Rasmus Areskoug

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.