Four Gartner Hype Cycle themes to think about in 2023 and beyond

The 2023 Gartner Hype Cycle identifies 25 must-know emerging technologies designed to help enterprise architecture and technology innovation leaders: 

Evaluate the business impact of emerging technologies

Examine and explore potentially transformative technologies 

Strategize how to benefit from these technologies

These technologies are expected to greatly impact business and society over the next two to 10 years, and will especially enable CIOs and IT leaders to deliver on the promise of digital business transformation. 

Because emerging technologies are disruptive by nature, it’s critical to understand the potential use cases and paths to mainstream adoption.

“The technologies in this Hype Cycle are at an early or embryonic stage,” says Gartner Distinguished VP Analyst Arun Chandrasekaran*. “Great uncertainty exists about how they will evolve, so there are greater risks for deployment, but potentially greater benefits for early adopters.”

*Arun Chandrasekaran is Distinguished Vice President Analyst who focuses on providing strategic advice to CTOs and CIOs on how to spur technology innovation within enterprise IT.

Theme No. 1: Emergent AI

These technologies provide opportunities for sustainable differentiation and greater workforce productivity. While generative AI has great potential to enable competitive differentiation, several other emerging AI techniques also offer immense potential to enhance digital customer experiences, make better business decisions and distinguish yourself among your competition.

An example of emergent AI, generative AI can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. It will continue to have profound business impacts, including on content and product development; automation of human work; and in enhancing customer and employee experiences as it reaches mainstream adoption in two to five years. 

Other critical technologies in emergent AI include:

AI simulation is the combined application of AI and simulation technologies to jointly develop AI agents and the simulated environments in which they can be trained, tested and sometimes deployed.

Causal AI identifies and uses cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously.

Federated machine learning aims to train a machine learning algorithm without explicitly sharing data samples, enabling better privacy and security. 

Graph data science (GDS) is a discipline in which data science techniques are applied to graph data structures to identify behavioral characteristics that can be used to build predictive and prescriptive models.

Neuro-symbolic AI is a form of composite AI that combines machine learning (ML) methods and symbolic systems to create more robust and trustworthy AI models.

Reinforcement learning (RL) is a type of ML where the learning system receives training only in terms of positive feedback (rewards) and negative feedback (punishments).

Theme No. 2: Developer experience (DevX)

Enhancing developer experience is critical for most enterprises. The suite of technologies under this theme focuses on attracting and retaining top engineering talent by supporting interactions between developers and the tools, platforms, processes and people they work with.

Value stream management platform (VSMP) is an example of DevX technology that seeks to optimize end-to-end product delivery and improve business outcomes. VSMPs are typically tool-agnostic. They connect to existing tools and ingest data from all phases of software product delivery — from customers’ needs to value delivery. VSMPs help software engineering leaders identify and quantify opportunities to improve software product performance by optimizing cost, operating models, technology and processes. Value stream management platforms will take two to five years to achieve mainstream adoption.

Other critical technologies in developer experience include:

AI-augmented software engineering, the use of AI technologies and natural language processing (NLP) to help software engineers create, deliver and maintain applications.

API-centric SaaS,****  a cloud application service designed with programmatic request/reply or event-based interfaces (APIs) as the primary methods of access.

GitOps, a type of closed-loop control system for cloud-native applications.

Internal developer portals, which enable self-service discovery and access to resources in complex, cloud-native software development environments.

Open-source program office (OSPO), the center of competency to build strategies for governing, managing, promoting and efficiently using open-source software (OSS) and open-source data or models.

Theme No. 3: Pervasive cloud

These technologies focus on how cloud computing will evolve and become an important driver of business innovation. They are reimagining the cloud at the edge, making it more vertically integrated and enabling industry-relevant solutions. Maximizing value from cloud investments will require automated operational scaling, access to cloud-native platform tools and adequate governance.

Industry cloud platforms exemplify pervasive cloud, and address industry-relevant business outcomes by combining underlying SaaS, PaaS and IaaS services into a whole product offering with composable capabilities. These typically include an industry data fabric, a library of packaged business capabilities, composition tools and other platform innovations. IT leaders can use the composability of these platforms to be adaptable and agile in response to accelerating disruption. They will take five to 10 years to reach mainstream adoption.

Other critical technologies in pervasive cloud include:

Augmented FinOps, which applies the traditional DevOps concepts of agility, continuous integration and deployment, and end-user feedback to financial governance, budgeting and cost optimization efforts.

Cloud development environments (CDEs), providing remote, ready-to-use access to a cloud-hosted development environment with minimal effort for setup and configuration.

Cloud sustainability, the use of cloud services to achieve sustainability benefits within economic, environmental and social systems.

Cloud-native, which refers to something created to optimally leverage or implement cloud characteristics that are part of the original definition of cloud computing, and include capabilities delivered as a service.

Cloud-out to edge, an architectural construct where a centrally managed cloud environment, typically a hyperscale cloud, provides cloud service capabilities that are extended to edge environments.

WebAssembly (Wasm), a lightweight virtual-stack machine and binary code format designed to support secure, high-performance applications on webpages.

Theme No. 4: Human-centric security and privacy

The technologies in this bucket focus on how organizations can become resilient by implementing human-centric security and privacy programs. They enable enterprises to create a culture of mutual trust and awareness of shared risks in decision making between many teams.

AI trust, risk and security management (AI TRiSM) is a great example of human-centric security and privacy and ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection. It includes solutions and techniques for model interpretability and explainability, data and content anomaly detection, AI data protection, model operations and adversarial attack resistance. It will take two to five years to achieve mainstream adoption.

