3 Ways to Use AI in Your Make Scenarios
Contents
Workflow automation saves time by eliminating repetitive manual tasks. And for rule-based processes like support ticket management and lead capture, it works great. But when situations require nuance, judgment, or personalization, traditional automation hits a wall.
AI changes that. Make's AI features let you automate what logic alone can't handle. You can process unstructured data, generate personalized outputs, and handle edge cases that would otherwise need human judgment. For small businesses and entrepreneurs especially, this means doing setting up automated processes that would normally require hiring developers or building expensive custom solutions.
The result: less manual work, smarter automation, and more time spent on what actually matters.
This article will walk you through Make's AI features and how you can begin applying them to your scenarios.
The GPT Framework
AI automation can seem complex, but it actually follows a simple three-step pattern:
- GET your data from anywhere—emails, forms, databases, or APIs
- PROMPT an AI model to process, analyze, or transform it into what you need
- TRANSFER the results where they need to go—into systems, customers, or team members
Applying the 3-step GPT framework lets you spot opportunities for AI intelligence at any point in your workflow.
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The power of Make here is that you can execute all three steps—get, prompt, and transfer—within a single scenario. This allows you to turn the framework into a complete AI-powered automation without leaving the platform.
3 Ways to Apply AI to Your Make Scenarios
Once you understand the AI automation pattern, the next step is integrating these tools into your workflows. Make offers three main ways you can implement AI, each suited to different levels of complexity and control.
Let’s explore these options:
1 - Add Individual AI Modules to Your Scenarios
The simplest way to begin implementing AI in your workflows is with a single module. Make supports 400+ AI apps—OpenAI, Perplexity, Mistral AI, Synthesia, and many more—that you can add directly into your scenarios. Individual AI modules excel at targeted tasks where you need a quick injection of AI intelligence without setting up complex logic or multi-step workflows.
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Examples of when you might use individual AI modules:
- Drafting intelligent replies to customer inquiries based on email content
- Generating product descriptions from raw specifications
- Summarizing lengthy documents, articles, or support tickets for quick review
This approach is ideal when you want to test AI capabilities or power one specific step in your workflow at relatively low risk.
2 - Build AI Agents
For more sophisticated automation, you can build AI agents—workflows that use AI to make decisions and take actions across multiple steps. Instead of a single AI call, agents combine data retrieval, AI reasoning, and conditional logic to solve complex problems autonomously, adapting to different scenarios as they unfold.
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Examples of scenarios where you might benefit from AI agents:
- Reading incoming customer emails, classifying sentiment, drafting responses, and escalating urgent issues to your team
- Monitoring competitor websites, extracting pricing changes, and updating your internal database
- Analyzing incoming leads, scoring them based on fit, and routing hot leads directly to your sales team
AI agents transform your workflows from reactive to proactive, handling entire processes that would otherwise call for human oversight at multiple checkpoints.
Bear Tip 🐻: Make has a library of pre-built AI agents you can set up and start using right away!
3 - Launch an MCP Server
For teams that need tighter control and governance, a Make MCP Server can connect your preferred AI models directly to your scenarios. This architecture gives you the ability to define exactly how the AI behaves, set security boundaries, establish compliance standards, and maintain complete visibility into AI operations—essential for regulated industries or sensitive data.
Photo from Make.com
Examples of situations where an MCP server is an attractive option:
- Healthcare : Processing patient intake forms with HIPAA-compliant data handling, then triggering workflows based on structured outputs
- Financial services: Performing AI-powered loan analysis, ensuring compliance requirements, and maintaining audit trails
- Software : Analyzing specific data sources with AI and defining custom guardrails for sensitive customer information
MCP servers represent the most powerful and secure way to scale AI automation across your organization while maintaining full control over the process.
Conclusion
Artificial intelligence is changing the way we work. While rules and logic still handle plenty of workflows effectively, some require nuance or personalization. The key is knowing when AI is the right tool—and when it isn't. This discernment is the first step to building smart, resource-efficient workflows on Make that streamline your processes without adding unnecessary complexity.
Ready to get started with Make? Check out these tutorials to see the workflow automation platform (and Bannerbear) in action:
👉 5 Powerful Tips For Generating Bannerbear Images withMake.com
👉 Routers & Filters: How to Process Your Data Conditionally onMake.com
👉 How to Automatically Create New Podcast Episode Cover Art (withMake.com)
