Glossary

A

Accessibility in multimodal experiences

The expansion of interactions beyond traditional interfaces to include XR, voice, image and gesture recognition, among others, bring new challenges in accessibility. Ensuring inclusivity in these contexts requires innovative design and testing to accommodate diverse user needs.

Addictive tech

Some applications are specifically designed to be addictive through the use of techniques like gamification and dark patterns. This is driven by fierce competition for eyeballs and engagement — and while there may be commercial reasons to adopt such an approach, an increasing awareness of the societal and environmental harms of addictive tech makes addictive tech a key issue organizations need to think seriously about.

Adversarial machine learning

These are attacks on (or using) machine learning systems. Attackers may tamper with training data or identify specific inputs that a model classifies poorly to deliberately create undesired outcomes.

Affective (emotional) computing

A collective term for systems and devices that can recognize, interpret, process, simulate and respond to human emotions.

Agent-based simulation

The use of simulated independent agents, each working towards their own goals, to model a real-world situation. Such simulations can help us understand complex phenomena such as the spread of diseases or protein folding.

AGI research

The concept of artificial general intelligence (AGI) refers to an AI system that possesses a broad range of capabilities across a range of intellectual tasks — it’s often compared to human-level intelligence. Debates about the threshold for AGI remain, and research into ways of achieving it continues and will play a part in wider discussions about AI and humanity.

AI agents

Functionality built into applications which combines the functionality of publicly available generative AI models with specific knowledge from outside the model, such as product information. One of the most interesting manifestations of this trend are ‘agentic assistants’ in which AI agents are used to accomplish particular tasks in certain domains, like HR or CRM.

AI as a service

“Ready-to-go” AI solutions offered as a service on cloud platforms. They often don't require specialized AI or ML skills to be used.

AI avatars

A digital representation of a person. The use of artificial intelligence allows the avatar to mimic the person it represents, thus making it ostensibly more convincing and life-like.

AI in robotics

Bringing today's AI capabilities into robotics is bringing new levels of 'intelligence'. It can help robots better respond to situations and external stimuli and ostensibly make decisions about what actions to take in relation to its environment.

AI in security

AI is increasingly being deployed both defensively, to respond to threats more dynamically, and offensively, to probe for weaknesses in a system.

AI marketplaces

Marketplaces such as AWS Marketplace, Google TensorFlow Hub and MS Azure Marketplace enable independent developers and companies to sell their models to a global market. They also allow consumers to quickly leverage those models to create value quickly.

AI observability

AI systems are notoriously opaque. Their complexity can make it very difficult to determine the relationship between inputs and outputs. AI observability is the broad practice of monitoring and analyzing an AI system's behaviors and performance to increase understanding and confidence that it is working as intended.

AI-ready data

AI-ready data is data that has been structured and organized in a way that makes it easy for it to be integrated with AI systems. It has a number of specific qualities: high-quality (auditable and verifiable), consistent across different platforms and robust, comprehensive metadata.

AI safety and regulation

Government regulation and guidance on the use of AI, intended to ensure responsible use and consequences of AI systems. This includes monitoring, compliance and good practice and is beginning to be extended to consumer interactions with AI. 

AI-assisted software development

The use of AI to speed up or improve software development. Examples include code completion in IDEs, AI-created automated tests, AI that can detect bugs or even AI code generation tools.

AI-generated media

Images, audio or video that have been manipulated by AI. Also known as synthetic media.

AI, IoT and XR combined solutions

A new breed of solutions in which multiple technologies are combined and act together. Drones, robotics and autonomous vehicles are all examples of devices that require machine learning, processing streams of data and layers of intelligence to solve problems.

AI/ML on edge

The ability to run AI and machine learning algorithms at the edge of a network, often on resource-constrained devices.

Alternative currencies

Currencies other than money, such as cryptocurrencies or reputation-based currency. Increasingly, this includes vendor-specific reward-based currencies such as Starbucks Stars or Amazon Coins.

