Gartner Hype Cycle: What Technologies are Considered Breakthrough

The Technology Development Curve (or Hype Cycle) is a conceptual product devised by the American IT research and advisory firm, Gartner. It presents a foresight into novel, breakthrough technologies predicted to evolve dynamically over the coming decade.

This cycle's methodology sheds light on nascent technologies that have recently emerged and are already causing quite a stir. The development curve forecasts their progress over time and their potential relevance for solving real-world business problems: it serves as a guide to decide whether to build startups around them, invest substantial funds in their development, hoping for returns in a few years.

Gartner defines "breakthrough technologies" as those inherently revolutionary, yet their viability and competitive edge are yet to be proven. To construct this graph and plot the 30 points on it, the firm's analysts sift through thousands of unique technologies they believe will make their mark on the 5-10 year horizon, profoundly impacting society and business.

The S-shaped curve consists of five segments, each representing a phase in the cycle.

The graph represents technologies expected to have a substantial influence on business and society within a 2-10 year horizon, empowering IT directors and business leaders to effectuate digital transformation of their business.

However, new technologies, without proven competitive advantage, are inherently disruptive. To capitalize on the opportunities they present, it's crucial to understand potential use cases and pathways to mass adoption. The implementation of these technologies could take anywhere from two years to several decades.

According to Melissa Davis, Gartner's Vice President of Analytics, all these technologies are in their early stages, with some still in their embryonic state, leading to great uncertainty regarding their development trajectory. Technologies in their nascent stage pose substantial risks upon deployment but potentially offer more advantages to early adopters, distinguishing them from major strategic technological trends.

In August 2022, the company updated its forecast. It identified 25 new technologies that are worth knowing if you're following technological innovations and don't want to miss important emerging trends.

The prospective technologies that analysts highlighted in 2023 fall into three groups:

1. enhancing the capabilities of immersivity;

2. accelerating automation and the deployment of artificial intelligence (AI);

3. innovations in the area of technology delivery optimization.

*Immersivity (from the English "immersive" - "presence, immersion") is a way of perception that creates the effect of immersion in an artificially created environment. We observe various examples of the immersion effect in cinema, theatrical performances, constant interaction with a virtual community in social networks, or multiplayer games.

In August 2022, Gartner released the traditional annual Hype Cycle for Emerging Technologies report with the most promising innovative technologies.

The advantage of these technologies is that they give people more control over their identification and data, as well as expanding their range of capabilities through virtual platforms and ecosystems that can be integrated with digital currencies. These technologies also offer new ways to connect with customers to enhance or open new revenue streams.

● A Digital Twin of the Customer (DToC) is a dynamic virtual representation of the customer that models possible behavior and learns to mimic it.

The essence of the technology is that it allows you to test a product, process, or business model "in digital," thus reducing the costs of conducting many real-world experiments.

Digital twins can model and predict customer behavior based on their personal characteristics, and can be used to improve the quality of customer service (CX) and support efforts to digitize products and services. For example, reducing queues in stores or traffic jams on the roads, applying a fundamentally different approach to organizing processes in retail, healthcare, and other areas.

The implementation process of this technology will take from five to 10 years before it becomes mainstream and changes organizations.

Here are a few more relevant technologies based on the immersive experience:

● Decentralized Identification (DCI) allows a subject (usually a human user) to control their own digital identification using technologies such as blockchain or other Distributed Ledger Technologies (DLT), as well as digital wallets.

● Digital humans are interactive representations powered by artificial intelligence that possess some of the characteristics, knowledge, and thought of a human.

● An internal talent market or personnel marketplace helps to select employees within the company or form a pool of temporary workers engaged in projects without the participation of a recruiter.

● The Metaverse represents a collective, virtual 3D space formed by the convergence of enhanced physical and digital realities. It offers a perpetual, immersive experience that transcends traditional boundaries.

● A Non-Fungible Token (NFT) is a unique programmable digital entity built on the blockchain that publicly authenticates ownership rights over digital assets, such as digital art or music, or physical assets that have been tokenized, like houses, cars, or documents.

● SuperApp is an innovative breed of applications possessing an expanded feature set. It integrates online banking capabilities, lifestyle services, marketplace features, an embedded voice assistant, and personalized services. What used to be spread across multiple applications is now consolidated into one robust platform.

● Web3 represents a novel technology stack for developing decentralized web applications that empower users to control their own identification and data.

The expanded adoption of Artificial Intelligence (AI) is a critically essential pathway for product, service, and solution development. This entails speeding up the creation of specialized AI models, employing AI for developing and training AI models, and deploying them to deliver products, services, and solutions.

These measures enable more precise predictions and decision-making, and expedite the attainment of objectives. The role of humans in such technology is to act as users, evaluators, and observers.

● Autonomous systems serve as examples of accelerated AI automation. They are capable of modeling high-complexity phenomena that cannot be depicted through classical mathematical models or neural networks – for instance, the movement of urban transport.

