AI Integration Readiness Checklist: 9 Questions Before You Connect Systems

AI Integration Readiness Checklist: 9 Questions Before You Connect Systems

AI integration readiness means knowing which systems need to exchange data, who owns each workflow, which failure modes matter, and how the integration will be tested before it reaches production users. The best AI integration projects do not start with model selection. They start with operational boundaries.

Most AI integration projects fail quietly. The demo works because a clean prompt is connected to one clean data source. The production workflow breaks because customer records live in three places, permissions are unclear, sales and support use different definitions, and nobody agreed what the AI system should do when confidence is low.

Use this checklist before you connect AI to CRM, ERP, support, sales, finance, recruiting, or internal workflow tools. It is written for US operators who need AI systems that can run inside a real business, not just impress in a pilot.

1. Which workflow is the AI integration responsible for?

Do not describe the project as "connect AI to Salesforce" or "add AI to support." That is too broad to test. Name the workflow in operational terms:

• Qualify inbound leads before a sales rep reviews them
• Draft support replies from approved knowledge sources
• Turn call notes into CRM updates
• Route finance exceptions to the right owner
• Summarize account health before renewal meetings

A useful AI integration has a start event, an end state, an owner, and a measurable outcome. If the workflow cannot be written as a handoff between systems and people, the integration is not ready.

For broader implementation planning, the AI implementation services page covers how to move from workflow selection to production rollout.

2. Which system is the source of truth?

AI integrations become risky when every connected tool is treated as equally authoritative. A CRM field, spreadsheet, support ticket, Slack message, and call transcript should not all have the same status.

Before build starts, decide which system owns each important object:

• Customer account
• Contact
• Opportunity
• Ticket
• Contract
• Product usage event
• Internal task
• Approval status

Then define whether the AI system can read, suggest, update, or trigger action against each object. Read access is very different from write access. Suggested updates are very different from automatic updates.

If no one can answer this, the integration should stay in analysis mode.

3. What data is allowed to enter the AI layer?

The safest AI integration is not the one with the most data. It is the one with the right data, the right permissions, and a clear reason each source is included.

For each data source, document:

• Why the AI system needs it
• Whether it contains customer, employee, financial, legal, or health data
• Whether the AI provider stores or trains on inputs
• Whether the workflow can run with redacted or summarized fields
• Which users are allowed to see the output

This matters because AI integrations often create new data paths. A user who cannot access a finance field in the ERP should not receive that field through an AI summary in Slack.

4. What should happen when confidence is low?

Every useful AI workflow has uncertain cases. The integration plan should define the fallback before the first API call is written.

Low-confidence cases can trigger several safe behaviors:

• Ask a human to review
• Route to a specialist queue
• Request missing information
• Create a draft but do not send
• Log the case for later tuning
• Refuse to update the source system

The mistake is pretending the model will always know. Production systems need escalation paths. If the escalation path is not designed, the business will invent one later through Slack messages, manual overrides, and messy exceptions.

5. Which actions can the AI system take without approval?

Separate read, draft, recommend, update, and trigger permissions. They are not the same risk level.

For example, an AI sales assistant might safely summarize a lead, draft a follow-up, and suggest a CRM next step. It may not be ready to change deal stage, send the email, or create a discount approval task without human review.

A good permission model usually starts with:

• Read only for sensitive records
• Draft only for customer-facing communication
• Human approval for updates that affect revenue, compliance, or customer experience
• Automatic action only for low-risk, reversible steps

The permission model should match the workflow, not the vendor demo.

6. How will the integration be tested with real edge cases?

A clean test set hides the risks. Build the first evaluation set from messy operational examples:

• Duplicate accounts
• Missing fields
• Old tickets with stale policy language
• Customers with multiple products
• Contacts with changed roles
• Conflicting notes from different teams
• Abandoned opportunities
• Support cases with legal or refund risk

Each test case should have an expected behavior. The AI system does not need to solve every case. It does need to behave safely and consistently.

The AI integration services page explains how integration work differs from a standalone AI prototype. For system-level connection work, see AI system integration.

7. What logs will prove the workflow is improving?

If the integration goes live without logs, the team will argue from anecdotes. Decide measurement before launch.

Useful logs include:

• Input source used
• Output generated
• Confidence or routing reason
• Human approval or rejection
• Edit distance between draft and final
• Time saved per workflow step
• Error category
• Downstream outcome

This is not about vanity dashboards. It is about knowing whether the integration is helping, where it is unsafe, and which prompts or rules need adjustment.

8. Who owns maintenance after launch?

AI integrations decay. Source systems change, field names change, policies change, teams invent workarounds, and vendors update APIs. Someone must own the operating loop after launch.

Assign ownership for:

• Prompt and policy updates
• Data mapping changes
• Access reviews
• Failed workflow triage
• Vendor API changes
• Evaluation set refreshes
• Monthly performance review

If nobody owns maintenance, the integration becomes another fragile automation that people stop trusting.

9. What is the smallest production workflow worth launching?

The first version should be narrow enough to control and important enough to matter. Avoid connecting AI to every system at once. Pick one workflow with a clear owner, a manageable risk profile, and enough volume to learn from.

Good first AI integration candidates include:

• Lead intake enrichment before sales review
• Support ticket summarization for internal triage
• Meeting note conversion into CRM updates for human approval
• Customer health summaries before renewal calls
• Internal knowledge retrieval for one department

Bad first candidates usually involve high-risk automatic decisions, unclear ownership, broad data access, or customer-facing output with no review path.

The practical readiness rule

An AI integration is ready to build when the workflow, source systems, data permissions, fallback behavior, test cases, logs, and maintenance owner are clear. If those pieces are missing, the next step is not a better model. The next step is operating design.

TWOHUNDRED builds AI integrations around production workflows, not isolated demos. If you are deciding whether to connect AI into your CRM, support stack, sales process, or internal operations, start with the workflow and risk map first. Then choose the technical path that can survive real users.

Related reading: AI workflow automation and AI implementation services.