The Gartner Emerging Tech Impact Radar highlights the technologies and trends with the greatest potential to disrupt a broad cross-section of markets.Rapid innovation in critical enabling technologies is creating new possibilities for IT solutions.

By Lori Perri | 4-minute read | February 12, 2024

Gartner Emerging Technologies and Trends Impact Radar for 2024

Use the emerging tech impact radar to plan investments and strategy

Gartner recommends that when integrating emerging technologies and trends on products and services, you:

• Use them to enhance your competitive edge in the smart world, where online and offline experiences converge
• Prioritize prevalent and impactful generative AI (GenAI) use cases that already deliver real value to users
• Balance stimulating growth and mitigating risk
• Identify relevant emerging technologies and business benefits that support your strategic product roadmap

Each of the 30 technologies and trends on the impact radar falls into one of four themes: smart world, productivity revolution, privacy and transparency, or critical enablers.

Theme 1: Smart world

This theme covers the changes in how we interact with people, places, content and things based on the convergence of online and offline experiences. 

• AI avatars are human-like virtual personas created using computer-generated imagery and various AI techniques and applications.
• Digital twins are technology-enabled proxies that generate a digital representation by mirroring the state of a single or collection of physical or virtual assets, processes, persons or organizations.
• Multimodal user interfaces (UIs) refer to models in which user and machine interactions can occur simultaneously.
• Smart spaces are physical environments in which humans and technology-enabled systems interact in increasingly open, connected, coordinated and intelligent ecosystems.
• Spatial computing is a computing environment that combines physical and digital objects in a shared frame of reference.

Theme 2: Productivity revolution

This theme highlights the potential productivity of GenAI, which is largely driving its widespread adoption across the enterprise.

• Model compression is a set of techniques that reduces the size of a trained neural network for deployment on small devices or to increase a central system’s capacity.
• Autonomous unmanned aerial vehicles (UAVs) are autonomous flying machines, mostly used for asset inspection, but also starting to gain traction to help deliver small packages.
• GenAI describes technologies that can generate new, derived versions of content, strategies, designs and methods by learning from large repositories of original source content.
• GenAI-enabled virtual assistants (VAs) represent a new generation of VAs that leverage large language models (LLMs) to deliver superior functionality.
• Vision transformers for computer vision (ViTs) are neural networks that compute the relationships between pixels in sections of images to improve image classification, object detection and image generation.
• Intelligent applications use one or more AI techniques to learn from external, alternative data sources.
• Synthetic data is artificially generated rather than collected from real-world events.
• Self-supervised learning is an approach to machine learning (ML) in which labeled data is created from the data itself, without having to rely on historical outcome data or human supervisors to provide labels or feedback.

By 2025, generative AI will be embedded in 80% of conversational AI offerings, up from 20% in 2023.

Source: Gartner

Theme 3: Privacy and transparency

This theme focuses on making the right business and ethical choices in the adoption of AI and using AI design principles that will benefit people and society.

• Human-centered AI (HCAI) is a common AI design principle that calls for AI to continuously benefit from human input.
• Behavioral analytics refers to session-tracking capabilities that monitor user interactions with a protected service to build trust models for distinguishing fraudsters, trusted users and bots.
• Responsible AI is an umbrella term for many aspects of making the right business and ethical choices when an organization adopts AI.
• Decentralized identity (DCI) or associated self-sovereign identity (SSI) systems aim to address privacy and transparency challenges with traditional identity systems.
• Privacy-enhancing technologies (PETs) are a set of robust approaches that allow the processing of information while protecting underlying personal data.

Theme 4: Critical enablers

This theme centers on expectations for emerging applications — some of which will enable new use cases and others that will enhance existing experiences — to guide which technologies to evaluate and where to invest. 

• Neuromorphic computing is an engineering approach in which components of a computer are designed to mimic the functions of systems in the human brain and nervous system.
• Tokenization is a cryptographically secured representation of value or data.
• Quantum processors are chips that consist of qubits — or quantum bits — the counterpart to the bits in classical computing.
• Hyperscale edge computing (HEC) is a public-cloud-out-to-edge solution based on a distributed cloud platform, managed and controlled through a hyperscale public cloud service.
• AI chips are a type of semiconductor device optimized for processing deep neural networks.
• Web3 is a new stack of technologies for the development of decentralized web applications that enable architecture for extensibility, decentralized identity and cross-chain interoperability.
• Blockchain refers to a group of technologies focused on peer-to-peer distribution, decentralization, immutability, encryption and tokenization.
• Knowledge graphs (KGs) are machine-readable data structures that describe the relationship between heterogeneous data via a network of nodes and links.
• LEO satellite mega-constellations can offer global communications and internet access with latency and speed comparable with basic wired connectivity.
• Private 5G is based on 3GPP technology and spectrum to provide connectivity, optimized services and security for enterprises.
• Scalable vector databases provide vector search capability and are used in conjunction with LLMs to apply the model’s ability to respond to searches with information that is custom or specific to an enterprise or domain.