The AI Integration Checklist for Existing Business Software

A medical billing company bought an AI tool to auto-categorize incoming claims. Six weeks later, it still wasn't connected to their practice management system. The vendor blamed the company's "legacy setup." The company blamed the vendor's "incomplete documentation." Neither had checked whether the systems could talk to each other before signing the contract.

This happens constantly. Businesses pick an AI tool based on what it does in a demo, then discover it can't connect to the systems they already use. The integration step trips up more AI projects than the AI itself.

Below is the checklist we use before connecting any AI tool to existing business software. Run through it before you sign a contract, not after.

1. Check API Availability

Every AI integration depends on your existing software having an API (Application Programming Interface): a structured way for two programs to exchange data. If your software doesn't have one, AI can't connect without custom workarounds that add months and cost.

Log into your CRM, accounting tool, or project management platform. Search their help center for "API" or "integrations." Look for these three things:

• REST API or webhooks: the standard connection method. Neither means expensive integration.
• API documentation: public docs with endpoints, authentication, and examples. No docs means guesswork.
• Rate limits: how many requests per minute. A cap of 60/minute won't work for real-time processing of hundreds of records.

Some quick reference points: Salesforce has a mature REST API. QuickBooks Online has one, but QuickBooks Desktop does not (only a limited SDK). HubSpot offers a free API tier. Most modern SaaS tools have APIs. Legacy or on-premise software often doesn't.

2. Map Your Data Format

AI tools expect data in specific formats. Your existing software stores data in its own format. The gap between them is where integrations break.

Export a sample from your current system. Are dates stored as "02/14/2026" or "2026-02-14"? Are phone numbers formatted with dashes, parentheses, or plain digits? Are customer names one field or split into first/last?

A messy data situation multiplies here. If your CRM has three different date formats across 10,000 records, that cleanup happens before integration, not during. Budget time for a data transformation layer that converts your format into what the AI expects.

Will Your Systems Talk in Real Time?

Some integrations need real-time data flow: a chatbot pulling customer history during a live conversation. Others work fine with batch processing: an AI that categorizes support tickets every four hours.

The difference matters for cost. Real-time integrations require webhooks or persistent connections and cost 3-5x more to build and maintain. Batch integrations use simple scheduled API calls. Most businesses default to real-time when batch would work. That's an expensive assumption.

3. Authentication and Permissions

Connecting two systems means giving one system access to another. This is where security and IT teams get involved, and where projects stall.

Check what authentication your existing tool supports. OAuth 2.0 is the modern standard: specific permissions without sharing passwords. API keys are simpler but less secure. Some older systems require username/password credentials.

Practical questions to answer before starting:

• Who can generate API keys or configure OAuth apps? (Often IT, sometimes an admin role.)
• What permissions does the AI need? Read-only is different from write access that modifies data.
• Does your industry have compliance requirements (HIPAA, PCI, SOC 2) that restrict data flow?
• Will the integration use a service account or a specific user's credentials? (Service accounts are better for auditing.)

If your security requirements are strict, add two weeks to your timeline for the permissions review alone.

4. Test in a Sandbox First

Never connect an AI tool directly to your production system on day one. Every major business platform offers a sandbox or test environment: Salesforce has sandboxes, QuickBooks has a sandbox company, HubSpot has test portals.

Your sandbox testing should cover:

• Data read: Can the AI pull records correctly? Do all fields come through?
• Data write: If the AI creates or updates records, do they appear correctly?
• Edge cases: Missing fields, special characters, records that break expected format.
• Volume: An integration that works for 10 records might crash at 10,000.

Sandbox testing typically takes one to two weeks. One accounting firm skipped it and the AI duplicated 400 invoices in production. The cleanup took longer than the testing would have.

5. Build a Rollback Plan

Before going to production, document exactly how to turn the integration off. This isn't pessimism. It's standard engineering practice.

• How do you disconnect? (Revoke API key, disable webhook, toggle a feature flag.)
• Can you identify which records the AI created or modified? (Tip: tag AI-created records with a source field.)
• Who needs to be notified on rollback? (Your team, the vendor, downstream systems.)
• What's the manual process the AI replaced? Can your team resume it on short notice?

The best rollback plans are boring. A one-page document, a list of steps, and someone responsible for executing them. If your rollback involves "call the vendor and ask them to fix it," that's not a plan.

6. Define Success Metrics Before Launch

Write down what success looks like before going live. Not "the integration works" but "invoice processing time drops from 4 hours to 45 minutes within 30 days."

• Error rate: what percentage of records fail to sync? Aim for under 2% in month one.
• Latency: how long between an event in one system and the AI's response?
• Manual intervention rate: how often does a human fix something the integration got wrong?
• Business outcome: the time savings or accuracy improvement from your initial project scope.

Measure your baseline before turning on the integration. "Things feel faster" isn't a metric. Record how long the process takes today. Measuring AI ROI starts with knowing where you started.

7. Plan for Ongoing Maintenance

Integrations aren't set-and-forget. Software updates, API version changes, and data schema shifts all break integrations over time. Budget for these:

• API version monitoring: Most platforms deprecate old API versions every 12-24 months. Someone needs to watch for notices and update before the old version shuts off.
• Error alerts: An integration that silently fails for two weeks creates a data gap that's painful to reconcile.
• Monthly spot checks: Pull 10 random records and verify correctness. Automated monitoring catches crashes. Spot checks catch subtle data quality drift.

If you built the integration with a consultant, make sure your onboarding plan includes a handoff for ongoing monitoring. Don't assume the vendor will watch it forever.

The Quick-Reference Checklist

Before signing any AI integration contract, confirm each of these:

Nine items. If you can't check all nine before going live, you're not ready. Partial integration is worse than no integration because it creates the illusion that the AI is working while data falls through the cracks.

What This Saves You

The average failed AI integration costs $15,000-40,000 when you add vendor fees, internal time, and cleanup. Running this checklist takes a few hours. Most items surface problems early enough to fix them cheaply or to walk away before the contract starts.

The businesses that integrate AI well don't have better technology. They check compatibility before committing and test before trusting. That discipline is the difference between an AI integration that runs for years and one that gets abandoned in month two.