How AI improves invoice reconciliation in grocery retail
08/08/2025
Even today, around 20% of invoice reconciliation operations in grocery retail are still handled manually — a slow, error-prone process that often leads to delays. Here’s how AI is stepping in to simplify, speed up and automate this critical task.
For those who still associate artificial intelligence (AI) only with chatbots or customer care automation, the data tells a different story. AI is already making a tangible, measurable impact on back-office processes in grocery retail. One area where its role is particularly significant is invoice reconciliation.
Let’s take a closer look at how it works — and where else it can be applied to simplify, accelerate and automate business-critical operations.
There’s a process within the grocery retail world that happens every single day, under the radar — invisible to consumers but absolutely vital for the financial and administrative stability of companies.
It’s the reconciliation between purchase order, delivery note, and invoice. In other words, it’s that complex and critical step where the system must ensure that what was ordered, delivered and invoiced matches perfectly — item by item, price by price.
A process that might seem trivial at first glance, but for those managing hundreds of stores, thousands of suppliers and millions of product lines, it can turn into a real administrative nightmare and that’s no exaggeration.
On average, 20% of invoice reconciliation operations in grocery retail are still handled manually: the driver unloads the goods, an operator marks deliveries by hand on the delivery note, notes any discrepancies, then someone inputs everything into the system, and another person checks the invoice and initiates the payment process.
It’s a cumbersome process that can cause delays of up to 45 days in invoicing, significantly increasing the risk of human error and generating a massive — often invisible, but very real — cost for companies. That’s why artificial intelligence is needed to revolutionise invoice reconciliation in grocery retail, leading the way toward the perfect digital match.
Technology is finally mature enough to step into one of the areas that has long resisted change and there’s no better place to introduce AI than in document reconciliation — because the cost of inaction has simply become too high.
The systems now operating in this space are increasingly designed to minimise human involvement in repetitive, error-pronetasks.
All it takes is a simple photo of the paper document using the app: artificial intelligence recognises the content (thanks to advanced OCR – Optical Character Recognition and NLP – Natural Language Processing techniques), interprets it, compares it with the order and the invoice, and automatically flags any discrepancies.
The benefits are clear: drastically reduced processing times, eliminated errors, real-time handling of disputes, and above all, a centralised and consistent view of the data — enabling faster, more informed decision-making.
The system works via web, requires no local servers or in-store technical setups, and is accessible from mobile devices — even offline. A truly smart companion for those working in the field.
This awareness is what led to the creation of the .one Retail platform — an application suite we developed at Aton in collaboration with Teksmar, a company that joined our group at the beginning of 2025 and specialises in the digital transformation of retail chains.
From the outset, our goal was clear: to simplify, accelerate and automate this critical process by harnessing the power of artificial intelligence.
The transformation underway isn’t just technical — it’s cultural. For decades, the administrative side of grocery retail has operated with a linear mindset, where each step depended on the previous one, and a single error could halt the entire chain.
In this context, introducing AI means changing the perspective:
It’s not just about speeding things up — it’s about rethinking the role of information itself: the consistency between different data points becomes a strategic asset, not just a bookkeeping requirement.
The vision driving innovation is that of a fully interoperable grocery retail ecosystem — where POS systems, ERP platforms, logistics tools and front-end applications all speak the same language and share a unified information base.
Thanks to AI, it’s now possible to develop predictive modules for stock management based on anomalies in delivery flows, automatically generate credit notes in case of non-conformities, and integrate with blockchain for the immutable certification of fiscal documents.
The real value of artificial intelligence today goes beyond automation — it lies in its ability to enable new forms of collaboration among supply chain players, including suppliers and customers.
This is a turning point that our country — and the retail sector in particular — can no longer afford to postpone. Those who think this is just about “speed” or “cost savings” are missing the core message: in the administrative processes of grocery retail, artificial intelligence is, above all, a factor of resilience and competitiveness.
In a market where margins are shrinking and complexity is growing, being able to reconcile accurately and instantly means being more responsive, agile and robust.
The real revolution is only now beginning to take shape, driven by the growing integration between artificial intelligence technologies and automated reconciliation processes.
Until now, the electronic invoice has been viewed mainly as a document to be stored, validated and transmitted. But in the coming months and years, this vision will change radically: the invoice will become an active and intelligent node within a digital ecosystem capable of self-verification, contextual learning, exception handling, and communication with other documents in the procure-to-pay cycle — such as purchase orders and delivery notes.
This transformation is made possible by AI’s increasingly refined ability to understand natural language, recognise patterns, process semi-structured data and, above all, adapt to real-world, dynamic, and often non-linear workflows. The static rules of legacy management systems cannot handle these variables without halting the process or generating errors. Today, thanks to natural language processing (NLP), AI systems can not only read invoice data — they can understand its meaning. For example, they can detect that a “non-VAT charge” refers to an authorised ancillary expense, or that a change in product code is linked to a pre-agreed substitution.
This is no longer just a mechanical field-to-field match — it’s semantic and contextual understanding.
Supervised machine learning models, trained on thousands or even millions of real documents, are now capable of learning from historical flows, adapting to user behaviour, and improving their reconciliation accuracy over time. With each new case processed, the algorithm refines its criteria and becomes faster, more accurate, and more autonomous.
All this points to a near future in which the procure-to-pay cycle becomes increasingly autonomous. Invoices will be received, interpreted, cross-checked, validated, booked and archived — all without any human intervention, except in the case of unforeseen anomalies.
Major grocery retail companies in Europe are already experimenting with AI-based reconciliation systems that handle over 90% of cases without human input. But this trend is quickly extending to mid-sized companies and the public sector as well, driven by evolving European regulations that will soon make e-invoicing mandatory for all B2B transactions.
In an economic landscape that is increasingly unstable, globalised, and reliant on digital flows, those who place everyday document processes — such as e-invoices — at the core of their AI strategy will be the first to benefit from greater resilience, efficiency, and competitiveness.
This is no longer just a technological issue — it’s a strategic challenge, and the time to act is now.