Augmented reality

Where the physical world is combined with the digital. A limited form of AR is now ubiquitous, delivered via Apple and Android mobile devices, capable of overlaying virtual objects to a camera view of the world. More advanced AR is delivered via a dedicated headset such as Apple Vision Pro, Microsoft’s Hololens or Meta's Quest 3.

Automated compliance

The use of technology to make all the data required to satisfy compliance reports, checks and balances readily available. In many cases, automation simplifies reporting by sifting through data. Increasingly, though, AI is beginning to replace manual decision-making.

Automated workforce

The use of technology to perform repeatable or predictable workflows. Automated workforce doesn’t mean completely replacing humans; in some cases human-machine "teaming" may produce better results than either working alone.

Autonomous robots

Smaller and cheaper than their industrial counterparts, robots with on-board AI are able to sense their environment, navigate, learn to complete tasks and even fix themselves and other things.

Autonomous vehicles

Self-driving cars, trucks and public transport. While the headline focus may be on self-driving cars, autonomous vehicles also have high potential for specialized industrial and business applications such as mining and factory floors.

B

Biometric authentication

A way of verifying an individual's identity that uses fingerprint, facial recognition or other similar technologies. It is today a valuable cybersecurity tool in many different domains and industries.

Brain-computer interfaces

A device that reads and analyzes signals from the brain and turns them into an input mechanism for a computer. The human and the device, after a period of training, work together to encode and decode human intentions.

C

Changing perceptions of AI

AI — and generative AI in particular — have been widely hyped and are therefore extremely visible not just in the industry but in wider society and culture. This means that attitudes and understandings of it — whether that's enthusiasm and excitement or distrust — are necessarily important to organizations that decide to use it. The pace of technological change, moreover, means that attitudes could also change quickly.

Collaboration ecosystems

When individuals or organizations share common goals, they will probably want to work together. To do so, they need a set of tools and resources they can use to unlock value effectively — a good example is a remote environment for development teams. This is what a collaboration ecosystem is: it allows people to solve problems together.

Consumer XR

Consumer XR refers to products and services that give users extended reality experiences. High-profile devices like the Apple Vision Pro are shaping consumer XR, but the field is highly dependent on innovations in retinal resolution to ensure properly immersive experiences.

Context-aware systems

Systems that dynamically adapt their behavior using real-time contextual information, such as user location, activity, or preferences. While the concept has existed since the early days of ubiquitous computing, advancements in AI, IoT and edge computing have significantly enhanced their capabilities. Modern context-aware systems deliver highly personalized and responsive experiences, becoming a competitive advantage across industries and signaling their importance towards adaptive and human-centric technologies.

D

Data catalog

A comprehensive inventory of an organization's data assets. Crucially, it is built on well-organized metadata, which makes it easier for organizations to discover and retrieve a particular asset and then use it appropriately.

Data clean room

Secure environments for organizations to share and combine data with each other without having to physically share their own data.

Data contract

A formal agreement between two parties — producer and consumer — to use a given data set or data product.

Data fitness functions

Automated tests that assess the quality, consistency and reliability of data in real time. By continuously assessing key characteristics, these functions ensure data meets predefined governance standards and remains fit for use in evolving workflows, facilitating interoperability and trust across data systems. 

Data lineage

An emerging set of techniques to certify the provenance of data and to govern its use across an organization. This could prove transformative in the effort to track and enhance progress towards sustainability targets.

Data marketplaces

A system that enables the finding, buying, sharing and selling of data within and outside an organization.

Data mesh

A data platform organized around business domains where data is treated as a product, with each data product owned by a team. To enable speed and drive standardization, infrastructure teams provide tools that allow data product teams to self-serve.

Data product specification

A precise technical description of a data product that enables its provisioning, configuration, and governance.

Decentralized data architectures

Use of multiple data stores instead of singular, monolithic centralized stores. A good example is data mesh.

