Make.com OpenAI Integration: 7 Advanced Automation Examples

Why Make.com + OpenAI Integration Outperforms Traditional Automation

Traditional automation follows rigid if-this-then-that logic. The integration of generative AI through OpenAI into Make.com introduces a cognitive dimension that enables processing complex contexts, making nuanced decisions, and adapting responses based on content analysis.

According to OpenAI's State of Enterprise AI Report 2025, over 1 million businesses now actively use OpenAI's tools, with ChatGPT message volume growing 8x and API reasoning token consumption per organization increasing 320x year-over-year. This massive adoption is driven by measurable gains: enterprise users report saving 40-60 minutes per day using AI tools.

"83% of executives consider AI a strategic priority, and 75% believe their company might fail within five years without scaling AI." — Make.com Blog, 2025

The Make.com and OpenAI combination democratizes access to advanced AI capabilities without requiring a team of data scientists. According to Make.com, use of AI in workflows quadrupled in 2024, with the OpenAI app now the second most used on the platform. This guide explores seven production-ready scenarios that leverage this powerful integration for real business outcomes.

Discover how our Make.com automation expertise can accelerate your AI transformation journey.

Scenario 1: Intelligent Lead Qualification with Contextual Enrichment

Manual lead qualification consumes valuable time and often remains superficial. This advanced Make.com scenario uses OpenAI to perform deep prospect analysis with contextual understanding.

Workflow Architecture

Trigger: New lead in your CRM (HubSpot, Pipedrive, Airtable)
HTTP Module: Enrichment via Clearbit or Apollo.io to obtain company data and LinkedIn profile
OpenAI GPT-4: Contextual analysis with structured prompt
Router: Distribution based on qualification score (A/B/C)
Conditional Actions: Sales assignment, personalized email sequence, or archiving

Optimized OpenAI Prompt

The prompt must structure analysis according to your business criteria:

Analyze this lead and provide structured qualification:

Lead data:
- Name: {{name}}
- Company: {{company}}
- Title: {{title}}
- Industry: {{industry}}
- Company size: {{employee_count}}
- Website: {{url}}
- Initial message: {{message}}

Qualification criteria:
1. Industry fit (our target: SMB manufacturing/services)
2. Decision-making authority (C-level, director, manager)
3. Purchase intent (urgency, budget mentioned)
4. Product fit (expressed needs vs our offering)

Provide as JSON:
{
  "score": "A/B/C",
  "justification": "2-3 sentences",
  "strengths": ["list"],
  "potential_objections": ["list"],
  "recommended_approach": "sales strategy"
}

Measurable Results

This scenario enables:

75% reduction in qualification time (from 15 minutes to 3-4 minutes per lead)
25-30% increase in conversion rate through intelligent prioritization
Automatic personalization of initial outreach based on detected profile

Scenario 2: Multilingual Content Generation for E-commerce

For e-commerce businesses selling internationally, creating quality multilingual product descriptions is time-consuming. This Make.com workflow automates generation and SEO optimization across languages.

Complete Workflow

Trigger: New product added in Shopify/WooCommerce with basic description
OpenAI GPT-4: Generate SEO-optimized long description in source language
Translation Loop: Iterate over target languages (FR, DE, ES, IT)
OpenAI per Language: Translation with cultural adaptation and local SEO
CMS Update: Automatic injection of content into multilingual fields
Slack Notification: Alert marketing team for validation

SEO Generation Prompt

Create an SEO-optimized product description for e-commerce:

Product: {{product_name}}
Category: {{category}}
Specifications: {{specifications}}
Short description: {{base_description}}

Requirements:
- 300-400 words
- Naturally integrate keywords: {{keywords}}
- H3 structure for sections (Features, Benefits, Usage)
- Tone: professional yet accessible
- Include 2-3 concrete customer benefits
- Final call-to-action

Format: HTML with p, h3, ul, strong tags

Cultural Adaptation for Translation

For each language, a specific prompt ensures quality:

Translate this product description into {{target_language}} with cultural adaptation:

[Source description]

Guidelines:
- Adapt idiomatic expressions to local context
- Optimize for SEO keywords: {{local_keywords}}
- Respect typographic conventions of {{country}}
- Preserve HTML format
- Tone adapted to cultural expectations (e.g., more formal in DE, more emotional in IT)

This scenario reduces time from 2-3 hours per product to 10-15 minutes of validation, multiplying international market launch speed by 10x.

