Automating before organizing costs more

Automating a messy process can make the friction faster, but it does not remove it. If owners, exceptions, checkpoints and decision criteria are not clear, automation ends up locking in problems that were at least visible when the work was manual.

This guide proposes an order of work: understand the process, simplify it, define it and only then automate it, with what it needs to keep working when the operation changes.

The question is not whether you can, but what you are automating

Some processes are slow because they have too many steps. Others because nobody knows exactly who decides, or because every case brings a different exception. Automating without telling those apart usually produces a flow that is hard to maintain and even harder to audit.

Map the process as it is, not as it should be

With the people who run it, write down:

• What starts it: an email, a form, a date, a change in a system.
• Which steps it has and who does each one.
• Which data each step needs and where it comes from.
• Which decisions are made, and on what basis.
• Which exceptions come up and how they are handled today.
• What result is expected and who checks it.

There are almost always surprises: steps only one person does “because it has always been done that way”, data copied by hand across three systems, or approvals nobody remembers the reason for.

Signs it is not ready yet

• Two people describe the same process differently.
• There are more exceptions than rule.
• There is no single identifier that follows each case from start to finish.
• Nobody can say when the result is correct.

Simplify before you connect

A good practice is removing steps that add no value before connecting platforms or triggers. Often the biggest improvement is not a sophisticated automation but fewer unnecessary approvals, a minimum data set and exceptions put in order.

That lowers complexity, makes adoption easier and leaves traceability to sustain the process when the operation changes.

Flow definition sheet

Before building, each automation should have a short sheet:

• Trigger: which event starts the flow.
• Required data: what must exist to run it; if something is missing, the flow stops and alerts.
• Rules: what the flow decides, and on what basis.
• Exceptions: which ones are expected and who resolves them.
• Result: where it ends, how it is checked and who reviews it.
• Owner: the person responsible for keeping it working.

Useful automation is automation you can govern

A mature automation does not just run; it can also be understood, audited and adjusted. If nobody knows how it is built, who reviews it or what happens when it fails, the initial savings disappear quickly: the day it fails silently, work piles up without anyone noticing.

Error handling and alerts

Automation platforms let you prepare a flow to fail well. In Power Automate, for example, an article by Microsoft’s engineering team describes how to group actions into blocks (Try/Catch/Finally), configure what runs when a block fails or times out, log the error and alert a Teams channel the maintainers watch.

Whatever the tool, every flow should:

• Alert a person when it fails, with the exact case and step.
• Log every run, so you can audit what it did and when.
• Have a manual way to finish the case when the flow cannot.

Inventory and review

Keep a list of active automations with their owner, connected systems, credentials used and date of last review. Automations built by each team without an inventory are the modern equivalent of the critical spreadsheets only their author understands.

A typical example

A company wants to automate creating invoices from approved orders. Mapping the process reveals orders with discounts agreed by email, customers with two legal entities and approvals sometimes given over WhatsApp. Automating in that state would produce wrong invoices faster.

The right order: agree where discounts are recorded, clean up customer data, move approval to a single channel and then automate. The resulting flow is simpler and has almost no exceptions. Something similar happens with data: before asking for dashboards you need to agree on definitions and sources.

Common mistakes

• Picking the tool before understanding the process.
• Automating the exceptions instead of reducing them.
• Using someone’s personal account to connect systems.
• Having no alert when the flow fails.
• Not assigning an owner after go-live.

Where to start

Pick a repetitive process with few exceptions and a result that is easy to check. Map it, simplify it, fill in the sheet and automate it with error handling from day one. Put the logic of the process in order first; then it is worth deciding which part to automate and with which tool.

If you want to do it with support, this is how we work on custom software and automation.