AI glossary 2026 for executive leaders
109 terms to discuss AI in the executive committee, without jargon or complacency. Each entry gives the short definition, the concrete example and the angle that matters to a leader.
Fundamentals(23)
- AGI (artificial general intelligence)AGI (artificial general intelligence) refers to an AI system able to match or exceed humans at most cognitive tasks, not just one. There is neither a shared definition nor an accepted test to say it has been reached: each lab uses its own.
- Artificial intelligence (AI)Artificial intelligence (AI) refers to the set of techniques that enable a machine to perform tasks associated with human intelligence: recognising an image, understanding a text, predicting, deciding, writing. Under EU law, an “AI system” is identified by its capacity to infer: it infers from the input it receives how to generate predictions, content, recommendations or decisions (AI Act, Article 3).
- Computer visionComputer vision is the field of artificial intelligence that enables a machine to analyse images and video: recognising objects, detecting defects, reading a document, tracking movement. In Europe, its biometric uses (facial recognition, emotion recognition) are partly prohibited and partly classified as high-risk by the AI Act.
- Deep learningDeep learning is a form of machine learning that uses artificial neural networks made up of many layers, each processing data at a higher level of abstraction. It drives the progress in image recognition, speech recognition, translation and generative AI.
- EmbeddingAn embedding is the numerical representation of a word, a sentence, or a document, in the form of a vector in a space of several hundred or thousand dimensions. Two texts semantically close have geometrically close embeddings, which enables search by meaning rather than by keywords.
- Foundation modelA foundation model is a generalist AI model trained on massive, diversified data, which serves as a reusable base for dozens or even hundreds of different business applications, by adaptation (fine-tuning, RAG, prompt) rather than by specific training.
- Generative AIGenerative AI is a family of artificial intelligence models capable of producing new content (text, image, sound, video, code) from a natural-language instruction, as opposed to traditional AI which was limited to classifying, predicting, or detecting.
- InferenceInference is the usage phase of an AI model, during which the model computes a response from a given prompt. It is the operation billed by API providers, distinct from training which is an initial fixed cost.
- LLM (Large Language Model)An LLM (Large Language Model) is a type of artificial intelligence trained on text corpora of several hundred billion words, which produces natural language by predicting, word by word, the most probable continuation of a given text.
- Machine learningMachine learning is the branch of artificial intelligence in which a program learns to perform a task from examples, instead of following rules written by a programmer. It relies on a model whose parameters are automatically adjusted on data until it produces reliable results.
- MultimodalA multimodal AI model is a model capable of processing and producing several types of content simultaneously: text, image, audio, video, code. The same model can analyse a photo, understand a voice query, read a document, and reply in writing, without any intermediate pipeline.
- Neural network (artificial)An artificial neural network is a computing model made of small interconnected units, artificial neurons, organised in layers: each neuron combines its inputs according to weights, then passes a signal to the next layer. Learning consists of automatically adjusting these weights, from examples, until the network produces the right answers.
- NLP (natural language processing)NLP (natural language processing) is the field of artificial intelligence that enables a machine to analyse, understand and produce human language, written or spoken. Large language models (LLMs) such as ChatGPT or Claude are now its most visible form.
- OverfittingOverfitting describes an AI model that fits its training data so closely that it memorises it instead of learning general rules: it scores very well in internal tests, then makes mistakes on new cases. It is one of the most common reasons why a model that shines in a demo disappoints in production.
- ParameterA parameter of an AI model is a numerical value learned during training that determines the strength of connections between the artificial neurons of the network. The parameter count measures the model's capacity to store associations between the elements of its training corpus.
- Reasoning modelA reasoning model is a large language model trained to produce internal, step-by-step thinking before giving its answer. It is more reliable on complex problems (analysis, calculation, code, agent planning), but slower and more expensive, because its thinking tokens are billed.
- Reinforcement learningReinforcement learning is a machine learning method in which a system learns which actions to choose by trying them and receiving a reward or a penalty, so as to maximise a cumulative gain over time. Long associated with games and robotics, it is also used to adjust the behaviour of conversational assistants (RLHF) and to train reasoning models.
