Order-to-Cash turns revenue into cash, and most of its cost sits in people handling documents and exceptions: PDF orders, orders held behind a credit decision, remittances that arrive apart from the payment, short-pays that need a reason code.
Order-to-Cash turns revenue into cash, and most of its cost sits in people handling documents and exceptions: PDF orders, orders stuck behind a credit decision, remittances that arrive apart from the payment, short-pays that need a reason code, and collectors working an aging report by hand. The Hackett Group’s July 2026 benchmarks put the gap in numbers: organisations that redesign the process around AI run order-to-cash at 52% to 59% lower cost and 56% to 64% lower staffing per $1 billion of revenue, with 134% more digital order intake, 138% more automated credit decisions, 89% more payments applied automatically and 85% fewer delinquent days (The Hackett Group, 15 July 2026) 1.
The median company auto-matches 70% of payments and 61% match on the day of receipt, leaving up to $15 million of unapplied cash at a typical large enterprise (The Hackett Group, 11 August 2025) 2. About half of collection activity is touchless today; the average company runs 8.5 days more delinquent and writes off 1.1 points more bad debt than leaders, worth up to $44 million a year (The Hackett Group, 30 March 2026) 3.
Finance AI use is steady at 59% of functions, with payables automation the most common transactional use case at 37% (Gartner, 18 November 2025) 4, and 46% of finance leaders put working capital optimisation among their priority areas for AI agents (Deloitte, 4 February 2026) 7. Vendors have answered with named agents from SAP 10, Oracle 14, HighRadius 21, BlackLine 27, Esker 30 and Microsoft 34. For a high-tech manufacturer the same chain carries the distributor’s claims, ship-and-debit, price protection and stock rotation, which is why the deductions use case below reads the channel programme too.
The research note ranks 14 candidate use cases on four criteria in this order of weight: adoption evidence, vendor offers, named results and measured pain. Cash application and collections score the maximum on all four: two analyst benchmarks each 2 3, named agents from the ERP vendors and the receivables specialists, and pain measured in unapplied cash and delinquent days 1. Disputes and deductions follow on the same Hackett coverage 3, 2026 agents from SAP 10 and Oracle 15 and the largest named recoveries in the chain 25. Order intake ranks fourth on thinner adoption evidence 1, with agents from every large ERP 10 18, and absorbs the credit-hold candidate as its highest-value exception. The other nine candidates, from invoicing accuracy to revenue recognition, scored lower on today’s evidence and sit below as additional capability.
Match every incoming customer payment to its open invoices, post it in the ERP the day it arrives, and code each short-pay for the deductions desk.
Read the use case →Rank overdue accounts by cash impact and payment likelihood, send the right reminder or make the right call at the right time, and follow every promise to pay to its outcome.
Read the use case →Turn every short-pay, debit memo and distributor claim into a case with a reason, its evidence and a validity call, then credit, deny or write it off within policy.
Read the use case →Read customer orders from every channel, create them in the ERP with confirmed dates, and clear the credit, delivery and data blocks that hold them back from shipping.
Read the use case →Each page works the use case through the six stages, names who does what at which level, and lists the settings you would make.
Lima’s agent blueprint is a design of 960 agent roles across five end-to-end chains. It is the map of what Juno can be configured to do, and the first agents are built from it one at a time, the way the PO cancellation agent was.
Everything below is designed and yet to be built. Each role is configured from the same parts as the use cases above, and a role that matters to you can be the next agent built.
| Measure | What it shows |
|---|---|
| Auto-match rate and same-day application | How much cash posts on the day it arrives. The median company matches 70% automatically and 61% match the same day (The Hackett Group, 11 August 2025). |
| Unapplied cash | Payments received and still unmatched to an invoice: up to $15 million at a typical large enterprise (The Hackett Group, 11 August 2025). |
| Days sales outstanding and average days delinquent | How long revenue waits to become cash. The average company runs 8.5 days more delinquent than leaders (The Hackett Group, 30 March 2026). |
| Touchless collection share | The share of reminders, calls and promises handled by rules: about half today (The Hackett Group, 30 March 2026). |
| Bad debt written off | What is given up. A 1.1-point gap to leaders is worth up to $44 million a year (The Hackett Group, 30 March 2026). |
| Dispute resolution days and recovery rate | How long a deduction stays open and how much of an invalid claim comes back; 90 days is one vendor’s industry average (HighRadius, 10 September 2026). |
| Digital order intake and blocked-order dwell time | The share of orders that arrive as data, and the hours an order waits on a block: 134% more digital intake at leaders (The Hackett Group, 15 July 2026). |
Order-to-Cash brings the money in. Where does the buying side lose it, and which purchase-to-pay use cases go first?
Invoice matching, sourcing, the purchase order agent that runs in production, and supplier risk: the four on the buying side.
Bring two systems and one decision your planners make every week. In the first week we register the data, confirm the mapping with your team, answer your questions in ChatLima and run the first agent at the level you choose.
