How to integrate AI into existing business software

1. Choose a workflow before choosing a model

Start with a repeated task and the person responsible for its outcome. A useful brief describes the input, the decision or output, the applications involved, and what happens when the result is wrong. “Help support staff draft a reply using approved product documents” is easier to evaluate than “add AI to our CRM.”

Check whether ordinary software is enough. A fixed calculation, a required-field check, or a predictable routing rule may be simpler to implement without a language model. AI is more relevant when the task involves varied language, document interpretation, or drafting that a person can review.

Choose one workflow for the pilot. Record the current time spent, common errors and review steps before building. Those observations give the team a baseline; they are not a promise that automation will improve every measure.

2. Pick the smallest integration that fits

There are three common starting points. An assistant generates a draft from information the user supplies. A retrieval system searches approved documents before drafting an answer. An action workflow can also call business APIs to create or update records. Each additional capability adds dependencies and testing work.

For changing company knowledge, document retrieval can supply relevant source material at request time. This pattern is called retrieval-augmented generation, or RAG. It still needs document ownership, update handling and evaluation; connecting a search index does not guarantee a correct answer.

• Drafting: useful for summaries and suggested replies when a person supplies the context and checks the result.
• Document search: useful when answers depend on approved manuals, policies or product information and should include source references.
• Controlled actions: useful when a reviewed result needs to become a CRM note, ticket update or another specific API operation.

3. Work through a CRM example

Consider a support representative preparing a reply to a customer. In this illustrative design, the customer record stays in the existing CRM. The representative opens an assistant inside the support workflow. A backend service checks their identity and retrieves only the records and documents they are permitted to use.

The assistant drafts a response and shows the supporting document references. The representative checks the customer details, the proposed resolution and the source material. If the evidence is missing or conflicting, the workflow asks for clarification or returns the task to a person.

Only after the representative approves the exact draft does the application call an allowed CRM operation. The backend rechecks permission, validates the record identifier and required fields, and records the result. A repeated request should not create duplicate notes. If the CRM times out, the interface should show an uncertain or failed write rather than claiming success.

This is a design example, not a claim about a client deployment. Your vendor APIs, authentication options, licensing and rate limits determine which parts can be implemented in your environment.

4. Make data and access part of the design

List each source, its owner, the users allowed to access it, and how changes or deletions reach the integration. Keep credentials in the backend. Use the signed-in user and server-side authorization rules to decide which records can be retrieved and which actions can run.

For shared document stores, apply tenant and user filters before material reaches the model. A prompt asking the model to keep information private is not an access-control boundary. Microsoft’s multitenant RAG guidance describes preserving identity through retrieval and enforcing filtering on each request.

Treat retrieved text and incoming files as information, not instructions that can grant tool access. Keep the set of allowed operations narrow. Review provider data processing, retention and deployment settings against the requirements for the actual data involved.

Further reading: Microsoft: secure multitenant RAG design.

5. Test the workflow, including failure cases

Create representative examples before tuning the system. Include ordinary requests, ambiguous wording, missing documents, outdated information, denied access and API failures. Keep a separate set of examples for checking changes so that improvements are not judged only on the cases used during development.

Evaluate retrieval and the final response separately. When the wrong document is retrieved, rewriting the answer prompt may hide the symptom without fixing the source. Microsoft’s RAG design guidance treats preparation, retrieval, generation and evaluation as parts of a complete solution.

Agree acceptance criteria with the workflow owner. Review whether the answer is supported, whether the correct user can access it, whether an approved write reaches the right record, and how long a completed task takes. Record disagreements for human review instead of hiding them inside one average quality score.

Further reading: Microsoft: RAG solution design and evaluation.

6. Budget for operation as well as the build

Implementation cost depends on the number of systems, the condition of the source data, authentication work, user interface requirements and the depth of testing. A fixed price or delivery date without those inputs is a weak basis for comparison.

Running costs can include model requests, document processing, search storage, application hosting, vendor licenses and human review. Ask for the usage assumptions behind an estimate: requests per month, typical document sizes, update frequency and peak concurrency. Compare cost per completed, accepted task rather than token cost alone.

Release to a small group first. Assign owners for failed requests, document freshness, vendor changes and access removal. Keep the existing manual path available. Expand only when the measured results and operating workload justify it.

Explore the relevant service

• AI integration services for existing software
• Enterprise RAG and document search
• AI agent development for controlled workflows