AI Integration Patterns: Connecting AI to Your Business Systems

You've seen the demos. You've experimented with ChatGPT. Now comes the hard part: making AI actually work within your existing business systems.

The gap between "AI can do amazing things" and "AI is doing amazing things for us" almost always comes down to integration. This guide walks you through the practical patterns for connecting AI to the systems your business already runs on.

The Integration Spectrum

AI integration isn't one-size-fits-all. Think of it as a spectrum:

Level	Approach	Complexity	Best For
1. Manual	Copy-paste between AI and systems	None	Testing, exploration
2. No-Code	Zapier, Make, Power Automate	Low	Simple workflows
3. Low-Code	Retool, n8n, custom forms	Medium	Moderate customisation
4. API-First	Direct API calls, webhooks	High	Full control, scale
5. Embedded	AI built into your product	Highest	Product differentiation

Most businesses should start at level 2-3 and progress as needs grow.

Pattern 1: The Webhook Listener

Use case: React to events in real-time (new form submissions, CRM updates, support tickets).

[Business System] → webhook → [AI Processor] → [Action/Response]

Example: When a support ticket is created in Zendesk:

1. Zendesk fires a webhook to your endpoint
2. AI analyses the ticket content, extracts intent and urgency
3. Auto-tags, prioritises, or drafts a response
4. Updates the ticket via API

Implementation options:

• No-code: Zapier/Make catches webhook, sends to OpenAI, updates Zendesk
• Low-code: n8n self-hosted flow with custom logic
• Code: Vercel/AWS Lambda function with direct API calls

Key considerations:

• Latency matters for customer-facing use
• Build in retry logic for API failures
• Log everything for debugging

Pattern 2: The Scheduled Processor

Use case: Batch processing on a schedule (daily report generation, weekly email summaries, data cleanup).

[Scheduler] → [Data Fetch] → [AI Processing] → [Output/Storage]

Example: Daily sales report:

1. Cron job triggers at 7 AM
2. Pulls yesterday's sales data from CRM
3. AI generates narrative summary with insights
4. Emails report to leadership team

Why scheduled over real-time:

• Lower cost (batch vs. per-event)
• More reliable (can retry failed batches)
• Easier to audit and debug
• Better for non-urgent workflows

Tools:

• GitHub Actions (free tier available)
• AWS EventBridge + Lambda
• Vercel Cron Jobs
• n8n scheduled triggers

Pattern 3: The Human-in-the-Loop

Use case: AI drafts, human approves (emails, content, decisions with consequences).

[Trigger] → [AI Draft] → [Human Review Queue] → [Approval] → [Action]

Example: Customer email responses:

1. Customer emails support
2. AI analyses and drafts response
3. Draft appears in approval queue (Slack, dashboard, or email)
4. Agent reviews, edits if needed, clicks approve
5. Response sent automatically

Why this pattern matters:

• Reduces legal/reputational risk
• Builds trust incrementally
• Creates training data for improvement
• Satisfies compliance requirements

Approval channels:

• Slack with interactive buttons
• Custom dashboard with queue
• Email with approve/reject links
• Microsoft Teams adaptive cards

Pattern 4: The RAG Pipeline

Use case: AI that knows your company data (internal knowledge base, product docs, policy questions).

[User Query] → [Vector Search] → [Retrieved Context] → [AI + Context] → [Response]

Example: Internal HR chatbot:

1. Employee asks "What's the policy on remote work?"
2. System searches vector database of HR docs
3. Retrieves relevant policy sections
4. AI generates answer grounded in actual policy
5. Cites sources for verification

Architecture components:

• Vector database: Pinecone, Weaviate, Supabase pgvector
• Embedding model: OpenAI text-embedding-3-small, Cohere
• LLM: GPT-4, Claude, or open-source
• Orchestration: LangChain, LlamaIndex, or custom

See our full RAG guide for implementation details.

Pattern 5: The Multi-Step Agent

Use case: Complex tasks requiring multiple tools and decisions (research, analysis, multi-system updates).

