Your systems don't talk
to each other.
So a person does it. Someone reads the email, retypes the order into the ERP, checks the spreadsheet, and updates the CRM. That handoff is where AI automation actually pays, and it is nowhere near as exciting as a chatbot. It's just worth a great deal more.
Automation lives in the gaps.
Ask how to use AI to improve operations and someone will show you a chatbot. The duller answer is worth far more. AI automation pays best where your process leaves the software. An order arrives as a PDF attached to an email. A supplier confirms in a different format. A price exception needs a human to approve it, and the approval lives in a Slack thread nobody can find later. Every one of those gaps is currently filled by a person doing clerical work at professional-salary rates.
Traditional automation could never cover those gaps because the input was too messy to parse and the judgment too small to codify. Language models changed that. An agent can read the unstructured document, decide what it is, put it in the right system, and escalate the twelve percent of cases it should not decide alone. That last part is what separates back office automation that survives a year from a demo that gets quietly switched off.
I scope this work as an operator who has run both ends of it: the ERP and the warehouse floor, the CRM and the sales team who ignore it. That matters because the hard question in business process automation is never technical. It's which exceptions you are willing to let a machine handle. I work with companies across the United States and Canada.
Also known as: back office automation AI, business process automation with AI, intelligent process automation, AI workflow automation, agentic automation, operations automation consulting, automate back office work.
You already know where
the time goes.
Nobody needs a consultant to find these. They need someone who can fix them without breaking the systems of record.
Swivel-chair work
A person reads one screen and types into another all day. When they're on vacation, the queue backs up and everyone notices.
The spreadsheet that runs the company
It's not in any system, one person maintains it, and every integration proposal has quietly worked around it for six years.
Email as an API
Orders, confirmations, exceptions and disputes all arrive in an inbox and get triaged by hand, with no record of the decision.
Headcount as the scaling plan
Volume goes up thirty percent, so back-office headcount goes up thirty percent. That math is the whole reason to look at automation.
The work that actually pays.
Ranked the way I rank it in an engagement: volume, how expensive an error is, and how much judgment the step really needs.
Document intake
Purchase orders, invoices, bills of lading, insurance forms. Read them, validate them against the system of record, flag what doesn't match.
Order and exception flow
Route the clean ones automatically. Escalate the ones with a substitution, a short ship or a pricing conflict, with the context attached.
System-to-system handoffs
The middle layer between an ERP, a CRM, a WMS and four vendor portals that were never designed to speak to one another.
Quoting and pricing support
Assemble the quote from catalog, cost and history, and put a draft in front of a human instead of a blank form.
Support and inbox triage
Classify, draft, and route. The measure is deflection with a satisfaction score attached, not deflection alone.
Reporting nobody wants to build
The recurring pull-and-reconcile that eats a finance analyst's Monday. Boring, and one of the fastest payback items on the list.
How an automation
gets to production.
Eight to twelve weeks for a first workflow, including the part where it runs alongside a human before anyone trusts it.
Map the real process
- Sit with the people doing the work, because the documented process is never the actual one
- Count volume, handling time and error rate per step
- Find the exceptions and how often each one fires
- Pick the first workflow on payback, not on enthusiasm
Build the narrow version
- Automate the clean path only, and route everything else to a person
- Wire it into the systems of record properly, with an audit trail
- Build the eval set from real historical cases before launch
- Publish the run cost per transaction from day one
Run it beside a human
- Shadow mode: the agent proposes, a person decides, both are logged
- Compare against the eval set weekly and fix what drifts
- Widen the automated path only where the record supports it
- Agree the accuracy threshold that lets it act on its own
Hand it over
- Name the owner and write the runbook they'll actually use
- Set the alerting for failure modes, not just uptime
- Report the change in cost and cycle time against the baseline
- Queue the next workflow with everything learned from this one
The automation agency version
vs. the operator version.
