Introduction
In today’s dynamic world, operational efficiency isn't just a bonus — it's a necessity. Many businesses spend too much time and money maintaining their Make.com automation workflows. Value Added Tech offers a smart solution to cut costs and save resources, helping our clients streamline their operations.
Challenges
- Excessive time and effort spent on identifying inefficient or outdated scenarios.
- Difficulty in optimizing scenario settings without a treasure trove of detailed logs and metrics.
- A foggy view of how efficiently their automation workflows were actually performing.
- A ballooning budget due to unnecessary operations triggered by these scenarios.
For instance, the scenario shown above used to check some parameters of a contact but then took no further action due to filters. That led to the operations spending with no further impact. Our system helps detect these instances, assess the impact of false triggers, and optimize them.
Our Solution
To ensure that operations are not wasted on scenarios that do not contribute to the overall efficiency, the Value Added Tech team crafted an AI-driven automated system tailored to work seamlessly with Make.com and Airtable.
Step-by-Step Implementation
- Kicking Off the Automation: We initiated an AI-based system that constantly monitors Make.com scenarios to identify inefficiencies.
- Blueprint Scrutiny: Our system dives into the scenario blueprints, examining general settings to understand how each scenario works.
- Zeroing in on Key Steps: Using AI, it identifies the crucial steps within each scenario that significantly impact data processing or transferring.
- Calculating Essentials: The system figures out the bare minimum steps necessary for a scenario to be deemed relevant, sifting out non-essential operations.
- Deep Dive into Scenario Logs: It meticulously reviews numerous recent scenario rounds to assess the data processed and the essentiality of each round.
- Efficiency Audit: Weekly exports highlight scenarios with the lowest efficiency, providing insight into false consumptions.
- Auto Task Creation and Analysis: Scenarios flagged as inefficient automatically generate tasks, complete with a detailed analysis of triggers and points of optimization.
- Developer Empowerment: Developers receive a package of relevant metrics and information, drastically cutting down the time and effort required for optimization by avoiding the slog through endless logs.
The Impact
Our solution delivered a host of invaluable benefits:
- Boosted Operational Efficiency: The system saved our clients 600,000 operations within just the first days of use. We predict that the savings will exceed 3 million operations by the end of the first month.
- Slashed Costs: This efficient automation setup leads to an almost immediate return on investment, slashing operational costs by roughly $3000 per month.
- Proactive Scenario Detection: The intelligent system unearthed obsolete scenarios that were silently running in the background, consuming resources without adding value. Moreover, it flagged inefficient, amateur-constructed automations that were unnecessarily draining Make.com operations.
- Comprehensive System Support: The solution equipped the client with the necessary insights and metrics to maintain and manage large-scale Make.com systems efficiently across multiple stakeholders.
Beyond Immediate Gains
The project’s positive outcomes extended beyond the obvious cost savings and time efficiencies. Here's what else we were able to achieve:
- Enhanced Team Efficiency: Developers no longer needed to rummage through extensive log files to pinpoint issues. Our AI-driven metrics provided them with precise, actionable insights, freeing them up to focus on higher-value tasks.
- Scenario Lifecycle Management: Our system helped in identifying scenarios that had become redundant over time, ensuring that the client's automation framework was always sharp and up-to-date.
- User-Created Inefficiencies: We discovered several scenarios created by non-developer employees for their own tasks, which were not optimized and consumed more resources than necessary. Identifying and optimizing or eliminating these scenarios helped in reducing operations costs significantly.
- Scalability and Adaptability: The solution's adaptability allows for easy scaling and tweaks according to evolving business needs and new automation workflows.
Clients Feedback
Our clients are thrilled with the outcome. The efficiency and savings achieved were beyond initial expectations, and the additional insights provided newfound visibility into their operational landscape. They appreciated the proactive nature of the system, which not only provided immediate financial benefits but also laid the groundwork for ongoing operational excellence. Furthermore, this is a compounding system that increases its efficiency and cost-effectiveness from month to month.
Conclusion
Saving money while enhancing efficiency can sometimes feel like chasing two rabbits at once — but not with the right technology and expertise. Value Added Tech AI-driven system for Make.com scenarios brought remarkable, quick wins for our clients, coupled with long-term operational streamlining. This system helps us onboard clients with a large volume of automation, making the process fast even if a client has hundreds of active scenarios. This case study underscores how leveraging AI to optimize automation is a powerful strategy that delivers both immediate and lasting value.
If you’re looking to cut down on costs, boost operational efficiency, and gain valuable insights into your workflows, don't hesitate to get in touch with Value Added Tech. Let's discuss how we can tailor our solutions to meet your unique needs. Reach out to us at sales@vatech.io.
