Enterprise AI is different from trying a chatbot. It means AI running inside core processes, used by many people, connected to sensitive data and subject to audit. Getting there takes more than a good model. It takes governance, architecture and change management.
Step 1: Set up light governance first
Create a small AI working group with IT, legal or compliance, and two or three business leaders. Agree on:
- Which data can and cannot be sent to external AI services.
- Who approves new AI use cases.
- How outputs are reviewed and who is accountable for decisions.
- How incidents, such as a wrong answer to a customer, are reported and fixed.
Step 2: Build a use-case portfolio
Collect ideas from every department and score each on business value, feasibility, data readiness and risk. Pick two or three for the first wave, ideally in different departments so the organisation learns faster.
Step 3: Choose the platform
| Option | Strengths | Watch-outs |
|---|---|---|
| Public AI APIs | Fast to start, strong models | Data handling terms, cost at scale |
| Cloud AI platforms (Azure, AWS, Google Cloud) | Enterprise security, regional hosting options | More set-up work |
| Self-hosted open models | Full control of data | Hardware and skills required |
Many enterprises use a mix: a managed platform for most cases and self-hosted models for the most sensitive data.
Step 4: Get the data ready
Retrieval-based AI, where the model answers from your documents, depends on well-organised content. Remove outdated versions, tag documents by department and sensitivity, and make sure permissions in your document systems are correct, because AI should only show people what they are already allowed to see.
Step 5: Pilot with real users
Run each pilot with a group of real users for a few weeks. Measure time saved, accuracy and satisfaction. Collect the questions the AI could not answer; they reveal gaps in data and process.
Step 6: Integrate and scale
Successful pilots move into production with single sign-on, logging, monitoring, cost controls and support. Integrate AI into the tools people already use, such as your ERP, CRM, intranet or Microsoft Teams, instead of asking them to open yet another app.
Step 7: Train people and measure continuously
Short, practical training on what AI is good at, where it fails and how to check its work makes adoption much smoother. Review KPIs monthly and retire use cases that do not deliver.
Enterprise AI is a programme, not a project. If you want a partner for the first wave, our enterprise AI solutions team can help, and our guide on AI development vs off-the-shelf AI helps you decide what to build.
Frequently asked questions
How long does an enterprise AI pilot take?
A focused pilot typically runs for several weeks: set-up, a test period with real users and a review. Scaling across departments takes longer.
Can AI respect existing document permissions?
Yes, if designed properly. Retrieval systems should check the user's permissions before showing any content.