info@paultaylor.associates | 07801 207579

The importance of the Gartner Hype Cycle

Nov 24, 2025

The importance of the Gartner Hype Cycle

The Gartner Hype Cycle is a conceptual model that describes the progression of a technology from early innovation to mainstream adoption.

Unlike models focused solely on technological capability or commercial uptake, the hype cycle places a strong emphasis on public expectations, perception, and psychology.

It illustrates how new technologies are often met with extreme enthusiasm before encountering practical challenges that cause interest to decline, followed by gradual progress toward stability and proven value.

The model is typically presented as a curve with five key linear phases.

The vertical axis represents expectations or visibility, while the horizontal axis represents time. As a technology moves along the curve, it encounters different levels of media attention, investment interest, development progress, and user adoption. This enables organisations to understand not only where a technology stands in its lifecycle but also how they might time investments or adoption decisions relative to the hype cycle.

What are the five stages?

Gartner describes the hype cycle through five distinct phases, with each defined by unique characteristics, challenges, and opportunities.

1. Innovation Trigger

The first stage begins when a new technology breakthrough, idea, or significant R&D advancement occurs.

At this point:

  • There may be no functioning products, only proof-of-concept models or prototypes.
  • Media and industry analysts often focus heavily on potential rather than practical outcomes.
  • Investment flows toward early-stage projects, frequently driven by speculation.

During this phase, expectations begin to rise sharply even though real-world applications may still be years away. The technology is exciting, futuristic, and full of possibilities, but completely unproven.

Examples of past technologies at this stage include quantum computing in its early foundation years, early virtual reality research in the 1980s and 1990s, and, more recently, some advanced forms of artificial general intelligence (AGI).

2. Peak of Inflated Expectations

The second stage marks the point at which expectations reach their highest point.

Key characteristics include:

  • Media hype intensifies, often emphasising extreme or unrealistic applications.
  • Startups multiply rapidly, often attracting significant venture capital.
  • Early adopters experiment with the technology, but success stories are usually limited or anecdotal.
  • Failures occur frequently but are overshadowed by grand claims.

This stage is marked by exuberance. Organisations may feel pressure to invest simply to appear innovative, even if use cases are unclear.

Many technologies never move beyond this point, as they fade as exaggerated expectations crumble.

Examples include the hype surrounding blockchain in the mid-2010s or the initial surge of enthusiasm around 3D printing for consumer use.

3. Trough of Disillusionment

After expectations peak, reality begins to set in. This stage involves:

  • Disappointment when technologies fail to meet unrealistic predictions.
  • Some early startups are collapsing due to unsustainable business models.
  • Investors and media are shifting attention elsewhere.
  • Survivors focus on technical refinement, practical use cases, and solving real-world problems.

Though seen as a negative period, this stage is essential for filtering out exaggerated claims and leaving behind the genuinely promising applications. Technologies that continue through this phase often emerge stronger, more mature, and better aligned with actual market needs.

Examples include the AI “winters” of the 1970s and 1980s, when early optimism about artificial intelligence sharply declined due to technical limitations and unmet promises.

4. Slope of Enlightenment

The fourth stage marks renewed progress—but now grounded in realism. Here:

  • Successful examples begin to accumulate, demonstrating practical, reliable uses.
  • Second- or third-generation products appear, solving early technical issues.
  • More organisations begin experimenting with the technology in targeted ways.
  • Best practices, frameworks, and industry standards start to emerge.

The technology begins to demonstrate its true value, not its speculative value. Adoption moves from hype-driven to needs-driven.

A strong example is cloud computing in the early 2010s, after initial scepticism and security concerns faded, and businesses began to understand its scalability and cost benefits.

5 . Plateau of Productivity

In the final stage, the technology becomes stable, well-understood, and widely adopted. At this point:

  • Mainstream markets embrace the technology confidently.
  • Benefits are proven, widely documented, and measurable.
  • Vendor ecosystems mature, and industry standards are fully developed.
  • Innovation continues, but at a more predictable pace.

This is the stage where technologies transition from “emerging” to “essential.” The plateau’s height—representing the total value delivered—varies by technology. Some remain niche but useful, while others transform industries.

Examples include smartphones, mature cloud services, machine learning in enterprise analytics, and containerization technologies like Docker and Kubernetes.

