The process is automated before it is understood
If two employees perform the same work differently, the automation team does not have a stable specification. Technology will force the organisation to choose one path, but that path may not reflect the real business need.
Before implementation, identify the trigger, steps, decision points, inputs, output and ownership. This does not require hundreds of pages of documentation. It requires a shared understanding of how the process actually works.
Exceptions are postponed until later
A demonstration can almost always show the happy path. Production introduces missing fields, duplicates, unrecognised documents, unavailable systems and data that does not match the expected format.
If exception behaviour is undefined, the automation either stops or, worse, continues with incorrect information. Every critical step needs an explicit failure scenario.
Poor data quality is treated as a technical detail
Automation increases processing speed, but it can spread incorrect data just as quickly. If customer codes, product identifiers or mandatory fields are maintained inconsistently, integration will not make that problem disappear.
A robust solution often needs a validation layer that checks required fields, formats, allowed values and duplicates before information is passed downstream.
Technology is selected before process requirements
Projects that begin with “we have Power Automate” or “we need AI” risk shaping the business process around a tool. The order should be reversed: define requirements first, then choose the simplest technology that can satisfy them reliably.
One part of the workflow may fit a standard platform while another part needs an API integration or a small custom service.
Ownership disappears after launch
Production automation is not a script that can be forgotten. APIs change, credentials expire, business rules evolve and source systems introduce new fields.
It should be clear where failures are visible, who receives them, what response is expected and how changes are released. Without that operational layer, a good first version can become an unmanaged risk.
Reliable automation is an operating process, not only a technical solution
Most risks can be controlled when they are discussed before production: the process is understood, data is validated, exceptions are designed and post-launch ownership is clear.
A strong automation does not pretend complexity has disappeared. It defines what the system handles automatically and where people remain responsible.