How to Replicate This for Your Own Make.com Account
The system we built for this client uses Make.com's own API as the data source. Here's the architecture if you want to build something similar.
Step 1: Pull Scenario Data via Make.com API
Make.com exposes a REST API that lets you retrieve scenario details, execution logs, and operation counts. The endpoints you need:
GET /scenarios— lists all scenarios in your team with basic metadataGET /scenarios/{scenarioId}/blueprint— returns the full scenario structure as JSON, including all modules and their configurationsGET /scenarios/{scenarioId}/logs— returns execution history with operation counts, status (success/error/warning), and timestamps
Authentication uses a Make.com API token (generated in your account settings under API). All requests include Authorization: Token {your-token} in the header.
Step 2: Identify the "Essential Steps" in Each Scenario
This is where the AI component comes in. For each scenario, we send the blueprint JSON to GPT-4 with a prompt that asks it to:
- Identify which modules actually process or transform data (vs. modules that only route or filter)
- Determine the minimum number of operations a "useful" execution should consume
- Flag scenarios where the majority of executions consume fewer operations than the minimum threshold
A scenario that checks a contact record and then hits a filter that stops 95% of executions is consuming operations without producing value. The AI identifies these patterns from the blueprint structure.
Step 3: Analyze Execution Logs for Efficiency Ratio
For each scenario, we calculate an efficiency ratio:
Efficiency Ratio = (Executions that passed all filters / Total executions) × 100
A scenario with 10,000 monthly executions where 9,500 stop at the first filter has a 5% efficiency ratio. That's 9,500 wasted operations per month.
We pull 30 days of execution logs via the API, aggregate by scenario, and calculate this ratio for every scenario in the account. The results are written to an Airtable base.
Step 4: Weekly Efficiency Report and Auto-Task Creation
A Make.com scenario runs every Monday at 6 AM:
- Queries the Airtable base for scenarios with efficiency ratio below 20%
- Sorts by total wasted operations (highest first)
- For each flagged scenario, creates a task in the project management tool (Asana or Monday.com, depending on the client) with:
- Scenario name and ID
- Current efficiency ratio
- Estimated monthly operations wasted
- Estimated monthly cost of wasted operations (calculated from the client's Make.com plan cost per operation)
- The AI-generated analysis of why the scenario is inefficient and what to check
- Sends a Slack summary to the automation team channel with the top 5 worst offenders
Step 5: Optimization and Verification
Developers receive tasks with enough context to act immediately — no log-diving required. Common fixes:
- Move filters earlier in the scenario: If a filter stops most executions, it should be the first module after the trigger, not the fifth
- Use Make.com's built-in scheduling: Instead of a scenario that runs every 5 minutes and does nothing 90% of the time, use a webhook trigger or a longer polling interval
- Consolidate redundant scenarios: Multiple scenarios doing similar things can often be merged into one with router modules
- Archive obsolete scenarios: Scenarios created for one-off tasks that are still running on a schedule
After optimization, the efficiency ratio is recalculated the following week. The system tracks improvement over time.
What This Costs to Build and Run
The system itself runs on Make.com (meta, but accurate). The scenarios that monitor other scenarios consume roughly 50,000 operations per month for an account with 200+ active scenarios. At Make.com's Team plan pricing, that's approximately $15–20/month in operation costs to run the monitoring system.
For a client saving $3,000/month in wasted operations, the ROI is immediate.
The initial build took approximately 3 weeks: 1 week for the API integration and data model, 1 week for the AI analysis component and prompt engineering, 1 week for the reporting and task creation workflows.
Frequently Asked Questions
Does this work for small Make.com accounts? The system makes most sense for accounts with 100+ active scenarios and 500,000+ monthly operations. Below that threshold, manual review is faster than building the monitoring infrastructure.
What Make.com plan do you need? The Make.com API is available on all paid plans. The monitoring scenarios themselves require a plan that supports webhooks and scheduled triggers — the Core plan or above.
Can this be applied to n8n or Zapier? The concept applies to any automation platform with an API that exposes execution logs. n8n has a REST API with similar capabilities. Zapier's API is more limited but can expose task history. The implementation differs, but the efficiency ratio analysis logic is the same.
How long before you see savings? In our experience, the first weekly report identifies enough quick wins (filter repositioning, obsolete scenario archiving) to recover 20–30% of wasted operations within the first two weeks. The compounding effect comes from ongoing monitoring catching new inefficiencies as they're introduced.
