Order-to-Cash (O2C) includes each step from when a customer order is placed to when that revenue is in the bank and reconciled in accounts. Traditionally, automation has focused on structured tasks. For example, auto-creating invoices, scheduling reminders for overdue balances or extracting data from forms. That has undeniably improved efficiency.
Yet most teams will tell you the real slowdowns don’t come from generating invoices or running simple reminders. They come from exceptions and conversations, customers asking for copies, missing PO numbers, disputed amounts, unclear remittances or inconsistent payment terms. These interactions live in shared inboxes or across systems, and they are hard to automate using rule-based tools.
How AI agents Operate Within the Order-to-Cash Cycle
AI agents operate across the full Order-to-Cash cycle, from billing through collections to cash application. They help remove friction at every stage and keep revenue moving towards cash.
Billing and Invoice Management
The first delays in O2C often begin before an invoice is even paid. Missing PO numbers, incorrect billing terms or failed invoice delivery can stall the process immediately.
AI agents reduce this risk by validating invoice data against contracts and order records before dispatch. They detect missing information, flag inconsistencies and monitor invoice delivery status. When customers request copies or supporting documentation, the agent responds instantly and logs the interaction in the ERP. By stabilising billing at the start, fewer invoices turn into disputes later.
Collections and Dispute Handling
Once invoices are issued, the workflow becomes communication-heavy. Customers ask questions, confirm payments or raise disputes. Traditionally, this sits in a shared inbox waiting for manual triage.
AI agents read incoming messages, classify intent and determine the correct next step. A dispute is logged and routed. A payment confirmation updates the account. A documentation request triggers an immediate response.
They also prioritise outreach based on behaviour rather than static ageing buckets, draft contextual follow-ups and track payment commitments automatically. This keeps conversations moving instead of restarting from scratch each time.
Cash Application and Reconciliation
Even when payment arrives, manual work often remains. Remittance advice can be unclear, references incomplete and partial payments difficult to reconcile.
AI agents interpret remittance information, match payments to open invoices and flag discrepancies where human review is required. Account records update automatically, reducing manual reconciliation workload and improving reporting accuracy.
Real-Time Working Capital Control
The broader impact is not just task efficiency. AI agents influence working capital directly.
By resolving billing issues early, responding to queries quickly and tracking payment commitments consistently, they reduce the time invoices remain open. Instead of reviewing KPIs after performance slips, finance teams see issues as they emerge.
This shifts O2C from reactive management to continuous control.
Software Platforms with AI Agents for Order-to-Cash Teams
The Order-to-Cash software market has evolved quickly. Many platforms now claim AI capabilities, but the depth and focus of those capabilities vary significantly. Some tools apply machine learning to forecasting or risk scoring. Others use AI for document capture. A smaller group deploys true AI agents that manage communication-driven workflows across billing and collections.
When evaluating options, it helps to understand how each platform approaches AI within O2C.
Platform | Primary Focus | AI Agent Depth | Best For |
Paraglide | Billing and collections automation from the finance inbox | High | B2B teams with high-volume invoices and shared inbox bottlenecks |
HighRadius | Enterprise end-to-end O2C suite | Medium | Large global enterprises with complex credit structures |
Esker | Document and invoice automation | Medium | Organisations focused on invoice processing and document workflows |
Kolleno | Collections and payment acceleration | Low | Mid-market AR teams improving collections efficiency |
IBM Watsonx Orchestrate | Custom AI workflow orchestration | Variable | Enterprises building cross-functional AI agents |
UiPath | RPA + structured task automation | Low | Organisations already invested in RPA infrastructure |
Paraglide
Best suited for: Finance teams dealing with high volumes of billing queries, shared inbox backlog and collections conversations.
Paraglide uses AI agents to automate the operational work that typically consumes most of an O2C team’s time, especially in the finance inbox. Paraglide’s AI agents handle key parts of the Order-to-Cash workflow by:
Responding automatically to invoice queries and requests for documentation
Capturing PO numbers and promise-to-pay dates from conversations
Tracking and enforcing commitments, follow-ups and payment confirmations
Managing two-way billing and collections communication until resolution
Agents can work 24/7, respond in multiple languages and escalate matters to humans only when needed. Paraglide automates both inbound and outbound finance communication, reduces inbox backlog and helps teams reduce manual work and Days Sales Outstanding (DSO).