- RLHF (reinforcement learning from human feedback)RLHF (reinforcement learning from human feedback) is a training technique that adjusts a language model according to the preferences of people who compare and rank its answers. Popularised by InstructGPT (OpenAI, 2022) and then ChatGPT, it turned models that continued text into assistants that follow instructions, with a documented side effect: a tendency to agree with the user.
- Supervised learningSupervised learning is a machine learning method in which a model is trained on examples whose correct answer is known (a label), so that it can predict that answer for new cases. It is found in common predictive uses in business: customer scoring, fraud detection, automatic sorting of requests.
- TokenA token is the elementary unit of text that an AI model manipulates, generally a word fragment of 2 to 4 characters, depending on the model and the language. It is both the model's unit of computation and the AI providers' unit of billing, counted separately on input and output.
- TrainingTraining is the construction phase of an AI model, during which the model ingests a massive corpus and adjusts its billions of internal parameters to learn the statistical regularities of that corpus. It is the most expensive operation in a model's life cycle.
- TransformerThe transformer is the neural-network architecture, introduced by Google in 2017, that underpins nearly all current generative AI models. Its central innovation is the attention mechanism, which lets the model dynamically weigh the relative importance of words in a sequence.
- Unsupervised learningUnsupervised learning is a machine learning method that looks for structure in raw data, without any expected answer provided in advance: groups of similar items, unusual behaviour, variables that go together. It is mainly used to explore and segment (customers, products, transactions), and its results must be interpreted by people who know the business.
Interaction and usage(11)
- Chain of thoughtChain of thought means a language model writes out intermediate reasoning steps before giving its answer. Described in 2022 by Jason Wei and co-authors, it improves results on multi-step problems. Today's reasoning models do it on their own: asking for it in the prompt has become unnecessary, even discouraged.
- Chatbot (conversational agent)A chatbot (or conversational agent) is software that converses in natural language with a human, in writing or by voice, to answer questions or trigger tasks. Since August 2, 2026, the EU AI Act requires informing users that they are interacting with an AI, and the company remains responsible for what its chatbot says.
- Claude vs ChatGPT securityFor a leadership team, the security of Claude or ChatGPT depends less on the tool than on the plan chosen, enterprise or consumer, and on the usage framework set. By default, Anthropic and OpenAI do not use the data of their business offers and API to train their models.
- Context engineeringContext engineering is the discipline of choosing, at each step, the full set of information placed in an AI model's context window: instructions, documents, history, tool results, memory. It extends prompt engineering: a prompt is written once, a context is managed continuously, especially in agents working on long tasks.
- Context windowThe context window is the maximum amount of text an AI model can process in a single query, measured in tokens. It includes the prompt sent and the response to produce. Beyond this limit, the model loses access to the oldest information of the exchange.
- Conversation rolesConversation roles (system, user, assistant) are the labels assigned to each message in an exchange with an LLM. The system role defines the model's general behaviour, the user role carries the requests, the assistant role the model's past responses in the conversational history.
- HallucinationA hallucination is a factually false statement but formulated with confidence by a generative AI model. It stems from the statistical mechanism of generation: the model predicts the most likely next word, with no native capacity to verify. It is the main operational and legal risk of an enterprise AI deployment.
- Map and territoryThe map is not the territory. What you give an AI (prompt, files, context) is only a representation of the work. The real work happens elsewhere: in the code, the file, the constraints of the ground. The gap between the two is your unknowns. That is where, and almost only where, a mission derails.
- PromptA prompt is the natural-language instruction sent to an AI model to obtain a response. Its quality directly conditions the quality, relevance, and cost of the generated output. A good prompt is not long; it is precise, structured, and contextualised.
- Prompt engineeringPrompt engineering is the discipline of writing prompts methodically. It is not a rare profession but a transverse competence, which consists of formalising, testing, and standardising the instructions sent to an LLM to obtain reliable and reproducible outputs at the scale of an organisation.
