surrounding an emerging technology can change. The curve moves from an initial trigger and excessive enthusiasm through disappointment toward more grounded learning and productive use. Although developed by an IT research and advisory firm, the narrative is often applied to technologies and innovations beyond information technology.

When to use it

Use a Hype Cycle to structure discussion about an emerging technology’s evidence, maturity, uncertainty and possible timing. It can inform the scale and type of learning investment, but it cannot by itself predict returns or determine when to buy.

The framework can also place several innovations in a common conversation when prioritising R&D or business-development work. Comparisons remain qualitative: different technologies may have different markets, risks, evidence standards and adoption mechanisms even when shown at similar positions.

Origins

Research and advisory firm Gartner introduced the Hype Cycle in 1995. The model became one of the firm’s recurring research formats for representing the maturity and adoption of technologies and applications. Its familiar curve synthesises analyst judgement and evidence; it is not a measured natural law or a literal cycle.

What it is

The Hype Cycle is a qualitative model of changing expectations around an innovation. Its five stages are the innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment and plateau of productivity. The vertical curve should be read as visibility or expectations rather than technical performance, while time runs horizontally. A labelled position is therefore an analytical judgement, not a calculated forecast.

How to use it

Use the five phases to ask a different set of questions about the technology:

• Technology trigger. A breakthrough, demonstration, product launch or other event attracts attention. Usable products may not yet exist, and commercial viability is unproven. Identify the underlying capability and the evidence still missing.
• Peak of inflated expectations. Publicity amplifies early success stories while failures receive uneven attention. Some organisations act and many wait. Separate demonstrated value from general claims, and invest only where a specific use case, owner and learning objective justify involvement.
• Trough of disillusionment. Interest declines when experiments and implementations fail to meet inflated promises. Providers consolidate or disappear. Review what actually failed—technology, use case, implementation or expectations—and continue only where evidence and improvements justify it.
• Slope of enlightenment. Better-defined benefits, limitations and implementation patterns emerge. Second- and third-generation products appear, and more organisations fund pilots while cautious adopters wait. Compare credible use cases and determine what capabilities are needed to scale them.
• Plateau of productivity. Real-world benefits are better established, provider assessment becomes clearer and mainstream adoption may accelerate. A supporting ecosystem of products, services, standards and skills can form. Evaluate whether the now-better-understood economics and risks fit the organisation’s strategy.

As a technology matures and serves thousands of enterprises and millions of users, attention may shift from novelty to ordinary infrastructure. New products or applications built around it can generate separate waves of expectations.

Combine the Hype Cycle with evidence about adoption, such as the market-adoption curve or Bass diffusion model (see Diffusion model), and with a product life cycle (S-curve) where relevant. These frameworks describe different constructs. The Hype Cycle foregrounds collective expectations; it does not substitute for adoption data, technical readiness, unit economics, security review or customer research.

Final analysis

The model is memorable and useful for resisting both excitement and reflexive pessimism. Its limitations are equally important: the shape is not a cycle, stage placement is not produced by a publicly reproducible scientific measure, and technologies can skip stages, disappear or experience several expectation waves. A persuasive chart can therefore create false consensus. Record the evidence and assumptions behind any placement and make the investment decision independently.

Top practical tip

Use the curve to frame questions about evidence, maturity and expectations; then make the decision from the technology’s fit, economics and risks rather than its labelled position.

Top pitfall

The curve is a qualitative narrative, not a measured law of technology adoption. Many technologies skip stages, disappear or follow several cycles, so do not use a position on the curve as an investment forecast.

Further reading

Fenn, J. and Raskino, M. (2008) Mastering the Hype Cycle: How to Choose the Right Innovation at the Right Time. Cambridge MA: Harvard Business School Press.

Gartner, Inc.: www.gartner.com/technology/research/methodologies/hype-cycle.jsp