
Manish Choudhary
CEO & Co-founder, Ferry | Flexprice

Where the order to cash process breaks
Most O2C friction clusters at one seam: the gap between the contract and the invoice. Everything upstream of that is usually fine. The pain shows up when a signed commercial agreement has to become an accurate bill, month after month, as things change.
The data backs this up. In one survey of 200-plus finance leaders, 68% said their O2C process carries heavy manual workloads, 43% blamed poor integration between systems, and more than 40% regularly hit billing errors from data-entry mistakes, contract changes, and pricing discrepancies. Those are not exotic edge cases. That is the median finance team.
Disputes make it worse, and they almost always trace back to an invoice the customer disagrees with. Roughly 30% of late B2B payments come down to disputes, and a dispute that takes three to five days to resolve when you have a real workflow can drag out to 14 to 21 days when you are handling it over email. Every one of those days is DSO.
Now the structural problem, the one I think most explainers miss. The seven-step model assumes an order is a fixed thing, agreed once, fulfilled once, billed once. Modern B2B revenue is nothing like that. Contracts have tiers, ramps that step up over the year, true-ups, prorations, and amendments signed in the middle of a term. Usage-based pricing means the "order" is really a meter that produces a different number every month. Rules-based billing systems and spreadsheets were built to repeat the same calculation, so they handle the standard case beautifully and come apart the moment a contract is non-standard. And these days, most of the interesting contracts are non-standard.
This is why companies running modern pricing end up with a finance person manually rebuilding invoices in a spreadsheet every month. The automation they bought works, technically. It just cannot read a contract and reason about what changed.
How automation improves order to cash
Automation shortens the O2C cycle by removing the manual handoffs between the seven steps, so data flows from order to cash without a human re-keying it three times. At the simpler end, that means generating invoices straight from order data, running dunning sequences without someone having to remember, and applying incoming cash automatically. Good AR automation matches payments to invoices at 95 to 99% accuracy and cuts manual cash-application time by 70 to 85%, which is why teams that automate collections run materially lower DSO than teams that do not.
There is a ceiling to the old kind of automation, though, and it is worth being honest about it. Rules-based automation only does what you told it to do in advance. It is fast and dependable on the contract shapes you anticipated, and stuck on the ones you did not. Putting an "AI" label on a rules engine does not change that. If the underlying system cannot understand a contract it has never seen, it will still kick the strange ones back to a human.
The shift happening now is from automation that follows rules to automation that reads the contract and reasons about it the way a person would, then shows its work. That last part matters more than it sounds, because finance cannot run on a black box. You need to know why the system billed what it billed, traced back to the clause and the usage behind it.
How Ferry automates order to cash
Ferry runs the whole order to cash path off the contract itself. Its AI agent reads the signed agreement, builds the billing schedule, rates the usage, issues the invoice, runs collections, applies ASC 606 and GAAP revenue recognition, and posts the result to your ERP, with every figure traceable back to the contract and the usage event behind it. That traceability is the point. Ferry is built as auditable AI for finance, so when the agent bills something, you can see exactly why.
A few things separate this from the rules-based version. Ferry is AI-native from day zero rather than an AI feature added on top of an older engine, so it handles a contract it has not seen before instead of rejecting it. It covers the full contract-to-cash flow in one product, so you are not stitching together a billing tool, a collections tool, and a revenue recognition tool and hoping they agree. And it works on top of whatever billing system you already run, or as your billing system if you want it to be, so you are not ripping anything out to get started.
The results show up where finance actually feels them. Vapi cut invoice cycle time by 93% after handing the work to Ferry's agent. Segwise's founding engineer Kush Daga put it plainly: "Every number drills down to the invoice and the usage behind it, no more reconciliation spreadsheets." And on the close, Simplismart's Deblina Lahiri said, "We have cut our month-end close to under 2 days using Ferry AI. It would've taken us 2 weeks of effort otherwise."
The takeaway
Order to cash is not seven separate tasks. It is one cash engine, and like any engine it runs only as well as its weakest part. For most companies today that weakest part is the contract-to-invoice seam, where modern pricing meets automation built for a simpler kind of contract. If you want to find where your own O2C is leaking, map the seven steps and look for the one where a non-standard contract forces someone to open a spreadsheet. Then ask whether a rule could ever have handled it, or whether that step needed something that can actually read the contract. That question is the whole ballgame.
What are the seven steps of the order to cash process?
What is the difference between order to cash and procure to pay?
What is the difference between O2C and accounts receivable?
How do you improve the order to cash cycle?
How is the order to cash process measured?



















