# Integrating AI into an existing application: a staged approach

Add AI to an existing application through workflow selection, architecture, permissions, interface design, evaluation and staged rollout.

Author: Binov

Canonical: https://www.binov.com/en/guides/integrate-ai-existing-application

Adding AI to an application starts with a specific use: finding information, preparing an action or supporting a decision. A focused workflow may be enough. Reviewing architecture and permissions establishes what can be added without weakening the product.

## Choose a useful moment

Find a task where users switch tools, search extensively or copy information. Describe the result needed at that point. The AI capability should fit into that sequence and make the next step clearer.

A chat box on every screen is not necessarily useful. A summary action, document search or editable suggestion may provide a simpler experience.

## Review the existing system

| Area | What to check |
| --- | --- |
| Identity | Authentication and user roles |
| Data | Available sources and excluded information |
| Application | Interfaces, dependencies and workflow state |
| Operations | Logging, monitoring and environments |
| Interface | Waiting, errors, correction and approval |
| Evaluation | Reference cases and expected outcomes |

Separate the new capability from its technical prerequisites. Document decisions so that a demonstration does not bypass established product rules.

## Distinguish suggestions from completed actions

Make it clear what AI proposes and what has actually been applied. Users should be able to inspect, edit or reject a proposed change. An error or timeout must not look like an empty successful result.

Preserve the user’s access permissions. A model call must not provide a route around the application’s restrictions. Keep secrets server-side and send the browser only the information it needs.

## Design a fallback

Define what users can do when the feature is unavailable. The main workflow should remain understandable. Retried operations should not create duplicate outcomes or apply the same change twice.

Provide a way to disable the capability independently of the whole product. This supports incident recovery and rollback when a change behaves unexpectedly.

## Roll out progressively

Start with identified users and representative examples. Observe corrections, abandonment, delays and actual use of the output. Check that a response that worked in isolation still helps in the context of the screen.

Expand against explicit criteria. Keep model, prompt, data and code versions traceable. Rerun a reference set to find regressions relevant to the workflow.

## Common questions

**Must we modernise everything first?** It depends on the interfaces and condition of the application. A targeted scope may be possible, though some prerequisites may need attention.

**Does AI remove business approval?** No. Controls should match the consequences of an action and the quality observed.

**Can we start with document assistance?** Yes, if retrieval addresses the need and document access is controlled.

Read about [enterprise RAG](/en/guides/enterprise-rag-knowledge-base) and [production readiness](/en/guides/ai-poc-to-production). Explore [product engineering with Binov](/en/services/product-engineering).