Rasmus Areskoug

HighRadius and Esker are both enterprise AR platforms, but they solve slightly different problems: HighRadius goes deeper into AR-specific modules, while Esker covers a broader slice of finance operations. HighRadius is stronger for teams that need dedicated AR functionality such as cash application, credit management, deductions, treasury, and collections in one platform. Esker is stronger for teams that want a wider finance operations suite, with AP automation, procurement, order management, and AR under a single vendor. Paraglide is an AI-native alternative to HighRadius and Esker built to handle full AR conversations, not just workflows, templates, or first-touch reminders.

Gaviti is a mid-market AR platform with strong cash application, rule-based dunning workflows, payment tracking, and AR dashboards. Teams usually look for Gaviti alternatives for two reasons: they need help with inbound billing queries and customer replies, or they want AI to handle collections work more autonomously. Paraglide is the strongest Gaviti alternative for high-volume AR teams that want AI agents to manage billing queries, disputes, deductions, follow-up, and collections conversations end-to-end.

On 24 March 2026, the UK government published a late payment reform package aimed at large businesses that pay smaller suppliers slowly. The key proposals are a 60-day hard cap on payment terms, mandatory statutory interest at 8% above the Bank of England base rate, and stronger powers for the Small Business Commissioner to investigate poor payment practices and fine persistent offenders. This is not yet a fully implemented law. It is a government reform package and consultation response setting out what ministers intend to legislate for. Although the reforms are not yet fully implemented, finance teams already have enough detail to start assessing where payment processes may come under pressure.

The next phase of finance transformation will not be defined by better dashboards or another round of system change. It will be defined by whether finance can improve execution in the parts of the process where work is still manual, fragmented, and difficult to scale. This is where agentic AI fits. In many organisations, the real pressure sits between invoicing and payment, where queries, disputes, deductions, and other exceptions take up time, delay cash, and create more work across finance teams. Used properly, it helps finance deal with that work more consistently and with less manual effort.

Debtor days, also known as Days Sales Outstanding (DSO), measures how long it takes to collect cash after an invoice is issued. It is one of the clearest indicators of how efficiently a business converts revenue into cash. This guide explains the debtor days formula, shows a step-by-step example, and outlines the operational issues that usually push DSO higher. It also covers common calculation mistakes, why a single headline number can be misleading, and what finance teams can do to bring debtor days down.

Discounts and allowances can look simple on paper, but in practice they’re one of the most common reasons finance teams end up debating net sales vs gross sales, margin accuracy, and whether something belongs in marketing expense. In this article, you’ll learn what discounts and allowances really mean in accounting terms, how they should flow through the P&L (especially in FMCG), and when they should not be treated as marketing.

DSO, DPO and DIO are three of the most important metrics in working capital management because they show how quickly a business turns trading activity into cash. DSO measures how long it takes to collect from customers, DPO measures how long the business takes to pay suppliers, and DIO measures how long cash sits in inventory before stock is sold. Together, these metrics determine the cash conversion cycle and give finance teams a clearer view of operating cash flow, liquidity and capital tied up in day-to-day operations. They are useful not just for reporting, but for diagnosing where cash is getting stuck across receivables, payables and inventory. For AR and finance operations teams, DSO is often the most immediate lever. Slow collections are rarely just a chasing problem. They are often caused by billing queries, disputes, missing remittance, weak follow-up discipline or poor finance inbox management. Understanding DSO, DPO and DIO properly helps finance leaders connect operational friction to cash performance.

Finance operations teams are being asked to do two things at once: protect control and move faster. That is getting harder as invoice volumes rise, customer queries pile up, and more of the order-to-cash process gets pushed into shared inboxes, spreadsheets, and disconnected workflows. The pressure is most visible in accounts receivable. Collections stall because a customer is waiting for a copy invoice, a corrected billing contact, a missing PO number, a remittance clarification, or a dispute response. None of that sits neatly inside the ERP. It sits in email threads, handovers, notes, and manual follow-ups. That is why many finance teams still struggle to improve DSO even after investing in billing systems, dunning tools, or dashboards. AI agents are starting to change that. Not by replacing the finance function, and not by simply drafting better emails, but by taking ownership of repetitive operational work across the finance inbox, collections follow-ups, dispute handling, promise-to-pay tracking, cash application support, and credit workflows. For finance operations leaders, the opportunity is practical: reduce manual workload, tighten execution, and remove the day-to-day friction that delays cash collection.

