Generative AI in the enterprise: from idea to dependable use

What is generative AI?

Generative AI is a family of systems that creates new output from learned patterns. That output can be a summary, answer, image, code or structured recommendation.

Fluency is not proof of correctness; quality depends on the model, Context, sources and controls around it.

Enterprise use cases

Document summarization, knowledge answers, drafting, extraction and support assistance are common uses.

Each use case needs a defined goal, approved data, an outcome owner and a human escalation path.

• Document search and summaries
• Grounded employee answers
• Drafting and extraction
• Classification support

Limits and risks

A model can produce unsupported output, see sensitive data in Context or interpret ambiguous requests too confidently.

Important decisions and actions need approved sources, access controls, evaluation and human review.

What is AI hallucination? (Read more)

How to start

Start with a specific problem: who needs what answer, what success looks like and what the impact of an error would be.

A bounded pilot with quality, cost and time metrics creates a safer path to scale.

What are AI Evals? (Read more)

Choosing a useful first use case

A good use case starts with a recurring question, data the organization is permitted to use, and an observable outcome. A sourced answer about an internal policy has clearer boundaries than a generic chatbot.

Define an example of an acceptable answer and several failure examples before building. They become the basis for measuring quality, cost and human escalation in the pilot.

A checklist before scaling

Set the business owner, knowledge sources, permitted roles, retention policy and error-reporting path before connecting a model. If an answer must be grounded, source display is part of the product definition.

After the pilot, measure more than conversation volume: track useful-answer rate, escalations, high-risk errors, cost per request and time to resolve the user's problem.

Scenario: answering internal-policy questions

Consider recurring questions about leave, procurement or information security. Instead of answering from memory, the system retrieves only approved policy documents and shows them with the answer.

People should be able to open the source and escalate an insufficient answer to the owning team. This makes the model an assistant for accessing knowledge, not the decision authority.

Evaluation should include ambiguous questions, outdated documents and questions with no source answer. A good outcome can be uncertainty or human escalation.