Analyst firm Gartner has released its 2022 Hype Cycle for Emerging Technologies, featuring what the company calls 25 “must-know innovations to drive competitive differentiation and efficiency”.

However, only some are likely to achieve mainstream adoption by 2025, warns the company, with many taking a decade or more to mature – if at all.

“All these technologies are at an early stage, but some are at an embryonic stage, and great uncertainty exists about how they will evolve,” explained Melissa Davis, VP Analyst at Gartner.

“The embryonic technologies present greater risks for deployment, but potentially greater benefits for early adopters, which differentiates them from Gartner’s top strategic trends.”

The firm has divided the emerging technologies into three categories: evolving/expanding immersive experiences; accelerated AI automation; and optimised technology delivery.

Highlights among the emerging technologies to watch include:

Evolving/expanding immersive experiences

  • Digital twin of the customer (DToC): A dynamic virtual representation of a customer that simulates and learns to emulate and anticipate behaviour.
  • Decentralised identity (DCI): Via blockchain, distributed ledger technologies (DLTs), and digital wallets. These technologies are already maturing, and serve a similar function to:
  • Web3: A stack of technologies for the development of decentralised Web applications that enable users to control their own identity and data.
  • Digital humans: Interactive, AI-driven representations that have some of the characteristics, personality, knowledge and mindset of a human. A number of companies have been pushing the idea of digital employees in the robotic process automation space, and this would appear to be an evolution of this idea.
  • Internal talent marketplaces: These match internal employees and, in some cases, a pool of contingent workers, to time-boxed projects and work opportunities – a concept that does not seem new.
  • Metaverse: A persistent, shared, virtual 3D space, offering enhanced immersive experiences. This is certainly the big bet for the future of Facebook, now Meta, though some see Mark Zuckeberg’s plans, as they have been revealed to date, as dated and misconceived.
  • Non-fungible tokens (NFTs): This much-hyped concept describes a unique programmable blockchain-based token that publicly proves ownership of a digital asset. In a virtual world, such a development is inevitable, but NFTs’ real-world utility beyond separating wealthy individuals from their money remains unclear. Their usefulness is likely to be more boring than many innovators like to imagine.
  • Superapp: A composite mobile app built as a platform to deliver modular microapps that users can activate for personalised experiences.

Accelerated AI automation

Gartner describes this area as critical to evolving products, services, and solutions. It means accelerating the creation of specialised AI models, using AI to train AI models, and so on.

  • Autonomic systems: Self-managing physical or software systems, performing domain-bounded tasks that have autonomy, the ability to learn, and agency.
  • Causal AI: This 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. The distinction between this and evolutionary algorithms is unclear.
  • Foundation models: Transformer architecture-based models, such as large language models, which embody a deep neural network architecture.
  • Generative design AI or AI-augmented design: The use of AI, machine learning (ML), and natural language processing (NLP) to automatically generate and develop user flows, screen designs, content, and presentation-layer code for digital products. Generative systems have been around since the 1990s, however.
  • Machine learning code generation tools.

Optimised technologist delivery

These technologies focus on the key constituents in building digital solutions.

  • Cloud data ecosystems: These provide a cohesive data management environment that ably supports a broad range of data workloads, from exploratory data science to production data warehousing, says Gartner.
  • Augmented FinOps: This automates traditional DevOps concepts of agility, continuous integration, deployment, and end-user feedback to financial governance, budgeting and cost optimisation via AI and ML.
  • Cloud sustainability: The use of on-demand cloud services to achieve sustainability benefits within 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.
  • Cybersecurity mesh architecture (CSMA): An emerging approach for architecting composable, distributed security controls that improve security effectiveness.
  • Data observability: The ability to understand the health of an organisation’s data infrastructure by continuously monitoring, tracking, alerting, analysing, and troubleshooting incidents. Again, this does not seem to be a novel concept.
  • Dynamic risk governance (DRG): A new approach to defining the roles and responsibilities in risk management.
  • Minimum viable architecture (MVA): A standardised framework used by product teams to ensure the timely and compliant development and iteration of products.
  • Observability-driven development (ODD): A software engineering practice that provides fine-grained visibility and context into system state and behaviour. Observability by design is a good idea, especially as cybersecurity incidents increasingly resemble normal traffic.
  • OpenTelemetry: A collection of specifications, tools, APIs, and SDKs that describe and support the implementation of an open-source instrumentation and observability framework for software.
  • Platform engineering: The discipline of building and operating self-service internal developer platforms (IDPs) for software delivery and life cycle management.

(Source: Gartner, additional commentary by Transform Industry)