Automation rarely fails at the beginning. It usually does later — when a “simple workflow” surprisingly turns into a business-critical system. In Part 1, we explored Zapier and Make from a business and organizational perspective. In this part, we move into implementation reality: data structures, conditional logic, APIs, maintenance, and long-term scalability.

Table of Contents:

1. From simple automations to automation systems
2. Data structure and field mapping in complex scenarios
3. Conditional logic, branching, and error prevention at scale
4. Working with custom APIs, weak documentation, and edge cases
5. Automation architecture and task/operation efficiency
6. Monitoring, logs, debugging, and long-term maintenance
7. Fixing broken or poorly designed automations
8. What not to automate with Zapier or Make
9. The next few years for Zapier and Make
10. Conclusion: rule of thumb

From simple automations to automation systems

In Part 1 we focused on decisions made before the first automation is built: budgets, team structure, expectations, and tool familiarity. But once implementation begins, the nature of automation changes quickly.

At the start, most workflows are linear and forgiving. A trigger fires, a few actions run, and everyone is happy. The real problems rarely appear on day one. They emerge later — when volume increases, data becomes inconsistent, edge cases appear, and processes stop being isolated.

“At some point, you’re no longer automating tasks,” explains Kris Zielinski, Founder of Seamless Automation and expert in Sales & Marketing Automation, CRM Integrations & Development. “You’re operating a system that needs to behave predictably under many conditions.”

This is also why many automation issues feel unexpected to businesses. The early phase creates a false sense of simplicity. When automation becomes embedded in sales pipelines, CRM logic, or operational workflows, implementation details suddenly matter more than tool popularity or onboarding speed.

Data structure and field mapping in complex scenarios

At a basic level, Zapier and Make look very similar when it comes to data handling. Both platforms expose available fields clearly, provide search, and allow users to map data visually between steps. For straightforward use cases, the experience is almost identical. The difference appears when workflows become non-linear or when data availability changes across steps.

“It’s very common that a field you need later isn’t available early in the scenario,” says Kris. “That can happen for many reasons — API behavior, conditional execution, or how an app structures its response.”

This is where a seemingly minor difference becomes critical. In Make, referencing such fields is relatively straightforward, even if they were not explicitly available in earlier steps. The platform’s data structure is more transparent and forgiving when working with custom or evolving schemas.

In Zapier, the same situation can become a blocker. Without an existing reference, manually constructing or referencing missing fields is significantly harder. According to Kris, this limitation has stopped some projects entirely. “It sounds like a small technical detail,” he notes. “But we’ve seen cases where a project was simply not feasible in Zapier, while Make handled it without much friction.”

For teams dealing with custom payloads, nested data, or dynamic schemas, flexibility at the data-mapping level is not a luxury — it’s a requirement.

Conditional logic, branching, and error prevention at scale

Both platforms support conditional logic. Zapier uses Paths, while Make uses Routers. Conceptually, they serve the same purpose: branching logic based on conditions. The difference emerges as complexity grows. “Once you move beyond a few conditions, the interface itself becomes part of the problem,” Kris explains.

In Zapier, each step occupies significant vertical space. As paths multiply, understanding the overall logic requires constant scrolling and mental reconstruction of the flow. This does not make complex automation impossible — but it makes it harder to reason about.

Make takes a different approach. Its visual model allows entire scenarios to be viewed as connected modules on a single canvas. As complexity increases, the scenario often becomes easier — not harder — to understand.

“With Make, growing complexity actually improves clarity,” says Kris. “You can see logic, exceptions, and data flow in one place.” This matters not only during implementation, but especially when changes are required months later — often by someone who didn’t build the original automation.

Working with custom APIs, weak documentation, and edge cases

Modern automation rarely relies only on simple integrations. Custom APIs, partial documentation, and inconsistent responses are the norm rather than the exception. Both Zapier and Make provide strong foundations here. Each supports custom API calls, either through dedicated app modules or generic HTTP requests. For standard REST use cases, both tools are capable. The advantage shifts again in advanced scenarios.

