Everything You Need To Know About Implementing AI Agents for Accounts Receivable

Finance teams have spent the last decade trying to automate accounts receivable. First came email templates. Then workflows. Then collections dashboards.

It helped — but it didn’t fix the core problem.

Accounts receivable is still full of manual processes. Payment chasers live in inboxes. Reconciliation depends on humans spotting patterns. And when invoices pile up, manual effort rises faster than headcount. That creates a fragile operating model where cash flow depends on who’s available, who’s experienced, and who has time to chase the right accounts.

That’s why AI in accounts receivable is shifting again. Instead of “automation tools” that move tasks around, more companies are implementing AI agents — AI systems that can take action, handle exceptions, and support AR teams with predictive analytics and workflow execution. Platforms like Kolleno position its AI Agent as a collections assistant that can automate routine tasks and help teams manage collections and automated payment reconciliation through “Agentic Workflows”.

In this guide, we’ll cover what implementing AI agents in AR really means, how to do it safely, and how to make sure it delivers measurable improvements in cash flow management — not just nicer dashboards.

What accounts receivable refers to (and why it’s hard to automate)

At its simplest, accounts receivable refers to the money customers owe you for goods or services already delivered.

In practice, it’s a chain of interconnected AR processes:

• invoice processing and processing invoices
• sending payment reminders
• tracking outstanding invoices
• managing disputes and deductions
• reconciling incoming payments
• updating account status in your systems
• forecasting cash flow

Each step touches systems, people, and customer relationships. That’s why automation has historically struggled: it’s not one process, it’s a messy ecosystem.

And when AR breaks down, the result is predictable: more late payments, worsening days sales outstanding, and less cash flow visibility.

What AI agents change in AR (vs traditional automation)

Traditional accounts receivable automation tools automate steps. For example: send reminders three days before the due date, then again at 7 days overdue.

AI agents automate decisions as well.

Instead of relying only on static rules, AI systems can learn from historical data, payment history, and customer data to spot patterns and determine next best actions. With machine learning and natural language processing, they can also interpret messy inputs like inbound emails, missing remittance details, and partial payments.

In other words: AI agents reduce repetitive tasks and manual processes because they can handle ambiguity — which is where AR teams spend most of their time.

Kolleno describes this clearly: its AI-agent looks at customer records and auto-generates workflows with conditional branches based on what has worked best in the past, then adds clients into that plan with personalised messages.

The business case: why implementing AI in AR matters

You’re not implementing AI for the sake of it. You’re trying to improve cash flow.

When AI in accounts receivable works properly, you’ll typically see:

1) Faster collections and fewer payment delays

AI agents can prioritise accounts based on risk and act earlier — before invoices become overdue.

2) Better forecasting cash flow

With predictive analytics, you can forecast incoming payments based on payment behavior, not hope.

3) Stronger customer relationships

This one surprises people. Better customer communications reduce friction and improve customer satisfaction, because follow-ups become timely, consistent, and accurate — rather than rushed and reactive.

4) Operational efficiency across financial operations

Implementing AI agents reduces manual effort across finance operations, not only collections. It touches invoice processing, payment tracking, disputes, and reconciliation.

Step 1: Audit your current AR processes before you automate

Before implementing AI, map your current AR processes.

This matters because AI systems rely on context. If your workflows are undocumented, inconsistent, or split across spreadsheets, implementation will feel painful.

A quick AR audit should cover:

• how invoice processing works today
• how you handle payment terms and credit terms
• how payment reminders are sent (who owns them, and how often)
• how disputes and deductions are handled
• how you track incoming payments and payment tracking
• which steps rely on manual data entry or manual tasks

This stage doesn’t require new software. It requires clarity.

Step 2: Get your data foundations right (AI systems rely on this)

AI implementation is only as good as the data feeding it.

In AR, the key data sources include:

• invoices, credits, payments
• customer records and contacts
• communication logs
• CRM data (where relevant)
• payment history and past payment behaviors
• customer payment behavior and customer behavior signals

If you want AI to analyze customer behavior, you need clean, accessible financial data and enough transaction volume to find patterns.

A common pitfall here is assuming the vendor will “fix your data” during onboarding. Vendors can help, but data integrity is still your responsibility — especially for sensitive financial operations.

Step 3: Integrate with existing systems (don’t build a second AR universe)

One of the biggest reasons implementing AI fails is poor integration.

If an AR AI tool doesn’t integrate with your existing systems, it forces teams to work across multiple sources of truth. That increases manual effort and lowers trust in the output.

Your AI solution should integrate into your:

• ERP tool
• accounting system (for invoices, payments, status)
• payment providers / bank feeds
• CRM (optional)
• email systems (for customer communications)

Kolleno explicitly positions its AI Agent as something that can be integrated with enterprise systems, and frames the platform around collections, payments, and reconciliation in one place.

Step 4: Design workflows that reflect real AR operations

A key misconception: people think AI agents replace processes.

They don’t. They execute processes.