What are its advantages?

The Gartner Hype Cycle offers several strategic benefits to businesses, policymakers, researchers, and investors:

  • Clarifies Expectations – The model helps stakeholders distinguish between realistic capabilities and inflated promises. This is especially important during hype-driven stages when the risk of over-investment is high.
  • Helps with the timing of technology adoption – This means organisations can decide whether to be (a) early adopters, capturing competitive advantage but accepting risk. (b) pragmatic adopters, entering during the slope of enlightenment or (c) late adopters, waiting for stability but potentially losing early opportunities.
  • Supports portfolio and innovation management –  Organisations can map emerging technologies onto the hype cycle to prioritise R&D investments, avoid fads, and identify long-term strategic bets.
  • Encourages Critical Thinking –  By illustrating the cyclical nature of hype and disappointment, the model encourages scepticism and thoughtful evaluation rather than blind enthusiasm.
  • Helps with Communication Strategies – The hype cycle provides a simple visual language that helps leaders explain technological evolution (and their supporting decisions) within organisations.

But what are its disadvantages?

Despite its usefulness, the hype cycle has several limitations that must be recognised:

  • Lack of Empirical Measurement – The model is conceptual rather than data-driven. Critics argue that expectations are difficult to quantify and that stages can be subjective.
  • Overgeneralization – Technologies do not always follow the same path. Some skip stages, others regress, and some never reach full adoption. The model may oversimplify complex innovation processes.
  • Emphasis on Public Perception – Because the model relies heavily on media visibility and social excitement, it may not accurately reflect technical progress happening quietly behind the scenes.
  • Potential for Misinterpretation – Organisations may mistakenly believe that simply surviving the trough or reaching the plateau guarantees commercial success, which is not always true.
  • Lack of Time Scale – The hype cycle does not specify how long each stage lasts. Some technologies take decades to mature (such as AI), while others move through the cycle in a few years. This ambiguity can challenge planning.

Some working examples

The hype cycle applies to a wide range of innovations. Here are several well-known examples:

  • Artificial Intelligence (AI) – AI has moved through multiple hype cycles over its history. The early periods of inflated expectations in the 1960s and 1980s gave way to “AI winters,” representing classic troughs of disillusionment. Today’s machine-learning systems, natural language processing, and enterprise AI tools operate on the plateau of productivity, while more advanced forms (such as AGI) rest near the innovation trigger or early peak.
  • Cryptocurrency and Blockchain –  Cryptocurrencies experienced a dramatic peak around 2017–2018, followed by a steep trough as many projects failed and regulators increased scrutiny. Blockchain technology is gradually climbing the slope of enlightenment, especially in supply chain management and secure digital identity.
  • Virtual and Augmented Reality (VR/AR) – VR and AR experienced heavy hype in the mid-2010s but faced challenges related to hardware cost, comfort, and content development. Today, VR is moving up the slope of enlightenment in training, simulation, gaming, and remote collaboration, while AR is making steady industrial progress via mobile devices and smart glasses.
  • Cloud Computing – Cloud computing is one of the best examples of a technology that has reached the plateau of productivity. After early scepticism, security concerns, and implementation challenges, cloud platforms became essential infrastructure for modern businesses.
  • Internet of Things (IoT) – IoT initially faced inflated expectations, but security vulnerabilities, complexity, and fragmentation led to disillusionment. Now, industrial IoT (IIoT) is approaching the plateau as manufacturing and logistics adopt connected devices at scale.

To summarise

The Gartner Hype Cycle provides a valuable framework for understanding the complex, nonlinear evolution of emerging technologies. By charting how innovations rise from early excitement to inflated expectations, pass through disillusionment, and ultimately reach maturity and productivity, the model helps organisations make informed decisions about adoption, investment, and experimentation.

Although the hype cycle is not without limitations (particularly its reliance on perception and the absence of empirical metrics), it remains an influential tool for navigating uncertainty in the rapidly evolving technological landscape.

Its greatest value lies in highlighting the importance of timing and perspective: technologies are rarely as revolutionary as they seem at their peak, but they often deliver significant value once they emerge from the trough and mature into practical solutions.

Understanding the hype cycle allows businesses, investors, and researchers to separate fleeting hype from long-term opportunity, supporting smarter innovation strategies and more sustainable technological growth.