[Task] → [AI Plans Steps] → [Tool 1] → [Tool 2] → [Tool N] → [Result]

Example: Competitor research:

1. User requests "Analyse competitor X's pricing"
2. Agent plans: search web, visit pricing page, extract data, compare to ours
3. Executes each step, adapting based on results
4. Produces structured comparison report

When to use agents:

• Task requires multiple tools
• Steps depend on intermediate results
• Human would need 30+ minutes manually
• Output quality justifies complexity

See our AI Agents guide for more.

Integration Security Essentials

Every integration pattern needs security consideration:

API Key Management

• Never commit keys to version control
• Use environment variables or secret managers
• Rotate keys regularly
• Use least-privilege access

Data Minimisation

• Only send necessary data to AI
• Strip PII when possible
• Consider on-premise/private AI for sensitive data
• Log what you send for audit trails

Rate Limiting

• Implement client-side rate limits
• Handle 429 errors gracefully
• Queue requests during spikes
• Monitor usage against quotas

Choosing Your Integration Tools

No-Code Platforms

Platform	Best For	AI Integrations	Pricing
Zapier	Simple automations	OpenAI, Claude, many	Free tier, then $20/mo+
Make	Complex logic	OpenAI, custom HTTP	Free tier, then $9/mo+
Power Automate	Microsoft ecosystem	Azure OpenAI, Copilot	Included with M365

Low-Code Platforms

Platform	Best For	Self-Host	Pricing
n8n	Full control	Yes	Free (self), $20/mo (cloud)
Retool	Internal tools	Yes	Free tier, then $10/user/mo
Pipedream	Developers	No	Free tier generous

Code-First Options

Approach	Best For	Complexity
Vercel Functions	Quick deployments	Low
AWS Lambda	Enterprise scale	Medium
Cloudflare Workers	Edge performance	Medium
Self-hosted	Full control	High

Real-World Integration Example

Let's walk through a complete integration for a common use case: intelligent lead routing.

Goal: When a lead fills out a contact form, AI analyses their message, scores priority, and routes to the right team member.

Step 1: Capture the Lead

// Webhook endpoint receives form submission
{
  "name": "Jane Smith",
  "email": "jane@company.com",
  "company": "Acme Corp",
  "message": "We're looking to automate our invoice processing. Currently handling 500+ invoices/month manually."
}

Step 2: AI Analysis

const analysis = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{
    role: "system",
    content: `Analyse this lead and respond with JSON:
    - intent: what they're looking for
    - urgency: low/medium/high
    - deal_size: small/medium/large (estimate)
    - route_to: sales/support/technical
    - score: 1-100 priority score`
  }, {
    role: "user",
    content: JSON.stringify(leadData)
  }],
  response_format: { type: "json_object" }
});

Step 3: Route and Notify

// Based on AI analysis
if (analysis.score > 70) {
  await slack.postMessage({
    channel: '#high-priority-leads',
    text: `🔥 High-priority lead: ${lead.company}\n${analysis.intent}`
  });
}

await crm.createLead({
  ...leadData,
  score: analysis.score,
  tags: [analysis.route_to, analysis.urgency]
});

Result: What took 10 minutes of manual review now happens in 2 seconds, with more consistent scoring.

Getting Started: Your First Integration

If you're new to AI integration, here's the path of least resistance:

Week 1: Map Your Workflows

• List processes where AI could help
• Identify data inputs and desired outputs
• Note current systems involved

Week 2: Start with Zapier/Make

• Pick one simple workflow
• Use built-in OpenAI integration
• Get something working end-to-end

Week 3: Measure and Iterate

• Track time saved
• Note failure cases
• Identify improvements

Week 4: Decide on Next Steps

• Scale the working automation
• Add human-in-the-loop if needed
• Plan more complex integrations

Common Pitfalls

Over-engineering early: Start simple. You can add complexity later.

Ignoring errors: AI APIs fail. Build retry logic and fallbacks.

No monitoring: You need to know when integrations break.

Trusting AI blindly: Validate outputs, especially for consequential actions.

Forgetting costs: API calls add up. Monitor usage and optimise prompts.

Next Steps

AI integration is a journey, not a destination. Start with one workflow, prove value, then expand.

Need help connecting AI to your business systems? Get in touch — we specialise in practical AI integration that delivers measurable ROI.

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Related reading:

• No-Code AI Automation Guide
• Building AI Assistants with Tool Use
• AI Cost Optimization