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Overview
Make (formerly Integromat) has evolved from a visual automation platform into a capable AI workflow builder. With native AI modules and integration capabilities, Make enables GTM teams to build AI-powered automation without code. For teams wanting AI capabilities with a friendlier learning curve than n8n, Make offers a compelling middle ground.
What we'll cover:
- Make's AI features and how they work
- Core modules: AI assistants, text generation, and analysis
- Setup and configuration for GTM use cases
- Real limitations compared to alternatives
- How Make AI fits into a broader GTM stack
Make AI Features Overview
Make provides AI capabilities through dedicated modules that integrate with the visual workflow builder. Unlike platforms requiring custom code, Make's AI features work within its drag-and-drop interface.
Core AI Modules
| Module | What It Does | GTM Application |
|---|---|---|
| OpenAI Modules | Direct integration with GPT models | Content generation, analysis, classification |
| AI Text Generator | Generate text based on prompts | Email drafts, personalization lines |
| AI Text Analyzer | Extract information from text | Lead qualification, sentiment analysis |
| AI Image Generator | Create images via AI | Personalized visuals, content creation |
| AI Assistant | Conversational AI for complex tasks | Multi-step reasoning, research |
For teams building integrated GTM workflows, Make's visual approach reduces the engineering barrier to AI automation.
How Make AI Works
Visual Workflow Builder
Make scenarios (workflows) are built visually by connecting modules. AI modules fit into this flow like any other integration:
- Trigger fires (webhook, schedule, app event)
- Data flows through processing modules
- AI module receives data, generates output
- Output routes to next modules (CRM update, email send, etc.)
Prompt-Based Configuration
AI modules are configured with prompts that reference data from earlier in the workflow. You map fields from previous modules into your AI prompt, and the output becomes available for subsequent steps.
Make charges by operations (module executions). AI modules typically count as one operation, but the underlying API calls (OpenAI, etc.) are billed separately by those providers. Factor both costs into your calculations.
Example: Lead Qualification Workflow
- Trigger: New lead in HubSpot
- Enrichment: Pull additional data via API
- AI Analysis: Send lead data to AI module with prompt: "Analyze this lead against our ICP criteria: [criteria]. Return a score 1-100 and reasoning."
- Routing: Based on score, route to appropriate sequence or rep
- Update: Write score and reasoning back to CRM
GTM Use Cases
Automated Lead Scoring
Build AI-powered lead qualification that goes beyond rules-based scoring. AI can interpret unstructured data, reason about fit, and provide explanations—all within Make's visual interface.
Content Personalization at Scale
Generate personalized email content for each lead:
- Pull lead and company data
- Send to AI with personalization instructions
- Route generated content to email platform
This enables personalization at scale without custom development.
Intelligent Data Processing
Clean and enrich data using AI:
- Standardize company names and industries
- Extract structured data from unstructured fields
- Classify leads by segment or persona
Response Analysis
Analyze inbound responses with AI:
- Classify email replies (interested, not interested, wrong person)
- Extract meeting requests or questions
- Route to appropriate follow-up workflows
Setting Up Make AI Workflows
Connect AI Provider
Add your OpenAI (or other provider) API credentials to Make. This enables the AI modules to function.
Build Data Flow First
Create the scenario structure: triggers, data sources, and destinations. Ensure data flows correctly before adding AI processing.
Add AI Modules
Insert AI modules where intelligence is needed. Map input fields from previous modules into your prompts.
Craft Effective Prompts
Write clear, specific prompts with explicit output format requirements. Include examples when helpful. Test with sample data.
Handle Errors
Add error handling for AI failures. AI modules can timeout or return unexpected outputs. Build fallback paths.
Honest Limitations
Less Flexible Than Code
Make's visual interface is easier to use but less flexible than n8n or custom code. Complex AI agent workflows may hit the platform's limits.
No Native Agent Framework
Make doesn't have built-in AI agent capabilities like n8n. Multi-step reasoning requires chaining multiple modules manually, which can become complex.
Context Management
Each AI module execution is independent. Maintaining context across a workflow requires explicit data passing. Long conversations or complex reasoning need careful design.
No GTM-Specific Context
Like all general-purpose automation platforms, Make doesn't know your ICPs, personas, or messaging. That context must be injected into every prompt.
Teams using Make for GTM automation often pull context from external systems. Tools like Octave can provide ICP definitions, messaging playbooks, and qualification criteria via API. Your Make scenario calls Octave for context, then includes that context in AI prompts—ensuring consistency across all your automation.
Make AI vs. Alternatives
| Platform | Best For | Trade-offs |
|---|---|---|
| Make | Visual AI automation, non-technical users | Less flexible than code-based options |
| n8n | Custom AI agents, technical teams | Steeper learning curve |
| Zapier | Simple automation, largest app library | AI features less developed |
| Custom code | Maximum flexibility | Highest engineering investment |
Make excels for teams wanting AI automation without deep technical resources. For complex agent workflows, consider n8n or custom development.
Frequently Asked Questions
Make has native modules for OpenAI and supports other providers through HTTP modules. You can connect any AI API that accepts HTTP requests.
Make charges for operations (module executions). AI API costs (OpenAI, etc.) are separate and billed by those providers. Factor both into total cost.
Make scales reasonably well, but high-volume AI processing gets expensive (both Make operations and API costs). For very high volumes, evaluate cost-effectiveness against alternatives.
Yes, with proper error handling and monitoring. Many teams run production workflows on Make. Ensure you have alerts for failures and fallback handling.
Conclusion
Make brings AI capabilities to visual workflow automation. For GTM teams without deep technical resources, it enables AI-powered lead scoring, personalization, and data processing in a friendly interface.
But Make is an execution platform, not a strategy platform. It can run AI prompts; it can't provide the GTM context that makes those prompts effective. Teams getting the best results combine Make's automation with external context—ICP definitions, messaging playbooks, qualification criteria—from centralized systems.
If you're building GTM automation in Make and want consistent, context-aware AI across all your workflows, explore how Octave's context layer can provide the strategic foundation your automation needs.