An AI tool that lives in a separate tab gets used for a week. AI built into your CRM, helpdesk, store or team chat gets used every day. Integration is what turns an AI experiment into part of the business.
This article is part of AI for Business: 15 Practical Use Cases Across Different Industries — our overview of where AI delivers real value for businesses.
Where AI typically plugs in
| System | Examples | What AI adds |
|---|---|---|
| Website | WordPress, Shopify, Webflow, custom React/Vue | A chat widget answering from your content, capturing leads |
| CRM | HubSpot, Salesforce, Zoho | Leads created from conversations; personalised answers for known customers |
| Helpdesk | Freshdesk, Zendesk | Tickets created with full conversation context; triage and suggested replies |
| Databases | MySQL, PostgreSQL, MongoDB | Answers from live records; data extracted from documents written back |
| E-commerce | Shopify, WooCommerce product and order data | Product discovery, order status and returns help |
| Team chat | Slack, Microsoft Teams | An internal knowledge assistant where staff already ask questions |
| Your own apps | Mobile apps, portals, internal tools | AI features through a REST API |
Four integration patterns
1. Embed
Add an AI interface to an existing surface — a chat widget via one script tag, a Slack app, a panel inside your portal. Lowest effort, fastest value.
2. Read
Give the AI read access to documents and data so its answers are current: document folders indexed for retrieval, or specific database views for live figures.
3. Write
Let the AI create things in other systems — a CRM lead, a helpdesk ticket, extracted fields in a database — usually with a person approving at first.
4. Orchestrate
Multi-step flows across systems. This is agent territory and needs the strictest guardrails.
Principles for low-risk integration
- API-first and non-destructive. Connect through official APIs; don't modify core systems to fit the AI.
- Work with your existing database instead of copying everything into a new one.
- Least privilege. Read-only by default; write access only for specific actions.
- Proper credentials. OAuth tokens or scoped API keys, stored server-side — never in the chatbot's front-end code.
- Log every call the AI makes to another system.
- Gradual rollout. One team or one channel first, then widen.
This is how we approach adding an AI layer to existing platforms, as described in how we work, with integration details in our integrations FAQ.
A typical rollout
- Map the workflow: which systems the task touches, and where AI saves time.
- Check the APIs: what each system allows you to read and write, and any rate limits.
- Build read-only first: the AI answers and suggests; people act.
- Add write actions one at a time, with approval steps, then relax approval where accuracy is proven.
- Monitor: errors, API failures, costs and user feedback.
A focused AI integration into an existing product typically takes 3–4 weeks in our projects.
Common integration pitfalls
- Messy source data — duplicate customers or inconsistent product records produce inconsistent AI answers.
- Hidden permission leaks — an assistant that can read everything may show one user another's data.
- Brittle connections — no handling for timeouts or API changes, so the AI fails silently.
- No owner — integrations need someone to watch them after launch.
Next steps
Integration is usually part of a larger AI project — read how to build a custom AI solution for the full process, or see how integrations fit into an AI customer support system. To discuss your stack, contact us.