- Prompting patternsPrompting patterns are formulation conventions that structure a prompt to orient the model's output. They do not exist as native LLM commands, only as guides for the writer. The distinction matters: no pattern triggers a feature, the effect comes from linguistic precision.
Advanced architectures(11)
- A2A (Agent2Agent)A2A (Agent2Agent) is an open protocol that lets AI agents built by different vendors discover each other, delegate tasks to one another and exchange results securely. Launched by Google in April 2025, it has since been governed by the Linux Foundation and complements MCP: MCP connects an agent to its tools, A2A connects agents to each other.
- AI APIAn AI API is a technical interface that lets a software application send requests to an AI model hosted by a provider, and retrieve its responses. It is the standard access mode to AI in enterprise, as opposed to local hosting of the model.
- DistillationDistillation is a technique that transfers the knowledge of a large AI model (teacher model) to a smaller model (student model), while preserving most of the performance. It enables the deployment of lightweight models with reduced inference cost, viable on more modest infrastructures.
- Fine-tuningFine-tuning is an adaptation technique for an already-trained AI model, which consists of continuing its training on a dataset specific to your use case. It modifies the model's internal parameters, in contrast to RAG, which simply injects context at query time.
- Function callingFunction calling is the ability of an AI model to invoke predefined functions or tools to execute actions in an external system. The model returns a structured object (JSON) rather than text, allowing the application to call the function and reinject the result into the conversation.
- MCP (Model Context Protocol)MCP (Model Context Protocol) is an open standard, introduced by Anthropic in November 2024, that lets an AI model connect to data sources and external tools in a uniform way. It avoids writing specific connectors for every model-application combination.
- MoE (Mixture of Experts)Mixture of Experts (MoE) is an AI model architecture that splits the network into specialised sub-models, called experts. For each token processed, a router dynamically selects a few experts, leaving the others inactive. The model has the capacity of a large model but the compute cost of a smaller one.
- Open-source modelAn open-source AI model is a foundation model whose weights and architecture are freely downloadable and usable under a more or less permissive licence (Apache 2.0, MIT or a custom licence). It contrasts with the proprietary model (Claude, GPT, Gemini), accessible only via API or app. The choice engages sovereignty, cost, and long-term flexibility.
- RAG (Retrieval-Augmented Generation)RAG (Retrieval-Augmented Generation) is an AI architecture that pairs a search engine across your documents with a generative model. The model answers by relying on citable business data rather than on its training knowledge alone.
- SLM (Small Language Model)An SLM (small language model) is a language model compact enough to run on a computer, a phone or a modest server, generally under 10 billion parameters. Less versatile than a large LLM, it is enough for many repetitive, well-defined tasks, at a much lower cost and latency.
- Vector databaseA vector database is a database specialised in the storage and retrieval of vectors (embeddings). It allows, for a given query, finding the most semantically close content in a corpus, without exact lexical match. It is the typical search engine of a RAG system.
Agents and automation(15)
- Agent orchestrationAI agent orchestration is the coordination of several specialised agents, each responsible for part of a complex task, so they collaborate towards a common goal. A central orchestrator (or a supervisor agent) distributes the sub-tasks, supervises their execution, and aggregates the results.
- Agent SkillsAn Agent Skill is a folder containing a SKILL.md file and, where needed, scripts and resources, which teaches an AI agent to perform a task your way. The agent first loads only the name and description, then the detail when the task calls for it. The format, created by Anthropic, is an open standard.
- Agentic AIAgentic AI refers to the ability of an artificial intelligence system to pursue a goal autonomously: it plans steps, uses tools (software, databases, browser) and adjusts its actions based on the results it obtains. Where generative AI produces an answer to a request, agentic AI carries a task through to completion, under a level of human oversight defined in advance.
- Agentic loopThe agentic loop is the iterative cycle of an AI agent that acts, observes the result, and decides on the next action, until it reaches its goal or a stopping criterion. Without limits or observability, it can diverge into an infinite loop and consume in a few minutes the equivalent of several days of normal use.
- AI agentAn AI agent is an autonomous system that combines a language model with external tools to execute multi-step tasks: it analyses a request, plans an action sequence, executes, observes, and adapts its strategy. It differs from an assistant that merely responds.
