This week’s blog is from one of our exhibitors, Paraglide Ai:
AI agents in order-to-cash (O2C) are software agents that read, decide, and act across the billing, collections, credit, and cash application workflows that move an invoice from issue to paid. Unlike rule-based automation, they handle two-way conversations: answering inbound billing queries, replying to collections responses, and resolving the issues that block payment, not just sending reminders on a schedule.
AI-native solutions like Paraglide provide agents that do the work in O2C; handle end-to-end collection conversations, negotiate, solve disputes and apply cash.
Key takeaways
- AI agents in O2C handle use cases such as collections, billing queries, disputes, deductions, supplier portal uploads, cash application and credit.
- Agentic solutions like Paraglide manage the AR inbox, both outbound outreach and inbound queries.
- Paraglide is an AI-native O2C platform built around AI agents, with an average 34% DSO reduction and a 75% reduction in manual AR work.
- The core difference between generations is automating the entire conversation: legacy AR software sends templated payment reminders, Paraglide sends personalised reminders, handle replies, and follows up in existing threads with context
What AI Agents Do in the Order-to-Cash Cycle
AI agents cover the parts of O2C that are high-volume, repetitive, and conversation-dependent. Each agent owns a distinct stage of the cycle and works from live account data rather than static templates.
| O2C stage | What the AI agent does |
| Billing support | Reads inbound finance-inbox queries, retrieves the relevant data, and replies or routes to a specialist with full context |
| Collections | Runs end-to-end, two-way collections conversations, including replies and follow-ups |
| Credit | Makes credit decisions using credit scores, live payment behaviour, and desktop research |
| Reconciliation | Applies incoming cash and handles self-billing matching |
| Supplier portal | Logs into customer AP portals to submit invoices and track status |
| Disputes | Resolve and escalate disputes |
The common thread is inbound. A reminder can be scheduled, but a customer asking for a revised invoice, disputing a charge, or querying an allocation needs a response before the payment moves. AI agents produce that response.
Why Inbound Resolution Is the Real Bottleneck
The bottleneck in O2C is rarely a shortage of reminders. It is the backlog of inbound queries and replies that each block a payment until resolved. A customer will not pay an amount they are disputing, cannot process an invoice with a missing PO number, and often will not settle without a statement first.
Every reminder sent is also an invitation to reply, so outbound automation tends to increase inbound volume rather than reduce it. AI agents are suited to this problem because they can read a full email thread, understand what is being asked, pull live account data, and answer, rather than firing a generic template that misses the context.
How AI Agents Differ From Rule-Based O2C Tools
O2C tooling falls into three generations, and the difference is what happens after the reminder goes out.
| Generation | Approach | Inbound replies and queries |
| Gen 1 (legacy / RPA) | Deterministic, pre-LLM automation | Left to the AR team |
| Gen 2 (SaaS) | Rule-based reminder workflows | Left to the AR team, or templated at best |
| Gen 3 (AI-native, e.g. Paraglide) | AI agents that read context and act | Handled directly by the agent, with escalation for complex cases |
Rule-based tools match patterns. AI agents read conversations. For a finance function where most queries are contextual, multi-turn, and reference-dependent, that distinction decides whether a tool reduces inbound volume or resolves it.
How AI-Native O2C Tools Differs From Pre-AI Solutions
An AI-native O2C platform is built around agents from the ground up, rather than adding AI features on top of a rule-based system. Paraglide was designed this way from the start: agents are the product, not a layer bolted onto an older workflow engine.
The practical difference shows up in what the tool hands back to the finance team. Pre-AI solutions produce a to-do list. They surface a worklist of overdue accounts, flagged queries, and follow-ups, then leave a person to work through each item. The software organises the work; the AR team still does it.
AI-native agents do the work. Instead of adding an item to a queue, the agent reads the query, retrieves the data, and sends the response or takes the action. A dispute is captured and routed with full context. A collection reply is answered. An invoice is submitted to the AP portal. The queue shrinks because the work is completed, not because it has been sorted more neatly.
Paraglide is a leading AI-native solution that is built with agents in mind from the ground up, automating the 2-way conversation in the order-to-cash process.
| Capability | Pre-AI solution | AI-native (Paraglide) |
| Built around | A rule engine, with AI added later | Agents, from the ground up |
| Output to the team | A to-do list of tasks to work through | Completed actions and resolved queries |
| Inbound queries | Flagged for a human to handle | Read, answered, or routed with context |
| Role of the AR team | Executes every item on the list | Reviews complex cases; agents handle the rest |
The result is a shift from a platform that tells the finance team what needs doing to one where the agents carry it out.
How Paraglide Applies AI Agents to Order-to-Cash
Paraglide is an AI-native O2C platform built around agents that cover the full cycle, with the finance inbox as the starting point rather than an afterthought. The Billing Support Agent resolves standard queries end-to-end and routes complex ones with a full brief. The Collections Agent runs two-way collections conversations. The Credit, Reconciliation, and Supplier Portal Agents extend the same approach across credit decisioning, cash application, and AP portal submissions.
The measured results reflect where the time goes: an average 34% reduction in DSO, a 75% reduction in manual AR work, a 24% reduction in credit losses, and implementation in under ten days. The DSO figure is not driven by better reminders. It comes from resolving the inbound issues that were blocking payment.
Frequently asked questions
What are AI agents in order-to-cash?
AI agents in order-to-cash are software agents that read, decide, and act across billing, collections, credit, and cash application. They handle both outbound outreach and inbound replies, resolving billing queries and follow-ups that block payment rather than only sending reminders. AI-native solutions like Paraglide automate end-to-end O2C workflows with agents doing the work.
How do AI agents reduce DSO in order-to-cash?
AI agents reduce DSO by resolving the inbound queries that block payment, such as missing PO numbers, disputes, and statement requests, within minutes rather than days. Paraglide customers reduce DSO by an average of 34% through faster resolution rather than more frequent reminders.
What is the difference between AI agents and rule-based O2C automation?
Rule-based automation follows predefined workflows and templates, so it handles outbound reminders but leaves inbound replies to the finance team. AI agents read full conversation threads, retrieve live account data, and respond to what the customer actually asked, including multi-turn and complex queries.
Which parts of order-to-cash can AI agents handle?
AI agents can handle billing-query support, end-to-end collections conversations, credit decisioning, cash application and reconciliation, and AP portal submissions. Paraglide runs a dedicated agent for each of these stages across the full order-to-cash cycle.
Summary
AI agents in order-to-cash matter because they take on the conversation, not just the reminder. Legacy and SaaS tools automate outbound and leave every reply, dispute, and query to the AR team, which is where payment actually stalls. AI-native platforms such as Paraglide handle both directions across billing, collections, credit, reconciliation, and AP portals, which is why the DSO impact comes from resolution speed rather than reminder frequency.
Learn more at paraglide.ai. Meet the team and more finance experts at financeSHOWCASE. Next stop London, 22nd September, Tottenham Hotspur Stadium. Register for your FREE tickets here
