Generative AI in the enterprise: where to start

Almost every leadership team we speak with has the same question: we know generative AI matters, but where do we actually start? The technology moves quickly, the headlines move faster, and it is easy to spend months on pilots that never reach real users. Here is the practical path we follow with our clients.

1. Start with a problem, not a model

The best first projects share three traits: the work is repetitive, it depends on information you already have (documents, policies, emails, tickets), and a person can easily check the answer. Good examples:

• Answering staff questions from internal policies and procedures
• Drafting first versions of reports, summaries or customer replies
• Pulling key fields out of contracts, invoices or applications
• Searching across years of documents in plain language

Avoid starting with decisions that are high-risk, hard to verify or heavily regulated. Those come later, once you have the foundations in place.

2. Ground the AI in your own data

A general-purpose model knows a lot about the world and nothing about your organization. Retrieval-augmented generation (RAG) fixes that: the system first finds the most relevant passages in your own content, then asks the model to answer using only those passages, with references back to the source.

RAG gives you answers that are current, specific to your business and traceable, and it avoids training a model on sensitive data. The quality of the answers depends heavily on the quality of the content behind them, which is why data preparation matters as much as the model.

3. Build security and governance in from day one

Generative AI touches sensitive information, so treat it like any other enterprise system:

• Access control: people should only get answers from documents they are allowed to see.
• Data residency and privacy: choose enterprise services (such as Azure OpenAI, Amazon Bedrock or Google Vertex AI) that keep your data in your tenant and out of model training.
• Guardrails: filter harmful content, block sensitive data from leaving, and define what the assistant should refuse to do.
• Logging and review: record questions and answers so you can audit, improve and prove compliance.

4. Measure quality before you scale

“It looks impressive in the demo” is not a quality bar. Before a wider rollout, build a set of real questions with known good answers and test against it. Track accuracy, how often answers cite the right source, response time and cost per question. Re-run the same tests whenever you change prompts, models or content.

5. Keep people in the loop

The fastest route to value is usually AI drafts, people decide. Let the assistant prepare the first version and let your experts review and approve. This builds trust, catches mistakes early and gives you the feedback you need to keep improving.

6. Scale what works

Once a first use case is delivering measurable value, reuse what you built: the data pipelines, the security model, the evaluation tests and the user interface. The second and third use cases are much faster than the first.

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Thinking about your first generative AI project? We help organizations choose the right use cases, prepare their data and build secure, measurable solutions. Book a consultation and we’ll walk through what would work best for you.