Decentralized identity

Also known as self-sovereign identity, decentralized identity (DiD) is an open-standards-based identity architecture that uses self-owned and independent digital IDs and verifiable credentials to transmit trusted data. Although not dependent on blockchains, many current examples are deployed on them as well as other forms of distributed ledger technology, and private/public key cryptography, it seeks to protect the privacy of and secure online interactions.

Decentralized personal data stores

A data architecture style where individuals control their own data in a decentralized manner, allowing access on a per-usage bases (for example, Solid PODs).

Decentralized security

Rather than using traditional security perimeters that are a single point of failure, techniques such as zero-trust networks decentralize security checks across the network.

Developer experience platforms

Platforms which provide the tooling to make it as effective as possible for developers to create, test and deploy software. They also help developers leverage data effectively.

DevSecOps

An abbreviated portmanteau for development, security and operations. This is an approach that includes security as a first-class concern, together with development and operations.

Digital carbon management

Measuring organizational green house gas (GHG) emissions and efforts to mitigate those emissions. Establishing a carbon footprint and a program to determine it is an essential component on the journey towards net zero and is the first building block towards any sustainability strategy.

Digital twin

A virtual model of a process, product or service that allows both simulation and data analysis. 3D visualization can be used together with live data, so you can understand what is happening to pieces of equipment you can’t actually see.

E

Easing access to Generative AI

Making AI easier to use by lowering the barrier to entry with shared context and other data that those who aren't familiar with prompt engineering may struggle with.

Edge computing

Bringing data storage and processing closer to the devices where it is stored, rather than relying on a central location that may be thousands of miles away. The benefits of edge computing include reduced latency for real-time systems and improved data privacy. It’s also possible to run AI/ML models at the edge too.

Ethical frameworks

Decision-making frameworks that attempt to bring transparency and clarity into the way decisions are made, especially around the use of AI and potential bias in data.

Evaluating and managing AI outputs

Ensuring the quality, reliability, and safety of AI-generated outputs through evaluation frameworks — 'evals' — and guardrails. These include systematic tests to measure performance and tools to enforce ethical and operational standards, helping businesses deploy AI responsibly and effectively.

Evolutionary architectures

In contrast to traditional up-front, heavyweight enterprise architectural designs, evolutionary architecture accepts that we cannot predict the future and instead provides a mechanism for guided, incremental change to systems architecture.

Explainable AI

A set of tools and approaches to understand the rationale used by an ML model to reach a conclusion. These tools generally apply to models that are otherwise opaque in their reasoning.

F

Federated learning

An approach that downloads a machine learning model and then computes or trains a specific, modified model using local data on another device. The approach helps multiple organizations to collaborate on model creation without explicitly exchanging protected data.

Fine-grained data access controls

More granular access controls for data, such as policy-based (PBAC) or attribute-based (ABAC) that can apply more contextual elements when deciding who has access to data.

FinOps

The practice of bringing financial accountability to the variable spending model of cloud computing. It involves a collaborative approach among teams such as finance, operations and development to manage and optimize cloud costs effectively.

G

GenAI computer control

A new capability of generative AI tools to execute and automate computer-based tasks through natural language. They enhance digital workflows by enabling intuitive, conversational interactions with operating systems and applications. Examples include Claude's "computer use" feature and Auto-GPT, among others. 

Generative AI

AI that creates text, image, audio and video from simple human language prompts.

Green computing

Green computing is a diverse collection of practices and techniques that attempt to address the environmental impact of computation. It includes green cloud, green UX and green software development, all of which optimize systems, code and other parts of technology infrastructure to improve computational efficiency and reduce waste.

H

Hardware security

The growth in smart devices and embedded systems have made hardware an even bigger target for cybercriminals and malicious actors. Ensuring hardware is secure is today a key step in ensuring security across the enterprise.

I

Impact funds

Impact funds, or impact investing, is a trend whereby investors target businesses tackling significant social or environmental challenges in a bid to both develop a solution and, in doing so, unlock substantial financial returns.

Industrial XR

Using virtual environments to test and model desired physical outcomes in an industrial context.