Scenario 3: Sentiment Analysis and Intelligent Support Ticket Routing

Support platforms like Intercom have seen impressive results with AI. According to their use case with OpenAI's Realtime API, latency decreased 48% and 53% of calls are resolved end-to-end. Let's adapt this approach to Make.com.

Support Workflow Architecture

Trigger: New ticket in Zendesk/Freshdesk or support email
OpenAI Analysis 1: Sentiment detection (positive/neutral/negative/urgent)
OpenAI Analysis 2: Thematic categorization (technical, billing, sales, other)
Complex Router: Sentiment × category matrix = 12 possible paths
Differentiated Actions:
- Urgent + Negative → Immediate escalation to manager + SMS
- Technical + Neutral → AI knowledge base + tier 2 agent
- Sales + Positive → Sales team with opportunity context

Advanced Sentiment Analysis Prompt

Analyze this customer support message with nuance:

Message: {{ticket_content}}
Customer history: {{previous_tickets_count}} tickets, average satisfaction {{csat_score}}
Customer value: ${{ltv}}

Required analysis:
1. Primary sentiment: positive/neutral/negative/urgent
2. Secondary emotions: frustration/confusion/satisfaction/anger/anxiety
3. Urgency level: 1-5 (1=routine, 5=critical)
4. Primary category: technical/billing/sales/product/other
5. Customer intent: resolution/information/complaint/cancellation
6. Churn indicators: yes/no with detected signals

JSON format:
{
  "sentiment": "...",
  "emotions": [...],
  "urgency": 1-5,
  "category": "...",
  "intent": "...",
  "churn_risk": true/false,
  "churn_signals": [...],
  "recommendation": "priority action"
}

Contextual Response Generation

For non-urgent cases, a second OpenAI module generates a first-level response:

Write a professional customer support response:

Context:
- Detected sentiment: {{sentiment}}
- Category: {{category}}
- Customer message: {{message}}
- Relevant knowledge base articles: {{kb_articles}}

Requirements:
- Tone: {{if negative sentiment: empathetic and reassuring, else: professional and helpful}}
- Structure: personalized greeting, problem acknowledgment, solution or steps, offer additional help
- Include links to {{kb_articles}} if relevant
- 150-200 words maximum
- Signature: {{company_name}} Support Team

Result: first response time divided by 3, first contact resolution rate +35%, customer satisfaction +20%.

Scenario 4: Automated Competitive Intelligence with Strategic Synthesis

Manual competitive monitoring is tedious and often incomplete. This Make.com scenario automatically collects, analyzes, and synthesizes competitor activity.

Multi-source Collection Architecture

Scheduled Trigger: Daily at 8:00 AM
Parallel Collection Modules:
- RSS feeds from competitor blogs
- Twitter/LinkedIn API for competitor account posts
- Google Alerts via email parsing
- Web scraping (pricing page changes, new pages)
Aggregator: Consolidation of all collected data
OpenAI GPT-4: Global strategic analysis
Distribution: Report to Notion/Google Docs + Slack alert for critical items

Strategic Analysis Prompt

Analyze this competitive intelligence and produce strategic synthesis:

Data collected today:
{{#each intelligence_data}}
Source: {{source}}
Competitor: {{competitor}}
Type: {{type}} (blog post/social post/site change/press release)
Content: {{content}}
Date: {{date}}
{{/each}}

Our company context:
- Industry: {{industry}}
- Positioning: {{positioning}}
- Strategic priorities: {{priorities}}

Produce structured synthesis:

1. KEY HIGHLIGHTS (3-5 most significant items)
2. COMPETITOR ANALYSIS (detected strategic moves)
3. INDUSTRY TRENDS (observed patterns)
4. POTENTIAL THREATS (risks to our position)
5. OPPORTUNITIES (gaps to exploit)
6. TACTICAL RECOMMENDATIONS (3 concrete actions to consider)