Strengths:
Native AI agents embedded in the finance inbox and connected systems
Prioritises payment blockers and high-impact cases
Tracks key commitments automatically and keeps data synced to finance systems
Works across channels and languages
Considerations:
As a newer platform, organisations may want to assess scalability plans and long-term roadmap
Value depends on clearly defined operational workflows and integration setupHighRadius
Best suited for: Large enterprises with complex global credit and collections operations.
HighRadius offers a broad O2C suite covering credit risk, collections, deductions and cash application. AI and machine learning are applied to predictive analytics, prioritisation and forecasting. It is widely recognised in enterprise environments and supports high transaction volumes.
Strengths:
End-to-end enterprise O2C coverage
Strong predictive analytics and risk scoring
Mature enterprise footprint
Considerations:
Implementation timelines can be long
Configuration may require significant internal resources
Esker
Best suited for: Organisations prioritising invoice processing and document automation.
Esker combines document capture, workflow routing and AI-enhanced OCR. It performs well in automating invoice intake and structured workflow approvals. For companies with heavy document-processing requirements, this can reduce manual effort significantly.
However, its AI capabilities are more focused on document automation than conversational workflow execution.
Strengths:
Strong invoice capture and processing automation
Reliable OCR and structured routing
Considerations:
Less focused on managing live collections conversations
More automation suite than autonomous agent system
Kolleno
Best suited for: Mid-market teams looking to modernise collections and payment experience.
Kolleno is more focused on collections workflows and payment enablement than on the full billing-to-cash operational cycle.
For teams trying to address end-to-end O2C friction, that narrower scope may matter.
Strengths:
Payment portal
Supports collections workflows
Can be relevant for teams focused mainly on payment experience
Considerations:
More collections-centric than full O2C
Billing workflow automation is less central
Less focused on the wider finance inbox and billing query layer
IBM Watsonx Orchestrate
Best suited for: Enterprises building custom AI agents across multiple departments.
IBM’s platform allows organisations to design AI agents that automate tasks across systems. It offers flexibility and integration capabilities across enterprise environments.
However, it is not an O2C-specific solution. Implementation requires design effort and technical oversight.
Strengths:
Highly flexible AI orchestration
Strong enterprise AI ecosystem
Considerations:
Not purpose-built for Order-to-Cash
Requires internal technical capability
UiPath (Agentic Automation)
Best suited for: Organisations already invested in robotic process automation.
UiPath combines RPA with AI decisioning to automate structured workflows. It performs well in repetitive system-driven tasks such as data extraction and system updates.
While AI capabilities are expanding, the platform remains rooted in structured automation rather than finance-specific conversational workflow management.
Strengths:
Strong, structured automation capabilities
Enterprise-grade scalability
Considerations:
Requires configuration to manage unstructured finance communication
Not designed specifically for O2C teams
Choosing the Right Platform
The right platform depends on where friction exists in your Order-to-Cash process.
If the challenge lies in credit modelling or global enterprise complexity, a large O2C suite may be appropriate. If document capture and invoice intake are the primary pain points, document automation platforms may suffice.
However, for teams where delays are driven by shared inbox backlog, disputes and constant communication, platforms built around AI agents and workflow execution tend to deliver faster operational impact.
The distinction is important. Some tools automate steps. Others manage the process.
In 2026, the strongest improvements in DSO and dispute cycle time are increasingly coming from platforms that reduce operational friction across billing and collections workflows — not just from better reporting or reminder scheduling.
Conclusion
Order-to-Cash has always been central to financial performance, but the pressure on finance teams has changed. Volumes are higher, customer relationships are more complex and expectations around working capital are tighter. Traditional automation improved structured tasks, but it did not remove the operational friction caused by constant communication, exceptions and follow-ups.
AI agents address that gap. They operate inside the workflow, managing billing queries, collections conversations and cash application tasks in real time. Instead of reacting to ageing balances, finance teams gain continuous execution support across the full O2C cycle.
The impact is practical and measurable. Fewer invoices stall because of missing information. Disputes are identified and routed faster. Payment commitments are tracked automatically. Cash becomes more predictable.
As AI agents mature, they are moving from experimental projects to standard operational infrastructure within Order-to-Cash teams. For organisations looking to improve DSO, reduce manual workload, and strengthen working capital control, the question is no longer whether automation is needed, but how it is applied intelligently.
In 2026 and beyond, the most effective O2C teams will not simply automate tasks. They will automate workflows.