Integrated data and AI platforms

Platforms designed specifically for machine learning, providing end-to-end capabilities such as data management, feature engineering, model training, model evaluation, model governance, explainability, AutoML, model versioning, promotion between environments, model serving, model deployment and model monitoring.

Integrating unstructured data

Set of techniques and tools for processing and incorporating unstructured data, such as text, images, and videos, into workflows and decision-making. Approaches like natural language processing, computer vision, and data indexing systems make this data more accessible and actionable for businesses.

Intelligent machine-to-machine collaboration

Technologies enabling the direct interaction of devices and information sharing between them, usually in an autonomous fashion. This enables exceptionally rapid decision making and action with little or no human intervention.

Interfacing with AI

Establishing standardized methods for integrating generative AI into business systems using tools like LLM proxies and OpenAPI. LLM proxies act as intermediaries that simplify AI interactions, while OpenAPI defines clear, consistent interfaces for connecting AI models to applications, ensuring scalability and ease of use.

Internet regulation

The regulation of the internet has become more and more significant in recent years. This manifests itself in many different ways, from attempts to address harmful content, restricting children's use of social media and rules about how consumer data can be collected and used.

K

Knowledge graphs

A way to represent knowledge and semantic relationships between entities using a graph data structure.

L

LLMOps

The practice of integrating LLMs into business operations, focusing on deployment, monitoring, security and governance. This includes tools and processes for fine-tuning, performance tracking, cost management, and ensuring responsible AI use.

M

Mindful screen interaction

A growing shift toward intentional and balanced device use, driven by increased awareness of screen time. Tools like screen time trackers and focus apps exemplify this trend, supporting users in managing their digital habits.

MLOps

A movement to bring DevOps practices to the field of machine learning. MLOps fosters a culture where people, regardless of title or background, work together to imagine, develop, deploy, operate, monitor and improve machine learning systems in a continuous way. Continuous Delivery for Machine Learning (CD4ML) is Thoughtworks' approach to implement MLOps end-to-end.

Model training optimization

Strategies and techniques to enhance the efficiency and effectiveness of machine learning model training. Examples include Retrieval Augmented Generation (RAG), which combines data retrieval with generative AI for precise outputs; causal inference, which identifies cause-and-effect relationships to improve generalizability and reduce training data requirements; transfer learning, which leverages pre-trained models for faster adaptation; and automated hyperparameter tuning, which optimizes model performance with minimal manual effort. These approaches are crucial for reducing costs, minimizing energy consumption and accelerating deployment.

Multimodal AI

AI model interactions that span different modes of communication. For example, a chatbot that understands and responds in both written and spoken language.

Multimodal interactions

Systems that enable users to interact through multiple input methods, such as text, voice, image and gesture recognition. By combining these modalities, tools and applications create more intuitive and accessible experiences across diverse contexts.

N

Next-generation cryptography

Forms of cryptography created in response to technological or societal challenges. Examples include quantum-resistant encryption algorithms, confidential computing with specialized hardware secure enclaves, homomorphic encryption allowing computation to occur on the data while it is still encrypted and energy efficient cryptography.

Next-generation robotics

The next generation of robotics is underpinned by advancements in artificial intelligence and machine learning. These technologies are helping to bring new dimensions of responsiveness and 'reasoning' to robotics.

Next-generation wearables

The next generation of wearables are getting smaller but also ostensibly smarter thanks to the increasing integration of AI. These devices — ranging from the popular Oura to the Humane AI pin — offer users new ways to quantify the self.

O

Online machine learning

A technique where algorithms continuously learn based on the sequential arrival of data, and can explore a problem space in real time. Contrasts with traditional machine learning where model training uses only historical data and cannot respond to dynamic or previously-unseen situations.

P

Personalized healthcare

Understanding an individual patient’s genetic profile to identify potential issues before they happen and provide more effective treatments in response to existing conditions.

Platforms as products

A way of creating and supporting platforms with a focus on providing customer (user) value instead of treating platform building as a time-boxed project.