"Is an AI automation agency legit?" is a fair question and the honest answer is: some are. Here is how the two approaches differ once the invoice is paid.
Fast to demo, brittle in production
- , Scoped from a workshop, not from watching the work
- , Wired together in a low-code tool nobody in-house can debug
- , No evaluation set, so quality is judged by anecdote
- , Run cost discovered on the third month's invoice
Slower to demo, still running in year two
- →Scoped by counting volume, handling time and error rate
- →Built in your stack, owned by your team, documented
- →Evaluated against real historical cases before it goes live
- →Cost per transaction published before the build starts
Design your agents like you'd onboard a capable, literal-minded new hire. Narrow scope, clear escalation, and someone reviewing the work until it earns the rope.
Three ways in.
Most companies start with the assessment, because the expensive mistake in automation is picking the wrong first workflow.
Operations automation assessment
Two to four weeks. Map the process, count the work, rank the candidates by payback, and leave you with a sequenced plan and a business case for the first one.
First workflow to production
Eight to twelve weeks, fixed scope. One workflow taken from map to shadow mode to live, with the eval harness and the runbook.
Fractional AI leadership
I stay as the accountable executive while the portfolio grows: more workflows, the team to run them, and the governance around what agents are allowed to do.
Before you automate anything.
How do I use AI in operations, and where should I start?
The short version of how to use AI in operations: start where the work leaves the software. Find the steps where a person moves information between systems, interprets an unstructured document, or makes a small repetitive judgment call. Count the volume and the handling time on those steps, pick the one with the best payback, and automate only the clean path first. Route every exception to a human until the record shows the system handles that exception well. The mistake is starting with the most visible process instead of the most repetitive one.
What is back-office automation with AI?
Back-office automation uses AI to handle the administrative work behind a transaction: reading purchase orders and invoices, validating them against your ERP, routing exceptions, updating records across systems, and assembling the reports that someone currently builds by hand. What changed recently is that language models can read messy, unstructured input, which is what blocked traditional automation from covering most of this work.
My systems don't talk to each other. Is AI the fix?
Sometimes, and it is worth being honest about when it isn't. If both systems have decent APIs and the data model is clean, a normal integration is cheaper, faster and more reliable than anything with a model in it. AI earns its place when the input is unstructured, when the mapping requires interpretation, or when the vendor system has no API and a human is currently reading a portal. I'll tell you which case you're in during the assessment, and I've told plenty of companies to build the boring integration instead.
Is an AI automation agency legit?
Many are, and the category also attracts people who assembled a low-code workflow last quarter. The test is not their demo. Ask what the system costs to run per transaction at your volume, ask to see how they evaluate output quality against real historical cases, and ask who owns and debugs it after launch. A serious answer includes an evaluation set and a named owner on your side. If the answer is that quality is monitored by watching for complaints, you're buying a demo.
What does back-office automation cost?
The assessment runs two to four weeks at a fixed fee. A first production workflow is typically eight to twelve weeks of work, and the run cost is usually the smaller number: most document and routing workflows land between a few cents and a couple of dollars per transaction depending on volume and model choice. I publish the per-transaction cost before the build starts, because a workflow that costs more to run than the person it replaced is a failure regardless of how well it performs.
Will this replace my operations team?
In practice it changes what they do rather than how many of them there are. The clerical portion shrinks and the exception-handling and supervision portion grows, which is better work and usually better retention. Companies that grow into their automation rather than cutting into it get the best result, because volume goes up without the back office going up with it.
Related paths.
AI Consulting
The wider engagement: where AI pays across the business, not only in operations.
AI for logistics & supply chain
The vertical version of this work: freight, 3PL, order flow and carrier exceptions.
Design AI agents like employees
Why narrow scope and clear escalation beat clever prompting when agents touch real operations.
Which process eats
the most hours?
Tell me the one your team complains about. I'll tell you whether AI is the right tool for it, and roughly what it would take.