Order-to-cash automation has evolved in stages. RPA handled structured data tasks and legacy platforms automated outbound payment reminders, but neither addressed the conversations. Inbound billing queries, collections follow-ups, dispute notifications, and remittance questions still land in a shared inbox and get handled manually, one email at a time. This was not possible to automate with templates. Agentic AI closes this gap. Unlike RPA or reminder platforms, AI agents read unstructured emails, understand intent, retrieve live account data, reference full conversation threads, and act across the entire O2C cycle, from invoice inquiry management and collections to cash application and credit decisioning. They can automate replies to invoice queries, autonomously take actions across systems, and manage end-to-end collection conversations. Paraglide is the only AI-native agentic AR platform built for the full O2C conversation. Customers reduce DSO by an average of 34%.

Deductions and disputes are major blockers to getting paid in B2B, especially across FMCG, retail, wholesale, logistics, and manufacturing. Most AR teams manage them through a shared inbox, where claims get buried under routine queries, sit unresolved, and end up blocking payments far larger than the disputed amounts. Resolving these issues often depends on input from teams outside AR—such as sales, operations, or logistics—and without proper tracking, requests easily fall through the cracks. Traditional pre-AI tools improve visibility but don’t do the work, leaving teams to manually triage, input data, and chase follow-ups. AI agents change this by automating the entire process—from reading and routing to summarising, challenging, and resolving claims. By handling routine queries, they free AR teams to focus on the disputes that truly block cash. Paraglide stands out as the only AI-native AR platform that not only tracks deductions and disputes, but actively resolves them.

Invoice inquiry management is the day-to-day work of handling customer billing emails, from invoice copy requests and missing PO numbers to statement requests, disputes, and payment queries. Most finance teams still manage this manually in a shared inbox, which is why it so often becomes a bottleneck. This article explains why that work has a bigger impact on collections than most AR software accounts for, and how agentic AI solutions like Paraglide improve invoice inquiry management with accounts receivable AI agents that automate the finance inbox.

Odoo is an ERP that helps finance teams manage the core parts of accounts receivable, including invoices, outstanding balances, and scheduled follow-ups on overdue accounts. Many overdue invoices are delayed by billing questions, disputes, missing PO numbers, deductions, or requests for documentation, all of which usually end up being handled manually in the finance inbox. This article explores how AI agents for accounts receivable work with ERP systems like Odoo to help finance teams manage dunning, disputes and invoice queries better. Paraglide is an Agentic AI solution that integrates with Odoo to automate that layer of work, including payment reminders, replies, invoice queries, disputes, deductions, and other conversations that block payment.

Deductions are one of the most persistent operational challenges in the Order to Cash (O2C) process. When customers pay less than the invoiced amount, finance teams must determine whether the deduction is legitimate, investigate supporting evidence and either recover the remaining balance or approve the claim. For many organisations, this process remains highly manual. Deductions arrive through shared finance inboxes, customer portals, remittance files or ERP exceptions, and Accounts Receivable teams often spend significant time sorting emails, gathering documents and coordinating with internal teams before a case can be resolved. As deduction volumes increase, the impact becomes visible in delayed cash collection, higher Days Sales Outstanding (DSO) and increased operational workload across finance and shared services teams. A structured deduction process improves visibility, reduces investigation time and prevents revenue leakage. Increasingly, finance organisations are introducing automation and AI agents to handle the repetitive administrative work involved in deduction management while enabling AR teams to focus on investigation and resolution. This guide explains how deductions arise in the O2C process, how the deduction workflow operates in practice, the most common deduction categories, the metrics finance leaders should track and how automation can improve deduction resolution and working capital performance.

