AI is full of jargon. This is the plain-English version — what each term means and what it can bring to your organisation.
Computer systems that perform tasks normally needing human intelligence — recognising patterns, predicting, understanding language and generating content.
What it brings: Lets you automate judgement-based work, not just repetitive tasks, freeing people for higher-value work.
AI that learns patterns from data and improves with experience rather than following fixed rules.
What it brings: Powers forecasting, recommendations and anomaly detection that sharpen the more you use them.
AI that creates new content — text, images, code, summaries — from a simple instruction.
What it brings: Produces first-draft copy, reports and designs in seconds, cutting production time and agency cost.
The technology behind tools like ChatGPT and Claude, trained on vast text to understand and generate language.
What it brings: Drafts documents, answers questions, summarises long files and powers assistants across a business.
Using historical data and AI to forecast what's likely next — demand, churn, failure, cash flow.
What it brings: Turns guesswork into planning: better stock, earlier warnings, fewer costly surprises.
Software that carries out repetitive, rule-based tasks without human effort.
What it brings: Removes hours of manual processing, reduces errors and lowers back-office cost.
AI that reads, categorises and responds to human language.
What it brings: Automates email triage, document handling and customer queries.
AI that interprets images and video — spotting defects, reading documents, monitoring safety.
What it brings: Enables 100% inspection and automated monitoring beyond human capacity at scale.
Conversational AI that answers questions and handles routine tasks around the clock.
What it brings: Improves response times and cuts cost-to-serve while freeing staff for complex work.
The instruction you give a generative AI, and the skill of writing it well.
What it brings: Determines output quality — the difference between generic and genuinely useful results.
AI predicting future demand for products, services or capacity.
What it brings: Reduces waste and stockouts and right-sizes staffing — a direct margin gain.
AI spotting unusual patterns that may signal fraud, error or risk.
What it brings: Catches problems rule-based systems miss, cutting losses and investigation time.
AI tailoring content, offers and experiences to each customer.
What it brings: Lifts conversion, basket size and loyalty.
The policies and oversight ensuring AI is used safely, legally and ethically.
What it brings: Protects the business on data protection, accuracy and reputation as adoption grows.
Using sensor data and AI to predict equipment failure before it happens.
What it brings: Cuts downtime and maintenance cost and extends asset life.
An AI assistant embedded in everyday software that helps you work faster.
What it brings: Speeds up routine knowledge work without changing the tools your team uses.
The automated flow that collects, cleans and moves data for AI to use.
What it brings: Good data is the fuel for AI; a solid pipeline makes everything else work.
A live virtual model of a physical asset or process, used to simulate and optimise it.
What it brings: Lets you test changes and predict outcomes without disrupting the real operation.
Executive-level expertise without executive-level overhead.
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