AI Adoption Glossary: The Key Terms, Defined

A

Acceptance criteria

The specific, testable conditions an AI solution must meet to be accepted as done — thresholds for accuracy, latency, cost, safety and behaviour. They turn non-functional requirements into a pass/fail bar the build is measured against.

Agentic AI

AI systems that plan and carry out multi-step tasks with some autonomy — calling tools, querying systems and taking actions rather than only returning text. Agents raise the governance stakes because they act, not just answer.

AI Adoption Assessment

An expert, evidence-based evaluation of whether a company should start AI now, grading readiness and producing a go/no-go verdict. See should your company adopt AI and the AI Adoption Assessment product.

AI governance

The decision rights, policies, controls and accountability that determine how AI is built, approved, deployed and monitored — proportionate to each use case’s risk. See AI governance for adoption.

AI maturity model

A staged model describing how far an organization has progressed with AI, usually across five levels from experimental to transformational. See the AI maturity model.

AI readiness

How prepared an organization is to adopt AI successfully, measured across dimensions such as strategy, data, infrastructure, talent, governance and culture. See what is AI readiness.

AI Solution Blueprint

A complete functional and technical design package for an AI initiative, produced before implementation and executable by any competent delivery team. See what is an AI solution blueprint.

B

Build vs buy

The decision, made per use case, to build an AI capability in-house, buy an off-the-shelf product, or co-build with a partner — build the differentiator, buy the commodity. See build vs buy AI.

C

Change management

The training, workflow redesign and support that get people to actually adopt an AI system. Under-resourcing it is a leading reason technically successful builds fail at the last metre.

Cost of AI adoption

The full bill for adopting AI — not just the model, but data work, integration, change management and ongoing run cost. See how much AI adoption costs.

D

Data readiness

Whether an organization’s data is available, accessible, governed and of sufficient quality to power AI — often the single biggest blocker to scaling. See data readiness for AI.

Deployment model

Where and how an AI system runs — cloud, hybrid, private or sovereign — a strategy-level choice about control, cost and data residency. See cloud vs hybrid vs private vs sovereign AI.

Model drift

The gradual decay of a model’s quality as the world changes away from the data it was built on, requiring monitoring and retraining. (Filed here as “drift.”)

F

Fine-tuning

Further training a pre-trained model on a narrower dataset to specialize it for a task or domain — one option among prompting and retrieval for adapting a model to your needs.

Functional vs technical design

Two layers of a blueprint: functional design describes what the solution does for users; technical design describes how it is built. See functional vs technical design for AI.

G

Go/no-go

The company-level decision of whether to start AI now, defer, or not proceed — the job of the adoption assessment, distinct from per-initiative prioritization. See should your company adopt AI.

Guardrails

The controls that keep an AI system inside safe, correct boundaries — input/output filtering, policy checks, human-review points and hard red lines — specified in the blueprint and enforced in production.

H

Hallucination

When a language model produces fluent but false or fabricated output. Retrieval grounding, guardrails and human review reduce its impact in production systems.

Human-in-the-loop

A design where a person reviews, approves or can override AI output at defined points — the standard control for higher-risk use cases.

I

Implementation

Turning an approved blueprint into a running, adopted, operated system — the larger share of the cost, time and risk. See from blueprint to implementation.

Inference

Running a trained model to produce outputs (predictions, text, decisions) — the ongoing workload, and cost, of an AI system once it is live, as opposed to the one-time training.

L

LLM (large language model)

A model trained on vast text to generate and understand language; the engine behind most current enterprise AI applications, from chat to document processing.

M

MLOps

The engineering discipline of deploying, monitoring, retraining and maintaining machine-learning models in production — the machinery that keeps a model good after launch. See DevOps & platform engineering.

N

Non-functional requirements (NFRs)

The quality attributes a system must meet — accuracy, latency, cost, security, reliability, safety — expressed as measurable targets rather than features. See non-functional requirements for AI.

P

Pilot-to-production gap

The difficult transition where a pilot that worked on curated data fails to become a governed, integrated, monitored production system — where most AI projects stall. See why AI pilots fail.

Proof of concept (PoC)

A quick build to test whether an approach can work at all. Useful for learning, dangerous if mistaken for a production system — the two are engineered very differently.

R

RAG (retrieval-augmented generation)

Grounding a language model’s answers in an organization’s own documents, retrieved at query time, to improve accuracy and cut hallucination. See LLM applications & RAG.

ROI (return on investment)

The business return an AI initiative produces against its full cost — measured honestly with a baseline, not assumed. See how to measure AI ROI.

Roadmap

A sequenced plan of AI initiatives over a 12–24 month horizon, ordered by value, feasibility and dependency. See how to build an AI adoption roadmap.

S

Sovereign AI

AI infrastructure and data kept under a jurisdiction’s or organization’s own control for residency, security or regulatory reasons — increasingly a governance concern, not just an infrastructure one. See Sovereign Stack.

Strategic bet vs quick win

The portfolio balance between fast, low-risk AI wins that build momentum and larger, slower initiatives with bigger payoffs. See AI quick wins vs strategic bets.

T

TCO (total cost of ownership)

The full lifetime cost of an AI system — build, data, integration, run cost and maintenance — used to compare options honestly. See how much AI adoption costs.

Transformation strategy

The higher-level direction behind a roadmap — where AI should take the business and which model of change to pursue. See AI transformation strategy.

U

Use case

A specific business problem AI is applied to, ideally stated in one sentence with one metric. Finding good ones is the start of value. See where AI creates business value.

Use case prioritization

Ranking candidate use cases by business value against feasibility to decide what to build first. See AI use case prioritization.