With tight margins, complex supply chains, and unpredictable market trends, the focus for retailers in 2025 will likely remain on cost control and operational efficiency. To thrive in a competitive retail market, businesses must optimize every part of their operations, with the finance department playing a crucial role in this effort.
In finance, particularly in accounts payable, AI can streamline invoice processing, reduce manual tasks, minimize errors, and ensure efficient, consistent handling of invoices. Additionally, more advanced AI solutions can provide detailed real-time insights, creating a foundation for smarter decision-making and improved margins.
Here are five reasons why AI in accounts payable will be an essential tool for retail finance teams in 2025:
Manual processes such as the posting of incoming invoices, data entry and handling discrepancies are time-consuming and inefficient, especially in industries with high invoice volumes. With AI based AP automation, finance teams can automate these tasks, freeing up time for more strategic work like cost analysis and improving supplier contracts.
AI leverages machine learning to analyze historical data and identify patterns, enabling it to handle complex tasks faster and more accurately over time. This means the system continuously adapts to the unique needs of the business, improving efficiency without requiring manual adjustments.
A report from McKinsey shows that automation can reduce time spent on repetitive tasks by up to 70%. For finance teams in retail, this allows a greater focus on value-added activities that strengthen margins and enhance overall business performance.
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In the retail industry, where the volume of incoming invoices often is substantial, the risk of errors and inconsistencies in accounting is significant. Manual processes can also make businesses vulnerable to employee-dependent workflows, where discrepancies in handling invoices can lead to inaccurate financials and increased costs.
AI helps eliminate these challenges by standardizing processes. The technology ensures invoices are consistently coded to the correct accounts, regardless of who is managing them. By analyzing historical data and recognizing patterns, AI can identify anomalies such as pricing errors or incorrect dimensions and automatically suggest corrections.
According to Deloitte, AI can reduce errors in invoice processing by up to 90%. For finance teams in retail, this not only ensures more accurate accounting but also significantly reduces the time spent on correcting errors. With AI, financial management becomes a more accurate and predictable aspect of the business, strengthening financial control and decision-making.
One of the biggest challenges for retail finance teams is ensuring that invoices match purchase orders (POs), particularly when transaction volumes are high and complex. Manual processes for PO matching are time-consuming and prone to errors, leading to unnecessary costs and delays.
AI simplifies this process by automatically match invoices to the corresponding purchase orders. The technology identifies discrepancies, such as unexpected price changes, missing deliveries, or overbilling, and notifies the finance team so issues can be addressed promptly. This reduces the need for extensive manual reviews and lowers the risk of incorrect payments.
For the retail industry, where margins are slim and transaction volumes are high, intelligent matching means finance teams can save time, cut costs, and achieve more efficient processes. This technology enables businesses to improve supplier collaboration and handle procurement processes more precisely.
The retail industry requires constant monitoring of costs, margins, and market changes. Some advanced AI solutions automatically captures line-item data, enabling finance teams to identify cost drivers and anomalies with precision.
By analyzing each invoice line, these solutions can uncover pricing trends on key products, detect discrepancies like pricing errors or unexpected cost increases, and ensure that price adjustments are identified. This not only provides better cost control but also establishes a solid foundation for procurement strategies and supplier negotiations.
When this level of detail is combined with real-time insights, businesses can respond quickly to supplier price changes, adjust strategies, and avoid unnecessary costs. According to Harvard Business Review, access to real-time data can reduce decision-making cycles by up to 25%, while Gartner highlights that AI can help businesses adapt to market trends 50% faster than competitors.
This combination of real-time insights and detailed line-item data, available in some advanced AI solutions, empowers finance teams to reduce costs and improve margins – a critical advantage in a competitive market.
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With growing demands for sustainability reporting, including tracking of carbon footprints, finance teams need precise data that can be reported effectively and accurately. From 2025, retail businesses in many regions will face stricter regulations requiring them to document the environmental impact of goods and services.
As described, some AI solutions automatically collect detailed data from invoice lines. Businesses can easier monitor CO₂ emissions at product and category levels, automate climate accounting, and enhance sustainable procurement strategies. This reduces the need for time-consuming manual data collection, and businesses can better comply with national and international reporting standards. At the same time, it strengthens the finance team’s role as a strategic partner in the company’s sustainability efforts.
For finance teams in the retail sector, AI presents a unique opportunity to tackle complex challenges, reduce costs, and improve decision-making. At the same time, detailed insights and automation make it easier to meet increasingly strict sustainability reporting requirements, strengthening the company’s market position. By adopting intelligent solutions, businesses can save time, enhance accuracy, and achieve better margins – all on a platform designed for the needs of the future.