Format: Markdown with headings, bullet lists, bold for critical points

Intelligent Alerts

An additional module detects critical events requiring immediate response:

Assess criticality of this intelligence for immediate alert:

Today's synthesis: {{synthesis}}

Critical alert criteria:
- Direct competitor product launch
- Aggressive pricing change (>20% decrease)
- Major acquisition/partnership
- Massive marketing campaign
- Competitor reputation issue (opportunity)

JSON response:
{
  "critical_alert": true/false,
  "urgency_level": 1-5,
  "critical_elements": [...],
  "recommended_action": "suggested response",
  "deadline": "optimal reaction timeframe"
}

This workflow keeps you constantly informed without dedicating more than 10 minutes daily to reading the synthesis.

Scenario 5: Data Analysis Report Generation with AI Insights

Manual reports in Excel or Google Sheets take hours. This Make.com scenario automates extraction, analysis, and report generation with intelligent commentary.

Automated Analysis Workflow

Trigger: Scheduled (weekly/monthly) or webhook (on-demand)
Data Extraction: Google Sheets, Airtable, SQL database, or APIs (Google Analytics, Stripe, etc.)
Make Calculations: Basic aggregations, variations, averages
OpenAI Analysis: Data interpretation and insight generation
Visual Generation: Chart creation via API (QuickChart, Google Charts)
Compilation: Report assembly in Google Docs or PDF
Distribution: Email to stakeholders with executive summary

Advanced Data Analysis Prompt

Analyze this sales data and produce actionable insights:

Period {{period}} data:

Sales:
- Total revenue: ${{total_revenue}} ({{revenue_change}}% vs previous period)
- Transaction count: {{transaction_count}} ({{transaction_change}}%)
- Average basket: ${{avg_basket}} ({{basket_change}}%)
- Top 3 products: {{top_products}}
- Conversion rate: {{conversion_rate}}% ({{conversion_change}}%)

Customers:
- New customers: {{new_customers}} ({{new_change}}%)
- Returning customers: {{returning_customers}} ({{returning_change}}%)
- Retention rate: {{retention}}% ({{retention_change}}%)
- Average LTV: ${{ltv}}

Marketing:
- Acquisition cost: ${{cac}} ({{cac_change}}%)
- Campaign ROI: {{roi}}% ({{roi_change}}%)
- Top channels: {{top_channels}}

Produce structured analysis:

1. EXECUTIVE SUMMARY (3-4 sentences on overall health)
2. KEY PERFORMANCE (analysis of main KPIs with context)
3. DETECTED TRENDS (significant observed patterns)
4. ATTENTION POINTS (degrading metrics or risks)
5. OPPORTUNITIES (identified growth levers)
6. RECOMMENDATIONS (3-5 priority actions with estimated impact)

Tone: analytical yet accessible, precise numbers, relevant comparisons

Executive Summary Generation

A separate module creates a condensed version for executives:

Create a 100-word maximum executive summary based on this complete analysis:

{{complete_analysis}}

Format:
- 1 sentence on overall performance (positive/negative/stable)
- 2 most significant key figures
- 1 major trend
- 1 priority recommendation

Tone: direct, factual, decision-oriented

Time savings: from 4-6 hours of manual work to 15 minutes of report validation.

Scenario 6: AI Recruitment Assistant for Pre-screening and Feedback

Recruiting platforms using AI have seen remarkable results: 20% increase in started applications, 13% improvement in hire quality, and candidates applying 7x faster with 38% higher likelihood of being hired (source: OpenAI State of Enterprise AI Report 2025).

