Why You Must Test Automation Before Going Live (A Cautionary Tale)

A wholesale distributor once flipped the switch on a brand-new order automation without testing it against real data. Within four hours, 340 duplicate invoices hit QuickBooks, shipping labels printed for phantom orders, and the inventory count dropped into negative numbers. The cleanup took eleven days. The lost trust from key accounts took longer. This is not a hypothetical; it is the single most common catastrophe we see in order-to-cash automation projects.

Testing automation before going live is not optional. It is the difference between a smooth launch and a business-stopping disaster. Yet the majority of teams skip it, either because they assume the workflow "looks right" in the builder or because the pressure to launch overrides caution. Here is the framework that prevents those nightmares.

The Three Stages of Pre-Launch Testing

Effective automation testing follows a progression: unit testing individual modules, integration testing end-to-end data flow, and load testing at production volume. Skipping any stage leaves a blind spot that will surface at the worst possible moment.

Figure 1 — The three-stage pre-launch testing framework and consequences of skipping each stage

Stage 1: Unit Testing Individual Modules

Start with every single module in your workflow running in isolation. If you have a step that parses an incoming email into structured JSON, feed it twenty different email formats: plain-text orders, HTML orders, orders with attachments, replies with forwarded threads, and malformed messages. Record what each one produces.

Common failures caught at this stage include date format mismatches (MM/DD/YYYY versus DD/MM/YYYY), quantity fields that silently truncate decimals, and address parsers that collapse multi-line addresses into a single field. In data entry automation, a single misfield at this stage cascades into every downstream system.

For each module, create a test matrix. Document the input, expected output, and actual output. Flag any deviation, no matter how minor. A one-character discrepancy in a SKU field today becomes a warehouse pick error tomorrow.

Stage 2: Integration Testing the Full Flow

Once individual modules pass, connect them and run the full workflow against a sandbox or test environment. This is where most hidden bugs emerge. A module that works perfectly alone may fail when it receives data shaped slightly differently by the previous step.

The critical tests at this stage are:

• Happy path: A standard order with all fields populated correctly. Verify it arrives in the destination system with every field intact.
• Edge cases: Orders with special characters, international addresses, zero-quantity line items, or extremely long product descriptions.
• Error paths: Simulate an API timeout mid-workflow. Does the system retry? Does it create a partial record? Does it alert anyone?
• Duplicate detection: Send the same order twice. Does your workflow create duplicates or correctly identify and skip the second one?

Run at least fifty test transactions through the complete flow. We have seen automations that work flawlessly for the first ten records and then fail on record eleven because of a pagination bug in the API call. Volume matters even at the integration testing stage.

Stage 3: Load Testing at Production Volume

This is the stage most teams skip entirely, and it is the most dangerous omission. Your automation might handle five orders per minute perfectly but collapse at fifty. API rate limits, webhook queue depth, database write locks, and platform execution time caps all create invisible ceilings.

To load test properly, calculate your peak order volume. Take your highest single-hour order count from the past year and multiply by 1.5 for safety margin. Then simulate that volume hitting your automation simultaneously. Watch for timeout errors and HTTP 429 (rate limited) responses.

On platforms like Make.com, each scenario has an execution time limit. If your workflow takes 38 seconds per order and you receive 200 orders in a burst, you need to verify that the queuing mechanism handles the backlog without dropping records.

The Go-Live Checklist

Figure 2 — Complete go-live readiness checklist for automation launches

The Soft Launch Strategy

Even after passing all three testing stages, avoid flipping the switch for 100% of your traffic at once. Use a soft launch: route 10% of orders through the new automation while keeping the manual process as a parallel fallback. Monitor for 48 hours. If error rates stay below 0.5%, increase to 50%. After another 48 hours of clean operation, move to 100%.

This graduated rollout gives you a safety net. If something slips through testing, you catch it with 10% of your volume instead of all of it. The cost of processing a handful of orders manually for a few extra days is nothing compared to the cost of a full production failure.

What to Monitor After Launch

Going live is not the finish line. The first two weeks are critical. Set up alerts for execution failures, unusual latency spikes, and record count mismatches between source and destination systems. Compare the automated output against manual spot-checks daily for the first week. Watch for warning signs that integrations are silently breaking.

"The automation that works perfectly on day one and fails silently on day fifteen is more dangerous than the one that fails loudly on day one."

Build a reconciliation report that runs nightly: count the orders received, count the orders processed, and flag any discrepancy. This simple check catches drift before it becomes a crisis. Every inventory sync automation we build includes this reconciliation layer as a non-negotiable component.

Testing is not overhead. It is insurance. The teams that invest a week in structured pre-launch testing save months of cleanup, customer apologies, and financial reconciliation on the other side. Make it the rule, not the exception.