Inventory valuation determines how businesses assign cost to goods sold and to the inventory that remains on the balance sheet. The method used influences profit reporting, tax liabilities, working capital levels and how efficiently capital is deployed across the business. The most widely used inventory valuation approaches are First-In, First-Out (FIFO), Last-In, First-Out (LIFO) and the Weighted Average Cost method. Each method determines which inventory costs are recognised when products are sold and which costs remain recorded as inventory. Because inventory is usually one of the largest current assets on a company’s balance sheet, valuation choices affect financial statements and operational metrics such as gross margin, working capital and return on capital employed. Finance leaders therefore, need to understand not only how these methods work, but also how they influence financial performance, reporting transparency and capital efficiency. This guide explains the main inventory valuation methods, how they affect financial statements and how businesses can choose an approach that aligns with their operating model and financial objectives.

Trade spend sits at the heart of financial planning and performance for FMCG (Fast-Moving Consumer Goods) companies. It is one of the most complex and largest components of the cost base, frequently representing 10–25% of gross sales and directly affecting revenue, profitability, and working capital. In this article, you’ll learn exactly what trade spend is, how it’s accounted for under modern accounting standards, and how to optimise, allocate, and control trade deductions and disputes for stronger margins.

Order-to-Cash (O2C) describes the complete flow from when a customer places an order to when cash is collected and recorded. Over time, technology has helped streamline parts of this cycle. ERP systems auto-generate invoices, reminders push out automatically, and dashboards track key metrics. But the biggest ongoing challenge remains the volume of operational work around that cycle, answering invoice queries, handling disputes, matching payments, clarifying PO numbers, and chasing commitments. In 2026, AI agents are automating that operational layer, reading messages, identifying intent, taking action in systems and escalating only when needed. As a result, Order-to-Cash teams spend less time on repetitive work and more time on strategic work.

Sales ledger control ensures that customer balances in the receivables ledger match the total in the general ledger. Strong control reduces errors, prevents write-offs, and gives finance teams confidence in their numbers. This article explains the basics of sales ledger control, common reconciliation issues, and practical steps to keep accounts accurate, from invoice creation to cash application and month-end close. We also show how AI agents can help automate checks, flag exceptions, and free finance teams to focus on resolving problems rather than hunting for them.

Dunning is the process of following up on overdue invoices to ensure payment is received. It is a structured workflow that includes reminders, resolving issues that block payment, and escalating when necessary, all while keeping customer relationships professional and consistent. In SaaS companies, dunning works a little differently. Recurring revenue doesn’t always mean cash arrives on time, because unpaid invoices are often stuck in subscription workflows, billing questions, or internal accounts payable processes. Early-stage dunning confirms invoices are received and sent to the right person, while later stages involve more structured follow-ups, escalations, and actions tailored to different customer types. Effective dunning is more than sending reminder emails. Automation handles both routine follow-ups and the more complex tasks that arise when payments are delayed, but AI agents take it further. They can manage incoming emails, resolve disputes, capture missing information, and update systems automatically, making the entire dunning process and collections faster for finance teams.

Working capital management is a critical lever for finance leaders seeking to improve cash flow, operational flexibility, and business resilience. While many organisations focus on a single balance sheet figure, leading finance teams track multiple metrics to identify precisely where cash is tied up, including receivables, payables, inventory, and capital efficiency. In 2026, AI agents are increasingly helping finance teams optimise working capital by reducing delays in collections, resolving disputes, and providing actionable insights without adding headcount. This guide explores the seven key working capital metrics, how to calculate them, practical examples, and how AI agents can improve outcomes—especially by reducing Days Sales Outstanding (DSO).