Recruitment Workflow Architecture

Trigger: New application via web form, email, or ATS (Lever, Greenhouse)
Resume Parsing: Structured data extraction (skills, experience, education)
OpenAI Evaluation: Fit analysis with job description
Qualification Router: A/B/C/Reject classification
Automated Actions:
- A Candidates: Interview invitation + personalized email
- B Candidates: Reserve pool with constructive feedback
- C Candidates: Polite rejection email with advice
ATS Update: Candidate profile enrichment with AI analysis

Application Evaluation Prompt

Evaluate this application for the following position:

JOB DESCRIPTION:
Title: {{job_title}}
Mission: {{mission}}
Required skills: {{required_skills}}
Desired experience: {{experience}}
Education: {{education}}
Soft skills: {{soft_skills}}

APPLICATION:
Name: {{name}}
Total experience: {{years_experience}} years
Education: {{candidate_education}}
Declared skills: {{candidate_skills}}
Relevant experiences: {{experiences}}
Cover letter: {{cover_letter}}

Evaluate according to these criteria (score 1-5 for each):
1. Technical skills fit
2. Experience level (junior/mid/senior expected)
3. Cultural fit (based on letter and background)
4. Growth potential
5. Apparent motivation

Produce as JSON:
{
  "overall_score": 1-5,
  "classification": "A/B/C/Reject",
  "detailed_scores": {"technical": 1-5, "experience": 1-5, "culture": 1-5, "potential": 1-5, "motivation": 1-5},
  "strengths": ["list 3-4 items"],
  "concerns": ["list 2-3 items"],
  "interview_questions": ["3 relevant questions to ask"],
  "recommendation": "decision and justification"
}

Personalized Feedback Generation

For rejected candidates, constructive automated feedback:

Write a professional and constructive rejection email:

Application: {{name}} for {{position}}
Main rejection reasons: {{concerns}}
Identified positive points: {{strengths}}

Requirements:
- Tone: respectful, encouraging, professional
- Thank for interest and time invested
- Mention 1-2 specific positive points
- Give general constructive feedback (without overly precise details)
- Encourage reapplying for other opportunities
- Wish continued success
- 150-200 words maximum

Avoid: vague formulations, false hopes, overly personal details

This scenario enables processing 10x more applications with equivalent or superior pre-screening quality while offering better candidate experience.

Scenario 7: Multichannel Marketing Content Creation with Personalization

The 2025 trend identified by Make.com is hyper-personalization using AI to create tailored content (videos, images, voice) from CRM data. This advanced scenario automates creation of personalized multichannel campaigns.

Campaign Generation Workflow

Trigger: Campaign launch (webhook) or CRM segment (new customers, anniversary, abandoned cart)
CRM Data Extraction: Detailed customer profile (name, purchase history, preferences, behavior)
OpenAI Strategy: Definition of optimal marketing angle per segment
Parallel Generation:
- OpenAI Email: Personalized email writing
- OpenAI Social: Adapted LinkedIn/Facebook posts
- DALL-E API: Personalized visual generation
- OpenAI SMS: Short impactful message
Human Validation: Slack notification with preview for approval
Distribution: Send via Mailchimp, Buffer, Twilio upon validation

Personalized Marketing Strategy Prompt

Define a personalized content strategy for this customer segment:

SEGMENT:
Type: {{segment_type}} (new customers/loyal/inactive/abandoned cart)
Size: {{customer_count}} customers

AVERAGE PROFILE:
- Average age: {{age}}
- Preferred categories: {{categories}}
- Average basket: ${{avg_basket}}
- Purchase frequency: {{frequency}}
- Last purchase: {{last_purchase}}
- Preferred channels: {{channels}}

CAMPAIGN OBJECTIVE:
{{objective}} (acquisition/retention/reactivation/upsell)

Define:
1. MARKETING ANGLE (emotional/rational approach, main benefit highlighted)
2. TONALITY (formal/casual/enthusiastic/informative)
3. RECOMMENDED OFFER (promotion type, products to highlight)
4. CALL-TO-ACTION (desired primary action)
5. PERSONALIZATION (variable elements to use per customer)

Structured JSON format

Personalized Email Generation

Write a personalized marketing email according to this strategy:

Strategy: {{strategy}}
Customer: {{first_name}}
History: {{purchase_history}}
Recommended products: {{recommended_products}}

Structure:
- Catchy subject line (40-50 characters, personalized with {{first_name}})
- Pre-header (subject complement, 80-100 characters)
- Email body:
  * Personalized greeting
  * Hook (problem/benefit/news)
  * Offer/product presentation with benefits
  * Social proof or urgency
  * Clear and visible CTA
  * PS with personalization element (e.g., "We noticed you like {{category}}")