- AI agent platformAn AI agent platform is an environment that allows enterprises to create, deploy, and operate autonomous AI agents. Three families exist in 2026: developer SDKs (Claude Agent SDK, LangGraph, AutoGen), no-code platforms (n8n, Make, Microsoft Copilot Studio), and self-hosted open-source integrated products (OpenClaw, OpenHands).
- AI workflowAn AI workflow is the structured sequence of several AI inference steps to accomplish a complex task, usually with external tools interleaved. It can be pre-defined (linear chain) or dynamic (orchestrated by an agent), and is the dominant operational mode of mature AI deployments in enterprise.
- Augmented automationAugmented automation is the use of AI to assist a human worker in performing a task, rather than to replace them. The human remains decision-maker and responsible, the AI accelerates, prepares, or validates.
- Autonomous Claude CodeIn autonomous mode, you no longer drive every step of Claude Code: you set a stop condition once, and a check (an evaluator model or a script) verifies it after each turn until the work is genuinely finished. Four levers make this loop automatic: /goal, /loop, /schedule and Stop hooks.
- Claude CodeClaude Code is Anthropic's agentic coding tool, launched in preview in February 2025 and generally available in May 2025. It runs in the terminal, code editors, the desktop app and the browser. It implements an autonomous coding agent that reads, writes, and executes code via the Claude API. To distinguish from the Claude model itself: Claude Code is the tool, Claude the model that drives it.
- CopilotAn AI copilot is an assistant that suggests, proposes, and completes actions to the user, who keeps control of the final decision and execution. It is distinct from the AI agent, which executes autonomously. The copilot is a prudent mode of use, the agent a delegated mode of use.
- Deep researchDeep research is an AI agent feature that autonomously carries out a multi-step search on the web, sometimes in your documents, then writes a report with its sources. It works for a few minutes, where a chatbot answers in seconds. It can still be wrong: its sources must be checked.
- Evidence-based claim resolutionAI claim resolution hands a specialised agent the claims whose outcome depends on a proof: it gets the evidence at the source, verifies it, applies the business rules and resolves the case in the existing tool, never refusing alone, escalating with the proof in hand when the case is doubtful.
- Vibe codingVibe coding is a way of building software by describing what you want to an AI, in everyday language, and accepting the code it produces without reading or understanding it. The term was coined by researcher Andrej Karpathy in February 2025; it suits rapid prototyping, much less applications intended for production.
- Visual AI agentA visual AI agent is an agent that takes the image (photo, video, screenshot, scanned document) as primary input: it perceives visual content, extracts its meaning, decides, then triggers an action in business tools, where a classical agent acts on text alone.
Risks and governance(27)
- AI ActThe AI Act is European Regulation 2024/1689, the world's first legal framework to classify AI systems by risk level and to impose differentiated obligations: prohibition for unacceptable uses, strict requirements for high-risk systems, transparency for the rest.
- AI Act for leadersThe AI Act, in force since 1 August 2024, sorts AI systems by level of risk, and the Digital Omnibus postponed the high-risk obligations until the end of 2027. This delay does not excuse a leadership team from mapping its AI uses, training people (Article 4), keeping records and naming an owner.
- AI carbon footprintThe AI carbon footprint is the set of greenhouse gas emissions related to its life cycle: model training, component manufacturing, data centre operation, and inference at usage. It is becoming a significant consumption item on a global scale.
- AI governance in the CAC 40 (2026 study)In their fiscal 2025 universal registration documents, all 40 CAC 40 companies describe uses of artificial intelligence and 38 set out an AI strategy. Yet only 19 mention an AI policy or charter, 9 say they have made it public, and 14 name an executive body in charge of steering AI (WYP.agency study, October 2026).
- AI literacy (Article 4)AI literacy means the skills, knowledge and understanding that allow people to use AI systems in an informed way and to be aware of their opportunities, risks and possible harms. Since 2 February 2025, Article 4 of the AI Act has required every organisation that provides or deploys AI to take measures to develop it among its staff, without prescribing a standard training course or a certificate.