In an autonomous system, we assign certain properties to humans, cars, or any other objects, and initiate the prediction of their actions within the framework of a learning model that has the freedom to act.

Customers too, are in some respects, autonomous systems. Every individual has behavioral characteristics. Autonomous systems can calculate a customer's behavioral model within the context of any industry object, such as a medical organization or bank, and thereby predict their response to a planned change by the organization.

Autonomous software systems demonstrate three fundamental characteristics: autonomy, learnability, and agency. When traditional AI methods fall short of adaptability and flexibility, autonomous systems can succeed in assisting the execution of business tasks.
Gartner's forecast for the widespread adoption of the following systems in organizations lies within a 5 to 10-year horizon.

● Causal Artificial Intelligence (AI) identifies and utilizes causal relationships to transcend correlation-based prediction models and transition towards AI systems that can more effectively prescribe actions and operate autonomously.

● Foundation models or base models function as a "super-architecture" where billions of diverse types of data - texts, images, audio, dozens of types of graphs, and so on - are used to train a massive model. This approach can solve a vast volume of applied tasks with a single system. The idea is that once a gigantic mega-model with a multitude of input data has been trained, it can be "sliced" into different parts (distilled).

For example, a text generation skill based on keywords can be refined to create an AI copywriter for product descriptions in online stores. Or, the ability to generate images from texts can be used to create advertising banners. This technology is already in action: Cosmopolitan magazine published the world's first magazine cover created by artificial intelligence. In essence, a large model, with a bit of further training, can be used to solve a plethora of specialized tasks.

● Generative Design AI involves the use of AI technologies, Machine Learning (ML), and Natural Language Processing (NLP) for the automatic creation and design of user flows, screen design, content, and code in digital products.

● Machine Learning (ML) code generation tools encompass cloud-based machine learning models that connect to professional integrated development environments (IDEs). These environments are extensions that offer suggested code based on either natural language descriptions or partial code snippets.

The pool of technologies from this group focuses on the key components in building a digital business: communities of product, service, or solution developers (e.g., Fusion Team) and the platforms they use. These technologies provide feedback and understanding that optimize and accelerate the delivery of products, services, and solutions, as well as enhance business operations' resilience.

A Fusion Team is a multidisciplinary digital business team with a centralized cloud workspace (hub) for project work. Gartner research shows that distributed concurrent projects involving a broad range of specialists can progress 2.5 times faster than centralized sequential efforts. Fusion teams are key elements of this type of distributed digital development model.

Cloud data ecosystems provide an integrated data management environment that adeptly supports the entire range of data workloads, from data science research to production data storage. Cloud data ecosystems provide optimized delivery and comprehensive functionality that is easy to deploy, optimize, and maintain. Their widespread implementation can take from two to five years and will be very beneficial for users.

This group of technologies also includes:

● Augmented Financial Operations automates traditional DevOps concepts such as agility, continuous integration and deployment, as well as end-user feedback with financial management, budgeting, and cost optimization through the application of artificial intelligence and machine learning (ML) methods.

● Cloud resilience is the use of cloud services to achieve sustainability advantages in economic, environmental, and social systems.

● Computational storage (CS) offloads host processing from the main memory of the central processing unit (CPU) to the storage device.

● Cellular Security Management Architecture (CSMA) is a new approach to the development of composable distributed security management tools that increase overall security efficiency.

● Data observability is the ability to understand the state of an organization's data landscape, data pipelines, and data infrastructure through continuous monitoring, tracking, alerting, analyzing, and incident resolution.

● Dynamic Risk Governance (DRG) is a new approach to the critical task of defining roles and responsibilities for risk management. DRG tailors risk management appropriately to each risk, enabling organizations to manage risks better and reduce audit costs.

● Industry cloud platforms utilize foundational cloud services SaaS, "Platform as a Service" (PaaS), and "Infrastructure as a Service" (IaaS) to offer relevant industry bundle business and technical capabilities for a specific vertical as an integrated product.

● Minimum Viable Architecture (MVA) is a standardized structure used by development teams to ensure timely and requirements-appropriate product development and iteration.

● Observability-Driven Development (ODD) is a software development practice that provides detailed visibility and context of system state and behavior by designing systems that can be observed.

● OpenTelemetry is a set of specifications, tools, Application Programming Interfaces (APIs), and Software Development Kits (SDKs) that describe and support the implementation of open-source instrumentation and observability platforms for software.

● Platform Engineering is the discipline of creating and operating internal self-service developer platforms (IDPs) for software delivery and lifecycle management.

● The Gartner Hype Cycle™ for Emerging Technologies 2022 includes 25 "must-have" innovations for competitive differentiation and resilience.

● Few of them are likely to reach broad adoption within just two years; the rest will evolve over ten years and more.

● The nascent stage of these technologies makes them riskier to deploy, but the potential advantages for early adopters are greater.