- Tone: {{tonality}}
- Length: 200-300 words
- Format: Simple HTML (p, h2, strong, a)
- Include {{product_count}} product recommendations with links

Multichannel Adaptation

Each channel receives content optimized for its format:

Adapt this marketing message for {{channel}}:

Source email message: {{email_content}}
Strategy: {{strategy}}

{{channel}} requirements:
{{#if channel == "LinkedIn"}}
- 150-200 words maximum
- Professional B2B tone
- 3-5 relevant hashtags
- Engaging question at end of post
- Link to landing page
{{else if channel == "SMS"}}
- 160 characters maximum
- Direct and urgent tone
- Include short promo code
- Shortened link
- Opt-out mention
{{else if channel == "Facebook"}}
- Conversational tone
- Relevant emojis (2-3 max)
- Question for engagement
- Visual described for DALL-E generation
{{/if}}

Produce adapted content

Result: creation of personalized multichannel campaigns in 30 minutes instead of 2-3 days, with engagement rates 40-60% higher thanks to advanced personalization.

Best Practices for Optimizing Make.com + OpenAI Scenarios

Managing OpenAI API Costs

Intensive OpenAI usage can generate significant costs. Optimization strategies:

Intelligent Caching: Store responses for similar queries (e.g., identical product analyses)
Adapted Models: Use GPT-3.5-turbo for simple tasks, GPT-4 only for complex analyses
Token Limitation: max_tokens parameter to control response length
Batch Processing: Group requests when possible rather than individual calls
Monitoring: Make.com dashboard to track consumption per scenario

Output Quality and Reliability

"For reliable AI automations in production, prompt structuring and output validation are critical. A well-designed prompt reduces hallucinations by 70-80%." — Make.com best practices, 2025

Advanced techniques:

Strict Output Format: Enforce JSON with precise schema for easier parsing
Post-generation Validation: Make.com module verifying structure and critical values
Fallback Scenarios: Alternative paths if AI output is invalid or uncertain
Human-in-the-loop: Manual validation points for critical decisions
A/B Testing Prompts: Test different formulations and measure result quality

Security and GDPR Compliance

For European businesses, GDPR compliance is essential:

Data Minimization: Send only strictly necessary data to OpenAI
Anonymization: Replace names/emails with identifiers before AI processing when possible
OpenAI DPA: Ensure you have a Data Processing Agreement with OpenAI
Logs and Traceability: Maintain AI processing history for audits
Right to Erasure: Mechanism to delete processed data upon request

Monitoring and Continuous Improvement

Make.com + OpenAI scenarios require regular monitoring:

Performance KPIs: Execution time, success rate, cost per execution
Output Quality: Regular sampling and human scoring
Feedback Loops: Collect user feedback to improve prompts
Prompt Versioning: Document changes and their impacts
Proactive Alerts: Notifications if abnormal error rate or excessive costs

Conclusion: Scale Your Business with Intelligent Automation

The Make.com + OpenAI integration opens automation possibilities that were unthinkable just two years ago. The seven advanced scenarios presented in this guide demonstrate how generative AI can transform complex business processes into intelligent automated workflows.

Measurable benefits are significant: 40-60 minutes saved per day per user, increased output quality, personalization at scale, and reduced operational costs. According to Make.com, AI use in workflows quadrupled in 2024, and this trend shows no signs of slowing.

Keys to success:

Start with a high-impact scenario with manageable complexity
Invest time in designing precise and structured prompts
Implement monitoring and validation from the start
Iterate regularly based on feedback and performance data
Train your teams on AI possibilities and limitations

At Keerok, we help businesses design, implement, and optimize advanced Make.com scenarios integrating OpenAI and other AI tools. Our approach combines technical expertise and business understanding to create automations that generate measurable ROI from the first weeks.

Whether you want to automate lead qualification, optimize customer support, or create hyper-personalized marketing campaigns, get in touch with our automation experts for a free process audit and custom automation roadmap.

The future belongs to organizations that know how to combine human intelligence and artificial intelligence. Make.com + OpenAI is your accelerator toward this transformation. Learn more about our Make.com and Zapier automation services to start your journey today.