Five Questions to Ask Before You Put AI in Your Back Office

Everyone selling software right now says the word AI. For the owner of a 40-person machine shop or the finance lead at a 200-person plastics processor, the harder question is not whether AI matters. It is where to start without wasting a year and a budget.
After watching manufacturers work through that question, we have found that the companies that get real value ask five questions before they buy or build anything.

Which workflow hurts the most?

Skip the AI strategy deck. Pick one workflow that eats hours every week and has a clear start and finish. In most back offices, that is quoting, invoice reconciliation or reporting. If your estimator spends Tuesday morning assembling the same quote package they assembled last Tuesday, that is a candidate. If nobody can answer “what did we ship last month at what margin” without three spreadsheets, that is a candidate too. Basically, think of a few repetitive workflows that take up valuable time from people in the company. Or the task that you dread doing because it’s so slow.

Where does the information actually live?

Start by mapping it honestly. The RFQ arrives by email and the drawing attached is a PDF. Pricing history lives in the ERP — except for the jobs that live in Excel, and except for the ones that live in the finance team’s heads. This map matters more than any tool choice because AI that cannot reach your real systems will only ever produce demos.

Where must a human stay in the loop?

The goal is not lights-out automation; it’s moving your people from assembling information to approving decisions. Decide up front which steps need a human sign-off. These might be things like releasing a quote, paying an invoice or committing a delivery date. Good AI systems make those checkpoints explicit and bad ones hide them.

Is our data really not ready, or does that just feel true?

“Our data is a mess” is the most common reason manufacturers wait, and it is usually wrong in a useful way. If you have structured systems like an ERP or CRM, modern AI can already search them, analyze them, and write to them. And for messy processes, the right workflow builds structure as it runs, meaning that every quote it processes becomes clean data for the next one. Waiting for perfect data means waiting forever because data gets structured by being used.

What are we measuring after ninety days?

Pick the number before you start. Quote turnaround time. Invoices are reconciled per person. Days from ship to invoice. One manufacturer we work with measured RFQ response time and cut it by 70 percent, which mattered because the first quote in the customer’s inbox wins more often than the best one.

The pattern behind all five

Notice that none of these questions are about AI models. They are about workflows, systems, people and numbers, which is exactly why back-office AI succeeds in plants where generic chatbots fail. The chat window is maybe ten percent of the work. The other ninety percent is the unglamorous part: connecting to your ERP and inbox, structuring messy documents, remembering your pricing rules and knowing when to hand a decision to a person. Start with one workflow where the payoff is obvious. Prove it. Then let it compound.

About the Author

Adhitya Raghavan is co-founder and CEO of Galvant, which builds AI agents that automate quoting, AP/AR and procurement for manufacturers. He grew up in his family’s steel manufacturing business and is a Princeton engineer and Harvard MBA. He may be reached at araghavan@taktconnect.com.

Galvant is an MMA Premium Associate Member and has been an MMA member company since July 2026. Visit online: taktconnect.com.