AI transformation glossary

Short, plain-English definitions of the terms we use when we build, run and optimise AI workflows.

AI transformation

The process of redesigning how a business operates so that AI systems take on meaningful parts of the work, measured by business outcomes such as cost, speed, quality and capacity rather than by the technology deployed.

AI workflow automation

Using AI models, usually large language models combined with integrations and rules, to carry out multi-step business processes such as triaging tickets, extracting data from documents or drafting reports, with humans reviewing where judgement matters.

AI agent

Software that uses an AI model to plan and take actions toward a goal, such as reading a support ticket, reproducing a bug and drafting a fix, rather than only generating a single response.

Human-in-the-loop

A workflow design in which people review, approve or correct AI output at defined checkpoints, so automation scales without giving up control over important decisions.

KPI-driven AI

An approach where every AI workflow is tied to a business metric (for example hours saved, turnaround time or share of queries resolved) and is tuned continuously against that metric after launch.

AI pilot

A time-boxed, limited-scope deployment of an AI workflow on one real process, used to prove value against agreed KPIs before scaling.

AI readiness assessment

A structured review of a team’s processes, data, systems, risks and skills to decide where AI can be applied safely and with the highest return.

Workflow bottleneck

The step in a process that limits overall throughput, often manual data entry, hand-offs, or repetitive investigation, and usually the best first candidate for automation.