The AI terms that come up in every deployment conversation.

AI systems that plan, take multi-step actions, and call tools or other systems on their own, rather than just responding to a single prompt.

Independent verification that an AI system actually does what it's claimed to do, at an acceptable and measured error rate, before and after deployment.

Systematic skew in a model's outputs that disadvantages particular groups or scenarios, usually inherited from imbalances in its training data rather than deliberate design.

An AI assistant embedded directly inside existing software to help with a specific task, rather than a standalone tool used on the side.

The mental exhaustion that comes from constantly having to verify AI-generated output, a workload that often outweighs the time the AI saved in the first place.

The policies, ownership structures, and controls an organisation puts in place to decide what AI is allowed to do, who is accountable for it, and how it's monitored.

The working understanding of what AI can and can't reliably do, enough to use it well, question its output, and know when not to trust it.

The logging, monitoring, and tracing infrastructure that lets you see what an AI system is actually doing in production: latency, cost, errors, and output quality.

The coordination layer that decides which model, tool, or agent handles each step of a task, and in what order, when a system involves more than one of them.

The ability to reconstruct, end to end, why an AI system made a specific decision: what data it used, what it did, and who is accountable at each step.

A standardised test used to measure and compare AI model or system performance against a defined task or dataset.

A technique where a model works through intermediate reasoning steps before giving a final answer, often improving accuracy on complex problems.

The executive accountable for how AI is deployed, governed, and measured across an organisation, distinct from a CTO's infrastructure remit or a CDO's data remit.

AI systems that interpret and act on visual input, images or video, such as detecting defects, tracking progress, or reading a document's layout.

The maximum amount of text, measured in tokens, a model can consider at once when generating a response, including your prompt, any retrieved documents, and its own reply.

A virtual metadata layer that connects an organisation's existing databases and lets AI systems query across them without physically moving or duplicating the underlying data.

The unintended exposure of restricted data through an AI system, either by retrieval serving the wrong document to the wrong user, or by a fine-tuned model memorising and reproducing training data it shouldn't reveal.

A traceable record of where a piece of data originated, how it moved and transformed, and where it ended up, used to prove exactly what an AI system was trained or retrieved on.

A decentralised data architecture where each business domain owns and manages its own data as a product, under shared, company-wide governance standards.

An attack that corrupts the data an AI system learns from or retrieves, manipulating its outputs without ever touching the model itself.

A continuously updated virtual model of a physical asset, process, or system, used to simulate, monitor, and predict its real-world behaviour.

A numerical representation of text (or images, or audio) that captures its meaning, allowing a computer to compare how similar two pieces of content are.

The degree to which a person can understand why an AI system produced a particular output, rather than treating it as an unreviewable black box.

Further training an existing model on a narrower, task-specific dataset so it performs better on your particular use case, tone, or domain.

A large, general-purpose model trained on broad data, meant to be adapted to many downstream tasks rather than built for one narrow job.

Anchoring a model's response to verifiable source material, so it answers from real, checkable information rather than from memory alone.

The technical and procedural checks placed around an AI system to constrain what it can say or do, catching unsafe, incorrect, or out-of-policy outputs before they reach a user.

When an AI model generates a confident, plausible-sounding answer that is factually wrong or entirely fabricated.

A design pattern where a person reviews, approves, or can override an AI system's output before it takes effect, rather than letting the system act fully autonomously.

The process of running a trained AI model to generate an output from a new input, as opposed to training, which is how the model learned in the first place.

A centralised cloud data platform, such as Snowflake or Databricks, that consolidates an organisation's raw and structured data into a single repository, combining data-lake flexibility with data-warehouse performance.

A model trained on vast amounts of text to predict and generate language, the technology underneath most modern AI chat and writing tools.

The time between sending a request to an AI system and receiving its response, a major factor in whether a use case actually feels usable.

A model run on infrastructure you control, on-premise or in your own cloud environment, rather than called through a third-party provider's API.

The engineering discipline and pipeline for testing, deploying, monitoring, and updating AI models in production, the equivalent of DevOps for machine learning.

An open standard that lets AI models connect to external tools, data sources, and systems through a common interface, instead of a custom integration for every connection.

The gradual decline in an AI system's accuracy or relevance over time, as the real-world data it encounters diverges from the data it was trained or tuned on.

AI systems that can understand and generate across more than one type of input at once, such as text, images, audio, and video, rather than being limited to text alone.

A commercial model where a provider is paid for a measured result, an issue resolved, a lead qualified, rather than for hours worked or a licence held.

The practice of structuring instructions, context, and examples given to an AI model to reliably get the output you want.

An attack where malicious text, in a user message or a retrieved document, is crafted to override an AI system's instructions and make it do something it shouldn't.

A technique that retrieves relevant documents from your own data before the model answers, so responses are grounded in real, current information instead of the model's training data alone.

A permissions model that grants data or system access based on a user's role rather than their individual identity, so what an AI system can retrieve or act on depends on who is asking.

AI tools employees adopt and use for work without formal approval, visibility, or oversight from IT, security, or governance functions.

Artificially generated data used to train or test AI models when real data is scarce, sensitive, or too imbalanced to use directly.

The unit of text a model actually processes, roughly a word or part of a word, used to measure context length, usage, and cost.

A database built to store embeddings and quickly find the ones most similar to a given query, the storage layer underneath most retrieval-augmented systems.

The situation where switching away from an AI provider becomes so costly or disruptive that you're exposed to their pricing, policy, and downtime decisions with no real alternative.

Building software by describing what you want in natural language and letting an AI model generate the code, rather than writing it by hand line by line.

AI systems that understand spoken input and respond in natural speech, increasingly in real time and across both text and voice within the same conversation.