Mittelstandspresse
16.09.2026
AI in Retail Needs Clean Process and Inventory Data First
Insights from NRF Retail's Big Show Europe 2026: Why the Operating Model Comes Before Technology
Paris, 16.09.2026 (PresseBox) - "Technology alone does not drive transformation. Embedding the operating model comes first." This insight from the supply chain transformation of M&S Food, presented at NRF Retail's Big Show Europe 2026, highlights a key requirement for successful AI in retail: before AI can optimise processes, it needs a reliable data foundation. This is where clean process and inventory data become essential.
What the British retailer describes for its supply chain applies to retailers across Europe. An operating model defines who carries out which tasks and how. Only when these processes are clearly defined and consistently recorded in digital form does data emerge that AI applications can rely on.
Why AI Projects in Retail Often Fail Because of Poor Data Quality
Demand forecasting and automated replenishment rely on the stock level shown in the ERP or merchandise management system. In daily store operations, however, this book stock often differs from what is actually on the shelf. The causes are usually simple: goods receipts are booked based on the delivery note without checking, returns remain unprocessed in the stockroom for days, and transfers between stores are still documented on paper.
AI cannot see these gaps. If it works with phantom inventory, no reorder is triggered even though the shelf is empty. In omnichannel retail, this quickly leads to cancelled Online orders. On the other hand, unrecorded stock causes overordering and, as a result, write-offs. Staff also lose time manually investigating stock discrepancies.
What Data Retailers Need for Using AI
Stock figures alone are not enough. What matters is that every change is documented in a traceable way: which item was moved, when, where, by whom and for what reason. Three data areas are particularly relevant:
Goods movements such as goods receipt, stock transfers, returns and write-offs
Stocktaking results and stock corrections, including the reason for each discrepancy
Store tasks such as shelf replenishment, price labelling or picking online orders
Only this process data shows why stock levels differ. It gives AI the context that sales data alone cannot provide.
How Retailers Can Capture Inventory Data Reliably
Data quality is created where goods are moved: at the loading dock, in the stockroom and on the shop floor. Mobile, scan-based workflows replace paper lists and delayed bookings. This is where COSYS Retail Management Software comes in. The modular platform digitalises operational processes between stores, warehouses and head office in one shared app, from goods receipt and stocktaking to fulfilment and returns management.
With COSYS Task Management, tasks are assigned centrally and their completion is documented. Through the ERP integration, the captured data flows into the existing system, which remains the leading system. Retailers can start with a single module, such as stocktaking or returns, and later extend the retail software with more modules, devices or stores. This keeps both investment and project risk manageable.
Software, Hardware and Services from One Provider
Reliable data requires reliable devices. COSYS complements its software with rugged mobile computers and tablets from Zebra, Honeywell and Datalogic, mobile printers for labels, smart lockers for order pickup and robots for warehouse and fulfilment operations. With device services and COSYS MDM, all devices are managed consistently across stores and stay ready for use.
What AI in Retail Can Achieve with Clean Process Data
Once data is structured, it can be analysed with the process data analytics of COSYS Business Intelligence. COSYS AI accesses the company data recorded in the system and answers questions in natural language, for example:
"Which three stores had the highest discrepancies in the last stocktake, and in which product groups?"
"Which items had the most stock corrections without a discrepancy reason last month?"
"Which return reasons were recorded most often in the first half of the year?"
Store and logistics managers no longer need to analyse data exports manually in spreadsheets. They identify patterns faster and can address the root causes of stock discrepancies directly.
Where Retailers Should Start Before Their First AI Project
Practical tip: Choose one store and three items that are regularly missing from the shelf even though the system shows stock. Together with the store team, follow how these items move through your processes over two weeks, from delivery to sale. Wherever the flow of goods is no longer fully traceable, you have found the most useful starting point for digitalisation. This turns the message from NRF Retail's Big Show Europe 2026 into concrete action, long before the first algorithm is used.
Do you want to build AI in retail on reliable process and inventory data instead of isolated pilots?
Contact the COSYS team for individual advice on Retail Management Software, mobile data capture, ERP integration, process data analytics and AI-supported evaluations. Schedule an appointment at vertrieb@cosys.de or +49 5062 900 0.
Ansprechpartner
Vertrieb
+49 (5062) 900-0
Über Cosys Ident GmbH:
Die COSYS Ident GmbH mit Sitz in Grasdorf (bei Hildesheim) besteht seit knapp 40 Jahren und ist eines der führenden Systemhäuser im Bereich mobiler Datenerfassungslösungen für Android und Windows. Ein mittelständisches Unternehmen, das seit 1982 die Entwicklung von Identifikationssystemen vorantreibt und heute branchenspezifische Komplettlösungen für nahezu alle gängigen Geschäftsprozesse anbietet. Vom Prozessdesign und der Konzepterstellung, über die Implementierung der Hard- und Software bis hin zum Projektmanagement und maßgeschneiderten Wartungsverträgen, decken wir das komplette Leistungsspektrum der Systementwicklung, Integration und Betreuung ab. Des Weiteren bietet COSYS einen Reparaturservice, WLAN-Funkvermessung, sowie Lösungen für die Bauteil-Rückverfolgung mittels DPM-Codes.
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Clean process and inventory data as the foundation for AI in retail
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