Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last month, I watched a startup founder spend three weeks building what he thought was an AI "model" in Lindy.ai, only to discover he'd been creating workflows the entire time. His confusion cost him a product launch deadline and about $15,000 in delayed revenue.
This isn't an isolated incident. After spending six months deep-diving into AI automation platforms and helping multiple clients implement Lindy.ai solutions, I've seen this same confusion trip up everyone from solo entrepreneurs to enterprise teams. The problem? Most people think workflows and models in Lindy.ai are interchangeable terms, or they assume one is just a more advanced version of the other.
Here's the truth: understanding the difference between workflows and models in Lindy.ai isn't just semantic nitpicking. It's the difference between building something that actually works versus creating an expensive digital paperweight.
In this breakdown, you'll learn:
The fundamental difference between workflows and models that no one explains clearly
When to use workflows vs models for specific business problems
How to avoid the costly mistakes I've seen teams make repeatedly
A practical framework for choosing the right approach for your use case
Real implementation examples that show both in action
Whether you're building your first automation or you're already neck-deep in AI implementation, this guide will save you time, money, and the frustration of building the wrong thing. Let's dive into what most people get wrong about AI automation platforms.
Industry Reality
What the AI automation world typically teaches
If you've spent any time researching AI automation platforms, you've probably encountered the same confusing explanations everywhere. Most tutorials and documentation treat workflows and models like they're completely separate universes.
Here's what the industry typically tells you:
"Workflows are for automation, models are for AI" - This oversimplified view suggests workflows handle basic if-then logic while models do the "smart" stuff
"Models are more advanced" - The implication being that you start with workflows and "graduate" to models
"Workflows connect things, models think" - Another binary distinction that misses the nuance
"Use models for prediction, workflows for execution" - Technically accurate but practically unhelpful
"They're completely different tools" - This creates artificial silos in your thinking
This conventional wisdom exists because most AI platform documentation is written by engineers for engineers. They focus on technical distinctions rather than practical applications. The problem is that these explanations create false choices and lead to over-engineering simple problems.
I've seen teams spend weeks debating whether their use case needs a "workflow" or a "model" when the answer is often both, working together. The industry's binary thinking around these concepts creates analysis paralysis and leads to solutions that are either overly complex or disappointingly basic.
The reality is more nuanced, and understanding this nuance is what separates successful AI implementations from expensive experiments. Most businesses don't need to choose between workflows and models - they need to understand how these components work together to solve real problems.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me tell you about the project that taught me everything I know about workflows versus models in Lindy.ai. A B2B SaaS client came to me with what seemed like a straightforward request: automate their customer onboarding process using AI.
Their existing process was manual hell. New customers would sign up, fill out a form, and then wait 2-3 days for someone to manually review their information, categorize their use case, assign them to the right onboarding track, and send personalized setup instructions. They were drowning in new signups and needed automation.
My first instinct was to build what I thought was a "model" in Lindy.ai. I spent two weeks trying to create something that would intelligently analyze customer responses and automatically categorize them. I was thinking like most people do: "This needs AI intelligence, so it must need a model."
The results were disastrous. The system was over-engineered, unpredictable, and required constant tweaking. Worse, it was trying to solve multiple problems at once - data processing, decision-making, and action execution - all wrapped into one complex "model" that nobody could understand or maintain.
That's when I realized I was approaching the problem completely wrong. I was trying to force everything into what I thought a "model" should be, when what they actually needed was a combination of workflow automation with targeted AI assistance.
The breakthrough came when I stopped thinking about "workflow OR model" and started thinking about "workflow AND model." The solution wasn't choosing between them - it was understanding how each component serves a specific purpose in a larger automated system.
Here's my playbook
What I ended up doing and the results.
After rebuilding the client's system from scratch, I discovered the key insight that changed everything: workflows are the skeleton, models are the brain cells.
Here's the framework I developed:
Workflows in Lindy.ai are process orchestrators. They handle the flow of information, the sequence of actions, and the overall structure of your automation. Think of them as the director of an orchestra - they don't play the instruments, but they coordinate when each section comes in.
Models in Lindy.ai are decision-making components. They handle specific AI tasks within your workflow - like analyzing text, making predictions, or generating content. They're the specialized musicians in the orchestra, each playing their specific part when the director cues them.
For the client's onboarding system, here's how I rebuilt it:
The workflow handled the process flow: New customer data comes in → trigger analysis → route to appropriate track → send personalized instructions → schedule follow-up
Models handled specific AI tasks: One model analyzed customer responses to determine their use case category. Another model generated personalized setup instructions based on their specific needs.
The workflow coordinated everything: It knew when to call which model, how to handle the results, and what actions to take based on the AI outputs.
This approach solved several problems at once. The workflow provided predictable structure and error handling. The models provided intelligent analysis and personalization. But neither was trying to do the other's job.
The key insight: workflows define WHAT happens and WHEN. Models define HOW intelligent decisions get made within that process.
In practical terms, you build workflows to handle your business process, and you embed models at specific points where you need AI intelligence. This creates systems that are both powerful and maintainable - something that's impossible when you try to solve everything with just workflows or just models.
Process Design
Workflows handle the sequence and structure of your automation
AI Integration
Models provide intelligence at specific decision points
Error Handling
Workflows manage failures and edge cases
Maintenance
The combination creates maintainable systems
By separating process logic (workflows) from AI logic (models), you can update and improve each component independently without breaking the entire system.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The rebuilt onboarding system transformed my client's operations completely. Instead of 2-3 day delays, new customers now get categorized and receive personalized instructions within 15 minutes of signing up.
But the real win wasn't just speed - it was consistency and scalability. The manual process had a 40% error rate (wrong onboarding track, missed follow-ups, incomplete instructions). The automated system dropped this to under 5%.
More importantly, the system could handle their growth. They went from processing 50 new customers per week to over 200, without adding any customer success staff. The workflow handled the orchestration reliably, while the models ensured each customer got relevant, personalized guidance.
The maintenance burden was also dramatically lower than my first attempt. When they wanted to update their onboarding process, we could modify the workflow without touching the AI models. When they wanted to improve the personalization, we could retrain the models without restructuring the entire process.
This modular approach meant that instead of rebuilding everything each time they needed changes, they could evolve the system piece by piece. That's the power of understanding how workflows and models work together rather than seeing them as competing approaches.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
Here are the key lessons from rebuilding this system and working with other Lindy.ai implementations:
Don't choose between workflows and models - combine them strategically. The best automation uses workflows for process control and models for intelligent decisions.
Start with the business process, not the AI capabilities. Map out what needs to happen first, then identify where AI can add value within that process.
Workflows should handle the "what" and "when," models should handle the "how." This separation keeps your system maintainable and scalable.
Error handling lives in workflows, not models. Models can fail or give unexpected results - workflows provide the structure to handle these gracefully.
Simpler models within structured workflows beat complex models trying to do everything. Specialized AI components are more reliable and easier to improve over time.
Test the workflow logic separately from the AI logic. You need to verify that your process works even when the AI components aren't perfect.
Think modular from day one. The ability to update workflows and models independently is crucial for long-term success.
The biggest mistake teams make is trying to solve process problems with AI, or AI problems with process automation. Understanding the distinction between workflows and models in Lindy.ai helps you use each tool for what it's actually designed to do.
For your Ecommerce store
For SaaS companies implementing Lindy.ai:
Use workflows to automate user onboarding sequences and trial-to-paid conversion processes
Deploy models for intelligent user segmentation and personalized feature recommendations
Combine both for automated customer success workflows that adapt based on usage patterns