AIJan 10, 2024•7 min read

Automating Business Workflows with AI

How I helped a client save 20 hours per week by automating their repetitive tasks with custom AI solutions.

A recent client was spending 20+ hours weekly on repetitive data entry and document processing. Here's how we automated it all away using AI, saving their team for higher-value work.

The Problem

The operations team was manually handling:

•Email triage - Reading and categorizing 200+ daily emails
•Data extraction - Pulling information from PDFs and attachments
•Request routing - Assigning tasks to the right team members
•Response drafting - Writing standardized replies
•CRM updates - Logging interactions in their database

This consumed 4+ hours daily from their best people.

The Solution Architecture

We built an automated pipeline that handles the entire workflow:

code

Email Inbox
    ↓
AI Classification
    ↓
Document Processing
    ↓
Data Extraction
    ↓
Routing Logic
    ↓
Draft Generation
    ↓
Human Review (Optional)
    ↓
Response + Database Update

The Tech Stack

Component	Tool	Purpose
Workflow Engine	n8n	Orchestration and automation
AI Processing	OpenAI GPT-4	Document understanding
Database	Airtable	Flexible data storage
Notifications	Slack	Team alerts
Email	Gmail API	Inbox integration

Implementation Details

1. Email Classification

First, we categorize incoming emails using GPT-4:

code

const classifyEmail = async (email) => {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages: [{
      role: 'system',
      content: `Classify the email into one of these categories:
        - SUPPORT_REQUEST: Technical issues or questions
        - SALES_INQUIRY: Pricing, demos, partnerships
        - BILLING: Payment, invoices, refunds
        - SPAM: Irrelevant or marketing
        - URGENT: Time-sensitive matters

        Also extract:
        - Priority (1-5)
        - Key entities (names, companies, products)
        - Sentiment (positive/neutral/negative)

        Return JSON only.`
    }, {
      role: 'user',
      content: `Subject: ${email.subject}\n\nBody: ${email.body}`
    }],
    response_format: { type: 'json_object' },
  });

  return JSON.parse(response.choices[0].message.content);
};

2. Document Extraction

For PDFs and attachments, we use vision capabilities:

code

const extractFromDocument = async (base64Image) => {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages: [{
      role: 'user',
      content: [
        {
          type: 'text',
          text: `Extract all relevant data from this document.
                 Return structured JSON with:
                 - document_type
                 - date
                 - amounts
                 - parties_involved
                 - key_terms
                 - action_items`
        },
        {
          type: 'image_url',
          image_url: { url: `data:image/png;base64,${base64Image}` }
        }
      ]
    }],
    response_format: { type: 'json_object' },
  });

  return JSON.parse(response.choices[0].message.content);
};

3. Smart Routing

Based on classification, we route to the appropriate handler:

code

const routingRules = {
  SUPPORT_REQUEST: {
    queue: 'support-team',
    sla: '4 hours',
    priority_multiplier: 1.5,
  },
  SALES_INQUIRY: {
    queue: 'sales-team',
    sla: '2 hours',
    priority_multiplier: 2,
  },
  BILLING: {
    queue: 'finance-team',
    sla: '24 hours',
    priority_multiplier: 1,
  },
  URGENT: {
    queue: 'on-call',
    sla: '30 minutes',
    priority_multiplier: 5,
    notify: ['slack', 'sms'],
  },
};

const routeRequest = (classification) => {
  const rules = routingRules[classification.category];
  const priority = classification.priority * rules.priority_multiplier;

  return {
    ...rules,
    priority,
    assignee: getNextAvailable(rules.queue),
  };
};

4. Response Generation

For common requests, we generate draft responses:

code

const generateResponse = async (email, context) => {
  const response = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages: [{
      role: 'system',
      content: `You are a professional customer service representative.
        Generate a helpful, friendly response using this context:
        - Customer history: ${JSON.stringify(context.history)}
        - Relevant KB articles: ${context.knowledge}
        - Company tone: Professional but warm

        Match the customer's language if not English.`
    }, {
      role: 'user',
      content: email.body
    }],
  });

  return response.choices[0].message.content;
};

n8n Workflow

The complete n8n workflow looks like this:

code

Trigger: Email Received (Gmail)
    ↓
Filter: Not from team@company.com
    ↓
HTTP Request: Classify with GPT-4
    ↓
Switch: By Category
    ├── SPAM → Archive + Log
    ├── URGENT → Slack Alert + Route
    └── Others → Standard Processing
        ↓
    If Has Attachments:
        → Extract Data from Documents
        ↓
    Generate Draft Response
        ↓
    Create Airtable Record
        ↓
    Assign to Team Member
        ↓
    Slack Notification

Results

After 30 days of operation:

Metric	Before	After	Improvement
Hours/week on email	20+	3	85% reduction
Response time	4-6 hours	30 min	90% faster
Data accuracy	~85%	95%+	Fewer errors
Employee satisfaction	Low	High	Better work

Cost Analysis

code

Monthly costs:
- OpenAI API: ~$150
- n8n Cloud: $50
- Airtable: $20

Total: $220/month

Savings:
- 68 hours/month × $35/hour = $2,380

ROI: 10x return

Lessons Learned

•Start with one workflow - Don't try to automate everything at once
•Keep humans in the loop - AI handles triage, humans handle exceptions
•Monitor accuracy - Check AI classifications weekly at first
•Iterate on prompts - Prompts need refinement based on real data
•Plan for edge cases - Always have a fallback for unusual requests

When to Automate

Good candidates for AI automation:

•High volume - Hundreds of similar tasks daily
•Structured output - Clear categories or data fields
•Time-sensitive - Quick responses matter
•Repetitive - Same logic applied repeatedly

Poor candidates:

•Complex negotiations
•Emotional situations
•Legal/compliance critical
•Highly creative work

Conclusion

AI-powered workflow automation isn't about replacing people—it's about freeing them from repetitive tasks to focus on work that requires human judgment, creativity, and empathy.

The key is starting small, measuring results, and iterating. Even automating a single workflow can save hours every week.

Want to automate your workflows? Reach out!