- AI sycophancyAI sycophancy is the tendency of a generative AI model to tailor its answers to agree with the user, sometimes at the expense of accuracy. It stems partly from training on human preferences, which rewards pleasing answers more than correct ones.
- AI-generated content labelling (Article 50)AI-generated content labelling refers to the signals, machine-readable (invisible watermark, metadata) or human-readable (label, notice), showing that an image, video, audio or text was produced or altered by AI. Article 50 of the AI Act has required it since 2 August 2026: providers technically mark the outputs of their generative systems, and organisations publishing deepfakes or certain public-interest texts must disclose it.
- Algorithmic biasAn algorithmic bias is a systematic deviation in an AI model, inherited from training data, design choices, or deployment context, that produces unfair or erroneous decisions to the detriment of a population group or a type of case.
- AlignmentAlignment is the set of techniques that aim to steer the behaviour of an AI model towards the goals and human values of its user or publisher. It turns a raw model, capable of producing anything, into a useful, honest assistant that refuses requests contrary to the rules set.
- Auditing AI maturityAuditing AI maturity means measuring what is actually used, what data flows through it and what framework exists, then placing the organisation between denial, prohibition, framing and governance. Without an inventory of usage, an organisation cannot claim it controls its AI risks.
- Bringing shadow AI under controlBringing shadow AI under control means writing a readable AI policy, providing an approved and properly hosted tool, and having a person review every high-stakes output. Train before you sanction.
- DeepfakeA deepfake is an image, sound or video generated or manipulated by AI that resembles a real person, place or event and can be mistaken for authentic. In Europe, the AI Act requires such content to be disclosed as artificial. In France, Article 226-8 of the Criminal Code punishes its dissemination without consent and without a mention.
- Delegating to AI agentsAn AI agent is not a tool you launch, it is a decision you delegate. Until the delegation doctrine is written, every agent built draws the line in place of the decision-maker, and the sum of those default, scattered choices is the company's real AI policy.
- Digital OmnibusThe Digital Omnibus is a package simplifying EU digital rules, presented by the European Commission on 19 November 2025. Its AI part, Regulation (EU) 2026/1744, in force since 27 July 2026, postpones the AI Act's high-risk obligations to 2 December 2027, while its data part, which would amend the GDPR, is still under negotiation as of 14 October 2026.
- Digital sovereigntyDigital sovereignty is the capacity of a company or a state to control its data, its infrastructure, and its AI tools, independently of foreign jurisdictions. It conditions confidentiality, regulatory compliance, and resilience to access disruptions dictated by external political decisions.
- Explainable AI (XAI)Explainable AI (XAI) covers the methods that make it possible to understand why an AI system produced a given result, and to explain it to the person concerned. In Europe it is also a legal requirement: since the CJEU judgment of 27 February 2025, a person subject to an automated decision can demand an explanation of the procedure and principles actually applied.
- Frugal AIFrugal AI refers to an AI service whose necessity compared with a less resource-intensive solution has been demonstrated, which applies good practices to reduce its environmental impact, and whose uses have been questioned to stay within planetary boundaries. This is the definition of AFNOR Spec 2314, a French framework published in June 2024 and free to download.
- GDPR and AIGDPR governs all personal-data processing by an AI system operated in the European Union or targeting people located there. Four obligations apply as priorities: explicit purpose, data minimisation, access and opposition rights, and impact assessment for high-risk processing.
- GuardrailsGuardrails are technical control layers, added at the input or output of an AI model, that detect and block undesirable behaviours: malicious prompts, data leaks, forbidden content. They are distinct from alignment, which acts on the model's default behaviour itself.
- Human in the loopHuman in the loop refers to a mode of operation in which a person approves, corrects or rejects each decision of an AI system before it takes effect. It differs from human on the loop, where the AI acts alone under the supervision of a human able to intervene, and from human out of the loop, where no human intervenes during operation.