So when implementing AI agents, your team still needs to define AR operations rules such as:

• segmentation: which accounts get which approach?
• escalation logic: when do you call vs email?
• approval processes: when does a human need to intervene?
• dispute routing rules
• payment plans criteria and exceptions
• when to offer early payment discounts

Your goal is not zero humans. Your goal is fewer repetitive tasks and smarter human intervention.

Step 5: Decide where to keep human intervention (and why)

Some AR activities should remain human-led:

• sensitive customer negotiations
• complex disputes
• large strategic accounts
• exceptions involving contract nuance

But AI can still support these workflows by surfacing real-time insights and drafting communications.

Kolleno’s AI content includes examples of AI identifying commitments in customer emails and creating draft “promise to pay” entries linked to the right invoice — which reduces the chance teams miss customer signals.

That’s a strong model: let AI do the detection and drafting; let humans confirm and manage exceptions.

Step 6: Use AI to improve customer communications without damaging customer relationships

Collections often fails because messaging is either:

• inconsistent
• too aggressive
• too generic
• poorly timed

With AI agents, you can personalise customer communications at scale using payment history, account status, and patterns in customer behavior — while maintaining tone controls.

This improves customer relationships because customers don’t feel randomly chased. They feel managed.

Good implementations also support smarter alternatives such as:

• offering payment plans for accounts under stress
• adding early payment discounts for accounts likely to pay early
• adjusting payment terms based on risk

This is where AI moves from “collections automation” into cash flow management strategy.

Step 7: Use predictive analytics to improve cash flow forecasting

Once AI agents are operating, their biggest value often becomes forecasting.

With enough data, predictive analytics can identify:

• which accounts will pay late
• likely payment timing based on payment patterns
• expected cash flow trends by segment
• risk signals (e.g., sudden change in payment behavior)

This supports better cash flow forecasting and improves cash flow management across the business — especially for finance leaders needing clearer cash visibility.

It also helps finance operations reduce surprises, because problems show up earlier.

Step 8: Put access controls and data privacy at the centre

If you’re implementing AI in financial operations, security isn’t an “IT checkbox”. It’s part of the business case.

Your AI systems must be designed with:

• role-based access controls
• audit trails (who did what, when)
• encryption and secure hosting
• strict handling of sensitive data
• explicit data privacy rules

Kolleno’s AI Agent page states that customer data is used exclusively to power AI features for the organisation and is not used to train AI models for other users, and references ISO 27001 and SOC 2 Type II certification.

You should expect this level of clarity from any vendor.

Step 9: Roll out in phases (implementation plan that works)

Here’s a rollout approach that works for most accounts receivable teams:

Phase 1: Automate payment reminders + prioritisation

Start with low-risk workflows. Let AI send payment reminders and triage accounts.

Phase 2: Expand to invoice processing + invoice capture

Add automated invoice capture and invoice processing support (especially for high volume environments).

Phase 3: Cash application and incoming payments

Implement AI matching and reconciliation to reduce manual processes.

Phase 4: Advanced workflows

Disputes, payment plans, segmentation logic, forecasting cash flow.

This approach avoids disruption and gives finance teams time to adapt.

Step 10: Measure impact (and make sure you get healthier cash flow)

Implementation success should be measurable.

Track:

• days sales outstanding (DSO)
• overdue rate and value of outstanding invoices
• payment delays by segment
• time spent on repetitive tasks
• proportion of AR activity requiring human intervention
• customer satisfaction and dispute turnaround time
• cash flow trends vs forecast accuracy

The win isn’t just productivity. It’s healthier cash flow, better cash visibility, and stronger control of the receivables engine.

Where Kolleno’s AI Agent fits

A useful benchmark when implementing AI agents is whether the platform supports both execution and control.

Kolleno positions its AI Agent as a collections assistant that automates routine AR tasks and supports collections and reconciliation via “Agentic Workflows”. It also links AI capability to core financial operations outcomes: improved collections, better forecasting, and less manual effort.

If you’re evaluating options, look for this blend:

• execution: automate follow-ups, cash application, dispute workflows
• intelligence: predictive analytics tied to real payment behavior
• governance: approvals, access controls, auditability

That’s the real difference between “AI features” and implementing AI systems that change the day-to-day reality for AR teams.

Final thoughts

Implementing AI agents for accounts receivable is no longer experimental. It’s becoming the standard operating model for modern financial operations.

But success depends on foundations: clean data, tight integration with existing systems, clear approval processes, and careful boundaries around sensitive data and customer communications.

Get that right, and AI doesn’t just reduce manual effort. It turns accounts receivable into what it should have always been: a cash flow engine that delivers consistent outcomes, better customer relationships, and smarter strategic decision making.

If you want to see what this looks like in practice, Kolleno’s AI Agent is one example of how AI in accounts receivable can support collections execution, reconciliation, and forecasting in one system — without losing human control.

Ready to start implementing AI agents to boost your accounts receivable? Book a demo to learn more about Kolleno.