The Gartner Hype Cycle is a visual representation of the maturity and adoption of emerging technologies. Gartner has used it since 1995 to help clients understand how new developments in IT affect their business.
The cycle includes five phases: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity. Companies need to know where they are on this curve as it will offer insight into what opportunities exist now and what challenges may arise if they wait too long before implementing changes.
Below are six emerging technologies from the current hype cycle.
What is the Hype Cycle?
If you aren’t very familiar with the Hype Cycle, here is a quick primer. The Hype Cycle attempts to show the development of technology in terms of several phases described as follows:
- Innovation Trigger: This is where a new technology first emerges.
- The Peak of Inflated Expectations: As the market begins to understand and scale this new product or service, there is high interest from both investors as well as consumers, but eventually, that excitement reaches an apex and then turns into disappointment as expectations are hard to meet for some reason (e.g., too expensive, difficult to use, etc.).
- Trough of Disillusionment: The realization sets in that what was promised may not be what exists at this point, with many early adopters turning away from it because they feel like they were misled. However, forward thinkers continue pushing through by looking for ways around current obstacles. A few more people might join the party at this point, but not many.
- The Slope of Enlightenment: As more realistic expectations are set about what is possible with real-world applications and as more forward-thinking companies find actual use cases, users start to see the benefits of using it in their day-to-day lives, which leads to further growth as potential customers become aware there isn’t any other way to do things now (due to various reasons). Adoption picks up speed here until everyone who wants one has a device or service.
- The Plateau of Productivity: The technology stabilizes when it becomes mainstream and reaches peak market adoption/usage rates. Consumers have figured out how to best leverage its strengths while minimizing weaknesses for most use cases. This is where it becomes a utility, and the price goes down.
Over time, the Gartner Hype Cycle has provided critical insights into how new technologies will develop and has, more importantly, given decision-makers essential information such as:
- Where the technology is on its development journey.
- What will shift the technology from early to mainstream adoption? How to best leverage it now and in the future depending on where it is on the Hype Cycle.
- When to invest in technology and not just because everyone is doing it.
- How long you should wait before adopting disruptive technologies that have the potential to be a game-changer for your business. When you should be quick and embrace new ideas that might substantially alter your organization’s prospects early on, and when you should hold off on innovations.
- Determining investment priorities by considering the potential advantages of each innovation.
The most recent Hype Cycle for Emerging Technologies is the 2021 model. In this model, Gartner identified six key emerging technologies to watch.
- Nonfungible Tokens (NFT): Unique Digital Assets
In simple terms, NFTs are cryptocurrencies or digital assets that can’t be replaced by another token and have individual identities and value. This means they’re not interchangeable like most tokens today. Instead, each one has its own set of rules and applications within blockchain-based environments. These include video games, decentralized marketplaces where buying/selling goods occur without an intermediary, or even artwork on the Ethereum network used to prove ownership rights to creative works, among other use cases.
Just like cryptocurrencies, NFTs can be used to transfer value between parties anywhere in the world without bank accounts or high transaction fees. As time goes on, we will see more widespread adoption of this technology due to its ability to provide greater security than current physical assets with digital tokenization where authenticity is recorded, and ownership transfers are made official by integrating blockchain into our daily lives.
Also read: Potential Use Cases of Blockchain Technology for Cybersecurity
- Quantum ML: Machine Learning at Scale
Machine learning (ML) has been around for a while now, but getting results from it has always been difficult as it tends to require large sample sizes, which means companies need lots of data. However, what happens when you don’t have that much available data—if your company is a startup, for example?
This is where Quantum ML comes in. Machine learning, on some level, has been around since the 1950s, but only recently, with the development of quantum computers, have we seen companies start to think about how to use this technology from an AI perspective, and it’s looking very promising indeed.
Already, the current version of OpenAI’s Codex program automatically generates accurate software code 37 percent of the time. However, machine-written programs need scalable methods for building applications—something Quantum ML provides as it allows them to improve accuracy over time at scale, while still delivering results faster than traditional algorithms.
Also read: How Quantum Computing Will Transform AI
- Generative AI: AI Code that Writes Itself
Today we’re starting to see the rise and benefit of generative models in many areas such as text, speech, and even music, where they can be used to create content based on pre-existing data. This is significant for companies looking to scale their ML efforts by not relying so much on human coders but using generative AI, which is generated automatically through neural networks with minimal input from humans.
It offers enormous benefits that include:
- Self-learning from each set of data ensures that higher-quality outputs are generated
- Lower project risk
- Less biased ML models through reinforced training
- Sensor-less depth prediction
- Developing artificial intelligence that can comprehend more abstract ideas in both simulation and reality
- Homomorphic Encryption: Transformative Data Security
Today, we can encrypt data in such a way that it can’t be decrypted without being given specific conditions. The problem with current forms of encryption is that you have to decrypt data to work with it. In the process, you expose the data to the risks you were trying to protect it from in the first place.
The solution is homomorphic encryption. With homomorphic encryption, you can analyze and manipulate data without compromising security. This will allow companies to save time and money by not transferring data back and forth between networks or worrying about it being copied for nefarious purposes.
When it comes to critical personal data, such as financial services or healthcare, homomorphic encryption has a lot of promise. When individuals’ privacy is important, homomorphic encryption can safeguard the actual data while allowing it to be analyzed and processed.
Also read: Enterprise End-to-End Encryption is on the Rise
- Sovereign Cloud: Regulating the Cloud
Cloud computing is a growing trend that has been around for a while now, and it’s going to continue expanding over time, but one of its biggest issues at present is data regulations which vary from country to country. This can be problematic when companies have customers based in multiple jurisdictions as they need to adhere to each respective law or risk being prosecuted under them, which means some may choose not to do business with those countries.
For example, the digital and cloud technology and services market is currently controlled by a handful of American and Asian firms. Consequently, many European businesses store their data in these locations, spawning political tension and worries about data sovereignty and compliance with local rules.
A sovereign cloud solves this, allowing countries to secure their digital and data sovereignty, which will enable them to create and enforce laws around data protection, intelligence gathering, residency requirements, and protectionism.
- Composable Applications and Networks: It’s All Coming Together
Finally, we’re starting to see the benefits of software and hardware becoming more intertwined as they share their data, a feat which has been made possible thanks to composable applications and networks that work together seamlessly. This is significant for companies as it means they can move faster when building their platforms since there’s no need to wait for compatibility between different systems like they’ve had to do in the past.
Composable networks also allow us to change the way we work and live by allowing people (and machines) to collaborate and automate their processes without needing a central hub for data storage. It does this using decentralized applications where anyone can contribute while still maintaining an uncompromised record of transactions.
Companies that adopt composable applications and networks will pivot and adapt quickly to business disruption and crises.
A Word of Caution
Gartner’s Hype Cycle is a popular framework for understanding the various phases of new technology adoption. Over the next few years, many technologies will be emerging from their “peak of inflated expectations” phase into full-on mainstream adoption and transformational change. However, it is important to mention one key criticism of Gartner’s Hype Cycle. It can’t predict which technologies will survive and which will become extinct. When considering adopting new technologies from the Gartner Hype Cycle, be critical of their use cases and how they can best suit your business needs.
Read next: Edge Computing Predictions for 2022
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