- ISO/IEC 42001ISO/IEC 42001 is the international standard, published in December 2023 by ISO and IEC, that sets the requirements for an artificial intelligence management system (AIMS): policy, roles, risk and impact assessment, controls and continual improvement. It is voluntary and certifiable, but it is not a harmonised standard under the AI Act: an ISO 42001 certificate does not create a presumption of conformity with the EU regulation.
- Making generative AI legally safeBanning generative AI pushes use into the shadows, and leaving it alone exposes the company. A written charter, a validated tool and human review of outputs cover the GDPR, the AI Act and the company's liability without slowing anyone down.
- Prompt injectionPrompt injection is an attack that slips instructions into the text an AI processes in order to hijack its behaviour: ignore its instructions, disclose data, trigger an action. Ranked the top risk for LLM-based applications by OWASP (LLM01:2025), it mainly targets assistants and agents that read external content: web pages, emails, documents, CVs.
- Red teamingRed teaming, borrowed from cybersecurity, is the practice of testing an AI system by simulating adverse usage attempts: rule bypassing, sensitive-data extraction, forbidden-content generation. It aims to identify vulnerabilities before a malicious actor exploits them in production.
- Responsible AIResponsible AI is a way of designing, buying and using AI that respects verifiable principles: fairness, respect for rights and privacy, transparency, robustness, clearly assigned accountability and environmental restraint. The international reference is the five OECD AI Principles, adopted in May 2019 and revised in May 2024.
- Shadow AI Barometer 2026In 2026, the ungoverned use of generative AI at work is no longer marginal. Two-thirds of office professionals who use AI for work have done so with a tool they believed was not permitted, and 88% have shared work information with a public AI tool: emails, meeting notes, customer data.
- Training dataTraining data is the set of texts, images, code, and other content used to train an AI model. Its composition determines what the model knows, what it ignores, its biases, and its legal risks. A major part of current AI litigation concerns their provenance and lawfulness.
Business and adoption(14)
- AI crash-test of a board packAn AI crash-test runs your board pack through several cross-checked models before the meeting, to surface weak signals, internal contradictions and points that won't hold in the room. AI sharpens the reading; the decision stays yours.
- AI POCAn AI POC (proof of concept) is a short pilot project aimed at testing the technical feasibility and business value of an AI application before any industrial investment. According to estimates cited by RAND (2024), more than 80% of AI projects fail, twice the rate of IT projects without AI.
- AI ROIAI ROI, modelled on classic financial ROI, is a deceptively appealing metric for evaluating an AI deployment. Three structural confusions invalidate it: underestimation of hidden costs, lack of robust pre-AI baseline, and concentration on operational efficiency to the detriment of the other two dimensions of value.
- AI TCOAI TCO (total cost of ownership) is the real total cost of an AI deployment in enterprise, aggregating five items: inference model, infrastructure, application integration, operational supervision, and team cost. The visible cost (subscription, API consumption) is rarely its main item.
- AI use caseAn AI use case is a concrete application of artificial intelligence to an identified business process, with a measurable objective and a defined perimeter. The three most deployed use cases in enterprise in 2026 are software development assistance, customer service, and data analysis.
- Augmented productivityAI-augmented productivity refers to measurable production gains (volume processed, speed, quality) that a worker achieves when they benefit from generative AI assistance. Real-world studies show an average gain of about 15%, strongly concentrated on the least experienced profiles.
- Briefing a leadership team on AITo brief a leadership team on AI, start from what teams already do, not from a lecture on the technology. A good session sets out risk, compliance and decision, then ends with written decisions.
- Choosing an independent AI advisorAn AI advisor is independent when it takes no vendor commission, sells no tool or deployment, cross-checks several models and has a person sign every deliverable. For an SME or a mid-market company, that is what keeps the advice in service of the business and not of a catalog.
- Cost per tokenCost per token is the elementary economic unit of an AI deployment. Providers bill input tokens (your prompt) and output tokens (the model's response) separately, with a typical ratio of 1 to 5. Mastering this cost requires distinguishing unit prices, consumed volumes, and optimisation levers.
