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Knowledge hub · 6 guides

AI for the enterprise

Where AI pays off in a large organisation, the solutions that deliver it, and how to start safely.

What it is

Enterprise AI comes in two families. Generative AI (large language models such as GPT, Claude and Gemini) reads and writes language: it answers questions, drafts, summarises and, increasingly, acts as an agent. Predictive machine learning finds patterns in data to forecast demand, spot fraud or recommend the next action.

The organisations that succeed start from a business problem, ground AI in their own data, keep a person in the loop where it matters, and measure results. The data and governance groundwork usually takes longer than building the model.

Explainer

Where to start: value against effort

  • Higher · Lower

    Quick wins

    Proven, low-risk and quick to measure.

    • Meeting and case summaries
    • Internal knowledge assistant
    • Invoice and document capture

  • Higher · Higher

    Strategic bets

    Bigger payoff; needs data and change management.

    • Agents that act in core systems
    • Demand forecasting
    • Customer-facing assistants

  • Lower · Lower

    Nice to have

    Easy but small impact. Fine as learning projects.

    • Slide and email drafting
    • Images for internal use

  • Lower · Higher

    Avoid for now

    Hard, and unproven for you today.

    • Fully automatic decisions in regulated processes
    • Training your own large model
Begin top-left for quick, visible wins that build trust and data foundations, and fund one strategic bet alongside. Revisit the map every quarter; things move.

Explainer

How an enterprise AI assistant answers a question

  1. 1

    Question

    Someone asks in Teams, a portal or an app.

  2. 2

    Retrieve

    Search finds the passages that person is allowed to see.

  3. 3

    Ground

    Those passages and the instructions go into the prompt.

  4. 4

    Generate

    A language model writes the answer.

  5. 5

    Answer

    With citations, guardrails and a feedback button.

This pattern, retrieval-augmented generation (RAG), keeps answers grounded in your own documents, respects who can see what, and cites its sources.

Use cases

Where AI helps, function by function

Common, proven starting points. Most organisations pick two or three to begin.

Guides

Guides in AI for the enterprise

Generative AI & large language models

Models that read and write language, code and images, available as APIs or inside tools you already use.

  • Summarising and drafting at volume
  • Pulling fields out of unstructured text
  • Coding and IT productivity
Typical project: 3–8 weeksSee vendors

Knowledge assistants & enterprise search

Ask a question in plain words and get an answer from your own documents, with sources.

  • HR, IT and policy questions
  • Engineers searching manuals in the field
  • Sales teams finding past proposals
Typical project: 4–8 weeksSee vendors

AI agents

AI that doesn't just answer but acts: looks things up, fills in forms and completes tasks across systems.

  • Service requests that touch several systems
  • Back-office tasks with clear goals but varied inputs
  • Research and preparation work
Typical project: 4–10 weeksSee vendors

Machine learning & forecasting

Models that learn from your history to predict demand, risk, churn and more.

  • Demand and sales forecasting
  • Fraud, credit and churn risk
  • Pricing and recommendations
Typical project: 4–10 weeksSee vendors

Document AI & computer vision

Read invoices, forms and IDs automatically, and inspect images and video.

  • Accounts payable and claims
  • Customer onboarding and KYC checks
  • Quality inspection in manufacturing
Typical project: 8–16 weeksSee vendors

MLOps & AI governance

Run AI reliably and responsibly: deployment, monitoring, risk controls and compliance.

  • Regulated industries
  • Several AI projects across different teams
  • Customer-facing AI
Typical project: 4–10 weeksSee vendors

At a glance

GuideBest fitPricingTypical project
Generative AIDrafting, summarising, extracting and answering at scalePer token (the amount of text processed) through APIs; per user for packaged assistants3–8 weeks
Knowledge assistantsPolicies, manuals, contracts and knowledge bases that are hard to searchModel usage per question plus search costs; or per user for packaged products4–8 weeks
AI agentsMulti-step tasks across several systems, with clear goalsModel usage per task plus platform fees; packaged agents are often priced per conversation or action4–10 weeks
Machine learningForecasting, risk scoring, pricing and recommendationsMostly the cost of building; running costs are cloud compute and storage4–10 weeks
Document AIHigh-volume paperwork and visual inspectionPer page or per image processed, or the cost of building a custom model8–16 weeks
AI governanceAny organisation moving AI from pilots into productionMostly people and process, plus tools for monitoring and evaluation4–10 weeks

Glossary

Key terms

LLM
Large language model: AI trained on vast amounts of text to understand and write language.
RAG
Retrieval-augmented generation: fetch relevant company content, then answer from it.
Agent
An AI that plans steps and uses tools (APIs, systems) to complete a task.
Hallucination
A confident but wrong answer; grounding and citations reduce it.
Fine-tuning
Further training a model on your examples to change its style or skill.
Guardrails
Rules and filters that keep inputs and outputs safe and on-topic.

Also worth reading

Other areas

Find AI for the enterprise partners

IT service companies with these skills on their profile, every fact labelled with its source.

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