Bad debt, doubtful allowances, and impairments are more than accounting concepts; they are practical levers that allow finance leaders to anticipate risk, protect cash flow, and make informed, confident decisions. These concepts work together as an ongoing credit risk management framework. By understanding how these concepts relate, spotting early warning signals in collection behaviour, and implementing a structured decision-making framework, finance teams can move from reactive write-offs to proactive management. Bad debt is the amount confirmed as uncollectible after reasonable recovery efforts fail, resulting in a write-off and a hit to profitability. Doubtful allowances are a proactive estimate of receivables unlikely to be collected, calculated using ageing, historical defaults, and customer-specific factors, reducing net AR without reversing revenue. Impairments are recognised when a receivable’s recoverable value declines due to credit deterioration, disputes, or customer financial difficulty, often before formal default, aligned with IFRS 9 expected credit loss thinking. Allowances predict portfolio-level loss, impairments flag specific exposures, and bad debt confirms loss once recovery is no longer realistic. Bad debt builds quietly through unresolved billing queries, missing documentation, slow dispute resolution, and broken payment promises. Early warning signs hide in email threads, CRM notes, and support tickets, not financial systems.

AI is not just automation, it spans three distinct approaches: rules-based automation, AI copilots, and AI agents; each suited to different types of work. AI agents represent a shift from assistance to execution, taking actions across systems, completing end-to-end workflows under human supervision and actually doing the work. Finance has adopted AI more cautiously than functions such as engineering, support, and sales. Finance is now well-positioned for agentic adoption, particularly in high-volume, repetitive, and measurable work. The highest-ROI uses of AI agents today are in operational finance, where these conditions are most common. The first and clearest high-ROI use cases are accounts receivable (billing support and collections automation) and accounts payable (invoice capture, coding, matching, and duplicate detection) Real value comes from agentic-native platforms that can safely act within systems, not legacy tools with chatbots rebranded as “AI-powered.”

AR teams managing hundreds or thousands of invoices per month hit a ceiling that process changes alone cannot fix. The constraint is not invoicing or reminder cadence; it is the volume of customer communication required before payment can proceed. Requests for invoice copies, PO validation, dispute resolution, and payment confirmation drive most delays. When these messages sit unanswered in finance inboxes, invoices age even when customers intend to pay. Workflow automation cannot manage inbound AR communication: ERPs and traditional AR tools track balances and send reminders, but they cannot read customer replies, resolve questions, or capture promise-to-pay commitments hidden in email threads. Platforms like Paraglide operate directly inside the finance inbox, using AI agents to manage customer replies, retrieve documents, capture promise-to-pay dates, and escalate only when human judgment is required, removing the communication bottleneck that drives DSO.

Decreasing DSO is about more than chasing customers faster — it's about building smarter, data-driven, and automated AR workflows. With AI agents managing reminders, resolving issues, and escalating intelligently, finance teams can collect faster, improve cash flow, and maintain strong customer relationships.

Kundreskontra är en central process för alla företag, men den har länge varit tung och arbetsintensiv. Med AI och automatisering kan företag nu frigöra tid, minska DSO, förbättra AR turnover och få bättre kontroll över sina åldersanalyser. Resultatet blir snabbare inbetalningar, starkare kassaflöde och en ekonomiavdelning som kan fokusera på det som verkligen driver värde.

Free cash flow conversion is a vital measure of financial strength because it shows how much of your reported earnings actually makes it into the bank. The biggest drag on FCF conversion is usually working capital, and within that, slow-paying customers. By managing accounts receivable tightly and reducing DSO, companies can unlock significant amounts of cash and transform weak FCF conversion into strong performance.

The role of the credit controller is evolving. Today, much of the job involves repetitive email and phone conversations with customers. Tomorrow, AI agents will handle these at scale, allowing humans to focus on the judgment calls, the disputes, and the exceptions that truly require their expertise. Credit controllers will not be replaced. They will be elevated, moving up the value chain to manage AI-driven teams of agents and focus on more strategic, value-adding work.

In large AR teams, overdue payments are usually due to inbox overload, not customer refusal to pay. High volumes of routine queries, missing POs, disputes, remittances, and invoice requests create delays that increase DSO, weaken forecasting, and stretch teams beyond capacity. AI agents for accounts receivable remove this bottleneck by automating high-volume finance communication. Agentic AR solutions can read and respond to emails, capture promise-to-pay dates, resolve disputes, match payments, and sync updates to ERP systems, turning unstructured inbox traffic into structured cash-flow data.