How to Estimate Test Automation Effort for ERP Releases

Every ERP program eventually asks the same question before a release: how much effort will it actually take to test this properly? Teams answer it differently depending on who is asking. A Scrum team estimates story points. An IT director planning a Dynamics 365 update, an S/4HANA migration, or an Oracle Fusion quarterly patch needs a different kind of answer. It has to account for how many business processes are affected, how many systems each one touches, and how much of that work will need to be redone at the next release.

This guide covers what actually drives test automation effort in ERP environments, why most of the real cost sits in maintenance rather than initial test creation, and how to build a realistic estimate instead of guessing.

Why Test Automation Effort Is Hard to Pin Down

A single estimate for test automation effort rarely holds up, because the number depends on variables that change from one business process to the next.

• Process complexity: a process like Order to Cash that spans CRM, ERP, logistics, and finance carries more integration points, and more places for a change to break something, than a process contained in one application.
• How often the underlying application changes: Dynamics 365 moves on Microsoft's always-on roadmap of monthly patches and continuous updates, the twice-a-year "release wave" model was retired in September 2026, though the pace of change itself hasn't slowed. SAP moves through transport cycles and enhancement package deadlines, and Oracle Fusion patches on a quarterly schedule. Each of these can touch the same process again, so the estimate has to account for repeated validation, not a single build.
• Business criticality: a process tied to revenue, compliance, or financial close carries more risk if it breaks, which usually means it deserves a more thorough, and more carefully maintained, automation footprint than a rarely used screen with a cosmetic change.

Treating all of this as one flat number, hours per test case, is where most estimates go wrong. The estimate needs to reflect which processes matter most, not an average across everything.

What Actually Drives the Effort: Creation vs. Maintenance

Most of the attention in effort estimation goes to the initial build, how long it takes to write and validate the first version of a test. In practice, that is usually the smaller piece.

Every release wave, transport, or patch has the potential to break existing automation. Manually finding and fixing broken scripts after each change, not the original test creation, is typically where teams lose the most time over the course of a year. An estimate that only accounts for build effort and ignores this ongoing cost will consistently run short.

Self-healing automation changes this equation directly. Automation that adapts to shifts in element IDs, layouts, and page structure without a manual rewrite keeps existing tests working through routine changes, which is the single biggest lever for reducing effort over the life of an automation program.

A Practical Way to Estimate Effort

A more reliable approach starts from the business process, not the test script.

1. Start with what is business-critical. Identify the processes that are high-frequency, tied to revenue or compliance, and span multiple systems. These deserve the estimate's attention first.
2. Reuse what already exists. Manual test cases, process documentation, and prior automation assets give a starting point for effort, rather than starting from a blank page for every process.
3. Separate what is worth automating from what is not. A process that runs every release and touches revenue is worth the investment. A rarely used screen with a cosmetic change usually is not. Estimating only the automation that will actually pay off keeps the number honest.
4. Account for maintenance separately from creation. Build in an ongoing effort line for every release cycle the process will go through, not just the first one.

This produces an estimate tied to business risk and release cadence, rather than a flat multiplier applied to every test case equally.

How No-Code Automation Changes the Effort Equation

Effort estimates often assume that automation work depends on developer availability, since traditional scripted approaches require someone who can write and maintain code. That assumption changes with a no-code approach.

With Avo Assure, functional consultants, business analysts, and ERP project managers can build and maintain tests directly, using the Avo Genius Smart Recorder to capture actions without writing scripts, across web, desktop, and connected enterprise systems. That removes one of the more common bottlenecks in a testing schedule, waiting for a developer to become available for every change. It does not eliminate the underlying effort, but it changes who can absorb it and how quickly.

What Enterprises Typically See

Across enterprise deployments, Avo Assure customers report 90%-plus test automation coverage, 85% less ongoing maintenance effort through self-healing automation, and an 80% reduction in the effort required to create and execute tests.

For SAP specifically, Synergy Marine Group worked with Accenture to validate standardized SAP business processes across multiple company codes, reducing manual regression effort by 90% and reaching approximately 80% automation coverage of planned business scenarios. Read the full Synergy Marine Group case study to see how it played out at enterprise scale.

These are outcomes from real deployments, not a substitute for your own estimate, but they are a useful reference point when sizing what is realistic for your organization.

The most reliable test automation effort estimate starts with your business processes, not a generic formula, because it is really a proxy for a bigger question: can you protect the processes that matter most, release after release. See how Avo Assure helps you estimate, build, and sustain ERP test automation with less guesswork. Book a Demo to get started.

Frequently Asked Questions