Other critical technologies in human-centric security and privacy include:

Cybersecurity mesh architecture (CSMA), an emerging approach for architecting composable, distributed security controls that improve overall security effectiveness.

Generative cybersecurity AI, which generates new derived versions of security-related and other relevant content, strategies, designs and methods by learning from large repositories of original source data.

Homomorphic encryption (HE), which uses algorithms to enable computations with encrypted data and enables businesses to share data without compromising privacy.

Postquantum cryptography (PQC), also called quantum-safe cryptography, algorithms designed to secure against both classical and quantum-computing attacks.

The 2023 Gartner Emerging Technologies and Trends Impact Radar shows product leaders where to capitalize on market opportunities.

Gartner research reveals four emerging technologies and trends to which tech vendors and product leaders will need to respond, calibrating their tech strategies, investments and tools to stay ahead:

- Smart world expands with increased fusion of physical-digital experiences.

- Productivity revolution accelerates with advances in artificial intelligence (AI) tools and tech.

- Transparency and privacy get more scrutiny amid exponential growth in corporate and personal data collection.
- New critical technology enablers create new business and monetization opportunities.

Four Emerging Technologies Disrupting the Next Three to Eight Years

Most of this year's emerging technologies and trends are three to eight years away from reaching widespread adoption but represent significant innovation in the years ahead.

Let’s look at four we think will prove especially interesting.

No. 1: Neuromorphic computing

A critical enabler, neuromorphic computing provides a mechanism to more accurately model the operation of a biological brain using digital or analog processing techniques.

It will take three to six years to cross over from early-adopter status to early majority adoption.

Neuromorphic computing will have a substantial impact on existing products and markets.

Neuromorphic computing systems simplify product development, enabling product leaders to develop AI systems that can better respond to the unpredictability of the real world. Their autonomous capabilities quickly react to real-time events and information, and will form the basis of a wide range of future AI-based products. Early use cases include event detection, pattern recognition and small dataset training.

We expect breakthrough neuromorphic devices by the end of 2023, but it will likely take five years for these devices to reach early majority adoption.

The impact is likely to be significant, though, as neuromorphic computing is expected to disrupt many of the current AI technology developments, delivering power savings and performance benefits not achievable with current generations of AI chips.

No. 2: Self-supervised learning

Self-supervised learning accelerates productivity by using an automated approach to annotating and labeling data.

It will take six to eight years to cross over from early-adopter status to early majority adoption.

Self-supervised learning will have a significant impact on existing products and markets.

Self-supervised models learn how information relates to other information; for example, which situations typically precede or follow another, and which words often go together.

Self-supervised learning has only recently emerged from academia and is currently practiced by a limited number of AI companies. A few companies focused on computer vision and NLP products have recently added self-supervised learning to their product roadmaps, however.

The potential impact and benefits of self-supervised learning are extensive, as it will extend the applicability of machine learning to organizations with limited access to large datasets. Its relevance is most prominent in AI applications that typically rely on labeled data, primarily computer vision and NLP.

No. 3: Metaverse

The metaverse fuels the smart world by providing an immersive digital environment.

It will take eight-plus years to cross over from early-adopter status to early majority adoption.

The metaverse will have a very substantial impact on existing products and markets.

The metaverse enables persistent, decentralized, collaborative, interoperable digital content that intersects with the physical world’s real-time, spatially organized and indexed content.

It is an example of a combinatorial trend in which a number of individually important, discrete and independently evolving trends and technologies interact with one another to give rise to another trend. The emerging, supporting technologies and trends include (but are not limited to) spatial computing and the spatial web; digital persistence; multientity environments; decentralization tech; high-speed, low-latency networking; sensing technologies; and AI applications.

The features and functionality these ETT bring to the metaverse will need to reach an early majority in order for the metaverse to cross the chasm. We consider all current examples to be precursors or premetaverse offerings because they are potentially capable and compatible but do not yet meet the definition of the metaverse.

While the benefits and opportunities from the metaverse are not immediately viable, emerging metaverse solutions give an indicator of potential use cases. We expect the transition toward the metaverse to be as significant as the one from analog to digital.

No. 4: Human-centered AI

Human-centered AI (HCAI) is a common AI design principle calling for AI to benefit people and society, which could improve transparency and privacy.

It will take three to six years to reach early majority adoption.

HCAI will have a substantial impact on existing products and markets.

HCAI assumes a partnership model of people and AI working together to enhance cognitive performance, including learning, decision making and new experiences. HCAI is sometimes referred to as “augmented intelligence,” “centaur intelligence” or “human in the loop,” but in a wider sense, even a fully automated system must have human benefits as a goal.

HCAI enables vendors to manage AI risks, and to be ethical, responsible and more efficient with automation, while complementing AI with a human touch and with common sense. Many AI vendors have already shifted their positions to the more impactful and responsible HCAI approach. The technology-centric approach of developing AI products has led to numerous negative impacts, urging vendors to rethink their AI product strategies.

The potential impact of HCAI is high because it leverages human abilities to make humans more productive and remove avoidable limitations, biases and blind spots.

In short:

The Gartner Emerging Tech Impact Radar highlights the technologies and trends that have the most potential to disrupt a broad cross section of markets.

The trends are organized around four key themes, which are critical for product leaders to evaluate as part of their competitive strategy.

Product leaders must explore these technologies now to capitalize on market opportunities.