Business process automation and AI
integration in Somerville

An automated process rarely touches one system. A new order might write to a CRM, then a billing tool, then a shipping queue. Three systems, one sequence. If the third step fails, the first two have already happened. The question is what happens next. Most teams only find out the hard way.

A simple automation that stops on error is fine when nothing has been written yet. The trouble starts once some steps have committed and others have not. The systems now disagree with each other. Nobody notices until a customer asks why they were billed twice, or not at all.

The standard answer is a compensating action for every step that changes data. If step one creates a record, there needs to be a defined way to undo or flag it once a later step fails. This is extra work up front. It usually gets skipped under deadline pressure, which is exactly when it gets needed.

Take an order created in a CRM that then fails while generating an invoice. Leaving that CRM record untouched hides the failure from everyone downstream. A better pattern marks the record as incomplete, with a reason, so a person or a later retry can pick it up on purpose rather than by accident.

Failed items need a home. Not a silent retry loop that keeps trying the same broken input every few minutes, but a specific place. A dead letter queue, a simple list of items that failed and why, gives a team one spot to review problems instead of hunting through logs after a complaint arrives.

Retries carry their own risk. If a step already succeeded once, and the automation retries the whole sequence from the start, it can create the same record twice. Each step needs to be safe to run again with the same input, usually by checking whether the result already exists before writing it a second time.

Testing an automation against the happy path only tells half the story. Break a connection mid run on purpose. Feed it a record a downstream system will reject. The response shows whether the failure is handled gracefully, or whether it leaves data in a state nobody planned for.

Recording the state of each step, and the final outcome, turns a failure into something that can be reconciled quickly. A log showing which of five steps completed before the error lets a person fix exactly what broke, instead of re-running everything and hoping for the best.

Not every failure deserves the same response. Some are safe to retry automatically, a few times, before giving up. Others, especially anything touching money or an outside party, should stop and wait for a person to look. Deciding which is which belongs in the design phase, before launch, not after the first incident.

Most automation projects are scoped around new activity. From launch day, a new lead triggers a workflow, a new order updates a dashboard. That framing quietly ignores everything already sitting in the source systems, sometimes years of records that never passed through the new logic at all. The gap grows every day the project runs without one.

A backfill runs the same rules against that older data, after the fact. It sounds like a small addition. It rarely is. In practice it often takes longer than building the live automation, because old records were entered under different rules and do not fit the shape the new process expects.

Historical data is messy in ways fresh data is not. Fields required now were optional two years ago. Categories a workflow depends on may have been renamed, merged, or left blank for a long stretch. Running new logic against that history surfaces every one of those gaps at once, all together, with no warning.

Volume changes the approach too. A live automation handles one record as it arrives. A backfill might process many thousands in a single pass. Speed matters here. That raises different concerns: how long the run takes, whether it can pause and resume, and whether the target system can absorb a sudden burst of activity.

Order matters more than it looks. Processing records in the wrong sequence can leave calculated totals or statuses briefly wrong, even when each record is handled correctly on its own. A backfill usually needs its own defined order, separate from whatever order the live automation uses for new activity. Get this wrong and the numbers lie for a while.

Side effects are the part teams most often forget. A live workflow that sends a notification email makes sense for a new order. The same workflow applied to five years of past orders would send five years of emails in one afternoon. Nobody wants that inbox. Those steps typically need switching off, or redirecting somewhere safe.

Verification happens at the end, and also at the start. Spot check a sample of updated records against the source, before and after the run. That catches mismatches early. Row counts alone are not enough, since a record can be touched and still end up with the wrong value. Check the value itself, and the count.

A backfill is also a natural point to decide how far back to go. Reaching all the way to the first record in a system sounds thorough. It is not always worth it. Very old data may no longer matter to current decisions, and the cost of cleaning it can outweigh what a business gets from automating it.

Treat a backfill as a one time project, separate from the ongoing automation. Keep the two from tangling. Once it finishes and the numbers check out, retire it. Do not leave it running. Otherwise a future team has to work out, years later, why an old script still touches production every single night.

An automation is easy to build and then quietly hard to maintain. The person who wired it together understands every branch by heart. Six months later that person has changed roles. Nobody else can say with confidence what the workflow does. Nobody can say why a certain step exists at all.

Ownership is often left implicit, which really means it belongs to whoever happens to notice something broke. That is a poor way to run anything touching customer data or billing. A workflow needs a named owner from the day it goes live, much like a piece of equipment has someone responsible for its upkeep.

The owner need not be the original builder. It can be a role rather than a person: whoever leads operations, or whoever manages the tools the workflow connects. A role survives a resignation. What matters is that it is written down somewhere a new hire can find it, rather than living only in the memory of one former colleague.

Documentation earns its keep at exactly this handover moment. A short description of what a workflow does, which systems it touches, and what a failure looks like saves hours compared with reverse engineering the logic from scratch. It does not need to be long. It needs to still be accurate a year later.

Access is a separate question from documentation, and both need attention. If a workflow runs under a former staff login or a personal API key, it can stop working the day that account gets disabled, often with no warning at all. Shared service accounts, tied to a team rather than a person, avoid that failure entirely.

Reviewing a workflow on a schedule, even a light one, catches drift before it becomes a real problem. Source systems change fields. Vendors update their platforms. Business rules shift. A workflow nobody has looked at in two years is usually still running. It is rarely still doing exactly what it was built to do. A short quarterly check, a few minutes per workflow, is often enough to catch the difference before a customer does.

When a workflow is retired rather than handed over, that decision needs to be visible too. Turning something off quietly, without telling the people whose process depended on it, tends to surface weeks later as a gap nobody can explain. Say what stopped, and why. A short note closes that loop.

None of this needs heavy process. A shared list of active workflows, each with an owner, a one paragraph description, and a note on where its credentials live, covers most of what a team needs. Keep it current. That habit matters more than the format the list takes.