Privacy first

Privacy first is a significant shift in business, organization and product strategy, where privacy operates as a core business value and offering. This shift moves away from the prior movement where "users are the product", into a new realm, where building trust and transparency comes first.

Privacy-enhancing technologies (PETs)

A collection of technologies and techniques designed to preserve user privacy while enabling secure and trustworthy interactions. Examples include anonymization, encrypted computing, differential privacy, decentralized identity (DiD) for self-owned digital IDs and verifiable credentials, and zero-knowledge proofs, which allow validation without exposing sensitive data. These tools play a critical role in safeguarding privacy in increasingly data-driven and interconnected systems.

Production immune systems

Systems that monitor metrics across complex distributed systems and take corrective action if a problem is detected. They are often used for security, but increasingly also for resilience and recovery in the face of an outage.

Q

Quantum computing

The use of probabilistic states of photons, rather than binary ones and zeros, to execute algorithms with significant speedup in specific problem domains. Recent advancements, such as Google's breakthroughs in quantum error correction, signal progress toward scalable systems. However, these developments also raise concerns about security, as quantum computers could potentially break classical cryptographic protocols, driving interest in quantum-resistant encryption methods.

R

Responsible tech facilitation

Tools and techniques are emerging that support incorporating responsible tech into software delivery processes, primarily focusing on actively seeking to incorporate under-represented perspectives; some examples include Tarot Cards of Tech, Consequence Scanning and Agile Threat Modeling.

S

Satellite networks

High-speed, low-latency broadband for places where traditional fiber or wireless network providers won’t spend the money to connect. Examples include Starlink from SpaceX, Kuiper from Amazon, OneWeb and Telesat.

Secure software delivery

Security applied to the entire process of software creation, which in modern architectures includes the delivery pipeline used to build, test and deploy applications and infrastructure.

Semantic representational technologies

A collection of techniques aimed at helping machines better understand data. It aims to put meaning at the very center of data, so concepts, categories and relationships can be better 'understood' by machines. For users, this can make it easier to search and manage incredibly complex data sets.

Small language models

An alternative to large language models (LLMs) that are more lightweight and efficient. While they aren't as powerful compared to their larger siblings, because they require less memory and computational power they can be used in devices at the edge of a network.

Smart systems and ecosystems

Networks of networks that use AI and ML to enhance a system to become more than the sum of its parts. For example, in a smart city, networks of cars and roadside sensors help speed the flow and safety of traffic.

Software-defined vehicles

Automobiles where the core functionalities, features and user experience are primarily governed by software, rather than traditional mechanical and electrical systems. This approach enables increased flexibility, customization and continuous enhancement through remote updates, significantly transforming the vehicle's capabilities and, in turn, the automotive industry's business models.

Synthetic data

Artificial data that mimics 'real' data. It is created algorithmically, expanding the potential size of a data set without requiring further data collection. This has many applications, from drug research to testing, and also has the benefit of reducing the risks and challenges that come from acquiring new, 'real' data.

T

Tactile interaction

Tactile interaction is an emerging trend in extended reality. It uses something called haptic feedback to enable richer and more immersive experiences where users can physically experience a virtual environment.

Talk to data

Talk to data (T2D) is a technology that allows users to interact with and analyze data using natural language queries as opposed to, say, the kinds of analytics and business intelligence dashboards that have become commonplace over the last two decades. It makes it easier to uncover insights and has a lower barrier to entry, giving more employees the ability to explore and ask questions about data.

Touchless interactions

The ability to interact with devices without touching, driven at least partially as a result of the COVID-19 pandemic. Specific technologies include hand tracking and voice and gesture recognition.

U

Understandable consent

Most terms of service (TOS) or end-user license agreements (EULAs) are impenetrable legalese that make it difficult for people without a law background to understand. Understandable consent seeks to reverse this pattern, with easy-to-understand terms and clear descriptions of how customers' data will be used.

V

Vector databases

Specialized storage systems designed to efficiently handle and index high-dimensional data vectors, commonly used in machine learning and AI applications.