- LLM evaluation (evals, LLM as a judge)LLM evaluation (“evals”) means measuring the quality of a model's or AI assistant's answers on a set of representative cases, against success criteria set in advance. LLM as a judge is one of its methods: a second model grades the first one's answers, which makes evaluation possible at scale, at the cost of known biases.
- Shadow AIShadow AI refers to the use of generative AI models by employees without validation or framing from their company. Widespread in 2025-2026, it creates legal, financial (leakage of business data to public models), and quality (unaudited outputs integrated into client deliverables) risks.
- Strategic AI framing in 10 daysA strategic AI framing for general management takes 10 working days, at a fixed fee of €2,900 excl. VAT. It delivers an exposure map, a written usage policy, a validated and properly hosted tool, and a human oversight matrix.
- Usage disciplineAI usage discipline is the set of rules, methods, and habits by which a company orients the daily use of AI by its employees: which use cases to activate, how to formulate requests, which models to use, how to audit outputs. It is the dominant lever of the real return of an AI deployment.
- WorkslopWorkslop is AI-generated work content that looks like good work but lacks the substance to meaningfully advance the task. The term was coined in September 2025 in Harvard Business Review by researchers from BetterUp Labs and the Stanford Social Media Lab; its cost falls on the person who receives the document and has to fix or redo it.
SEO and GEO(8)
- AEO (Answer Engine Optimization)AEO (Answer Engine Optimization) is a sub-discipline of GEO, focused on producing direct answers to user questions. It targets answer engines (Google AI Overviews, ChatGPT, Perplexity) that require a concise, sourced response, restituable as is, rather than a page to browse.
- AI Overviews and AI ModeAI Overviews are AI-generated summaries shown at the top of some Google results, with links to sources. AI Mode is a separate search experience for questions that call for exploration or comparison. Google announced them as available in France on 22 July 2026.
- CitabilityCitability is the property of a piece of content of being extracted, restituted, and attributed as is by an AI engine in its response to a user. It is built through writing (self-contained sentences), markup (Schema.org, useful though not required by Google), and topical authority (consistency and editorial density on a domain). It is the central objective of GEO.
- Conversational indexingConversational indexing is the process by which AI engines build their responses from web content: ingestion at training (dated corpus), retrieval at inference (RAG via web search), or both combined. It replaces the keyword indexing of classic engines with indexing by questions and intentions.
- Generative referenceA generative reference is the mention of a source in an AI engine's response to a user: textual citation, explicit attribution, or hyperlink to the original content. It adds to the classic organic link as a signal of editorial and brand visibility on the web.
- GEO (Generative Engine Optimization)GEO (Generative Engine Optimization) is the set of practices aimed at making content identifiable, trustworthy, and citable by generative answer engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews). It extends traditional SEO when users seek answers, not links.
- llms.txtllms.txt is a proposed web standard introduced in September 2024 by Answer.AI, providing AI models with a structured map of a website's content. Placed at the root of the domain, it is not a formal standard and its adoption by major LLMs remains uneven in 2026.
- Schema.org for AISchema.org is a standard structured semantic markup vocabulary (most often JSON-LD) that AI engines use to understand the nature of a web page: defined term, FAQ, article, product, event. Properly implemented, it makes the page easier for machines to read, without guaranteeing citation or enhanced display.
All terms from A to Z(109)
A
- A2A (Agent2Agent)
- AEO (Answer Engine Optimization)
- Agent orchestration
- Agent Skills
- Agentic AI
- Agentic loop
- AGI (artificial general intelligence)
- AI Act
- AI Act for leaders
- AI agent
- AI agent platform
- AI API
- AI carbon footprint
- AI crash-test of a board pack
- AI governance in the CAC 40 (2026 study)
- AI literacy (Article 4)
- AI Overviews and AI Mode
- AI POC
- AI ROI
- AI sycophancy
- AI TCO
- AI use case
- AI workflow
- AI-generated content labelling (Article 50)
- Algorithmic bias
- Alignment
- Artificial intelligence (AI)
- Auditing AI maturity
- Augmented automation
- Augmented productivity
- Autonomous Claude Code