“One major difference is OAuth2 handling in custom requests,” Kris points out. “Make supports this natively. Zapier generally doesn’t.” For integrations that require authenticated custom calls — especially when working with internal systems or less common SaaS tools — this becomes a decisive factor.

Additionally, earlier differences start to compound here: interface clarity, field mapping flexibility, and visibility into payloads all matter more when dealing with complex or unreliable APIs. Errors caused by documentation gaps or human mistakes are easier to isolate and fix in environments that expose structure clearly.

Automation architecture and task/operation efficiency

As automation matures, success depends less on individual steps and more on architecture. “Good automation design is about avoiding unnecessary operations,” Kris explains. “Anything that doesn’t create business value should be questioned.”

Many workflows can be implemented in multiple ways. Some consume fewer operations. Others are easier to maintain. Others scale better. Choosing the right approach early can significantly reduce long-term costs — regardless of platform.

This architectural mindset is often missing in early automation efforts, where workflows are built incrementally, by adding another step. Over time, this leads to bloated scenarios that are expensive, fragile, and difficult to optimize. The key takeaway is platform-agnostic: thoughtful design lowers total cost of ownership more than any pricing plan.

Monitoring, logs, debugging, and long-term maintenance

For long-term maintenance, Make clearly stands out. “From a maintenance perspective, Make is simply better suited for complex systems,” says Kris. Its logs, execution history, and visual debugging make it easier to understand what happened and why. This shortens resolution time and reduces dependency on the original builder.

However, Zapier does have one notable advantage worth mentioning. “When many errors occur, Zapier allows you to inspect individual failed executions and rerun a specific one directly,” Kris notes. “Make processes executions sequentially, so you can’t selectively replay a single run out of order.”

This feature doesn’t change architectural decisions, but it can be helpful in operational firefighting. It’s a small difference — but a real one.

Fixing broken or poorly designed automations

The need to fix broken automation happens more often than most companies realize. Processes evolve, tools change APIs, business logic shifts. Automations that once worked perfectly can degrade quietly over time. “In practice, we’re often asked to fix or redesign existing automations,” says Kris.

Here again, Make’s visual clarity becomes an advantage. Complex condition trees, multiple branches, and exception handling are easier to audit and refactor when the entire logic is visible at once. Readability directly impacts repair speed. The clearer the scenario, the lower the cost of fixing it.

What not to automate with Zapier or Make

Not every automation problem belongs inside a no-code platform. Advanced scraping, heavy UI automation, and workflows that require navigating complex front-end behavior often demand standalone applications.

“We’ve built bots using Selenium when no API was available,” Kris explains. “That logic simply doesn’t belong inside Zapier or Make.” However, this doesn’t mean these tools are excluded entirely. In such cases, Make can still act as an orchestration layer — triggering bots, monitoring results, and integrating outcomes into broader systems. Automation is an ecosystem, not a single tool.

The next few years for Zapier and Make

Automation as a market is still growing rapidly. Demand is increasing across industries, from small teams to large enterprises.

“Both tools have a strong future,” Kris says. “Automation itself is only accelerating.”

Zapier will likely remain a dominant entry point for businesses new to automation. Make will continue to attract specialists and teams building more advanced systems.

Conclusion: rule of thumb

After dozens of real-world projects, one simple rule consistently holds:

• Simple automations + low technical knowledge → Zapier
• Simple automations + high technical knowledge → Make
• Complex automations + high technical knowledge → Make
• Complex automations + low technical knowledge → call a specialist

Tools don’t fail — decisions do. Zapier and Make are both excellent platforms when used in the right context. The real challenge is recognizing when automation crosses the line from convenience to infrastructure. At that point, expertise matters more than UI simplicity and architecture matters more than speed.

That’s the difference between automating tasks and building systems.

Need a low-code automation tool but don’t know which is best for your business? Or maybe you already have Zapier/Make but feel like you’re not using them to their full potential?

We can help!