With the major end-of-year sales peaks approaching, retailers face a challenge that goes beyond simply increasing sales: connecting stores, e-commerce, warehouses, customer data, and management systems into a single operation.

The conclusion comes from ARBENTIA, a technology consultancy cited by Ecommerce News, which identifies six major technological challenges for the sector. These include the transition from omnichannel to truly unified commerce, real-time inventory management , and the application of artificial intelligence to specific processes.

From omnichannel to unified commerce

Consumers no longer distinguish between a brand's different channels in the same way that companies traditionally organized them. A purchase can begin online , be completed in a physical store, picked up at another establishment, and eventually returned through another channel. The problem is that, in many companies, stores, e-commerce , warehouses, and marketplaces continue to operate on separate systems.

For ARBENTIA, the challenge lies in creating a common view of inventory, orders, and customers. This integration allows them to know the real-time availability of each product and support operations such as click & collect , returns between different channels, or order preparation directly from stores without resorting to parallel processes.

Real-time stock market analysis becomes critical.

Visibility over inventory is another priority. In a context of tight margins, both product shortages and excess stock can have a direct impact on results. Technologies such as advanced analytics, mobility, automation, the Internet of Things, and identification systems can help improve traceability and anticipate replenishment needs.

Needs also vary depending on the category. In fashion, for example, it is necessary to correctly control references by size and color. In food, high turnover, expiration dates, and traceability add new variables to inventory management.

AI is starting to move out of the experimental phase.

The third priority identified involves using artificial intelligence in real operational processes, rather than in isolated or experimental projects. The technology can be applied to demand forecasting, product recommendations, anomaly detection, order automation, or worker support.

In-store, an assistant can suggest complementary products based on the customer's history or provide additional information to the salesperson during the service.

In operations, AI can identify abnormal stock behavior or automate the entry of orders received by email. In customer service, it can summarize interactions, analyze sentiment, or suggest responses. The condition, according to ARBENTIA, is that artificial intelligence works with reliable data and properly integrated processes.

Personalization requires a unique consumer perspective.

Using data for personalization emerges as another challenge. Segmentation based solely on general characteristics or purchase history is considered insufficient given the possibilities offered by integrating different sources of information.

The goal is to build a more complete view of the consumer based on interactions in stores, e-commerce, customer support, and other touchpoints.

Analytics and artificial intelligence can then transform this data into recommendations, communications, promotions, and service experiences that are more tailored to individual behavior.

Legacy systems hinder transformation.

Technological modernization is another significant obstacle. A substantial portion of retail companies remain dependent on legacy systems , which have accumulated processes and developments over the years and can hinder the integration of new applications, digital channels, or artificial intelligence tools.

Instead of completely replacing these systems, the trend identified by the consulting firm points to a progressive modernization, maintaining a common technological core and adding new capabilities in phases.

This approach allows for prioritizing processes with the greatest potential impact and reducing the risk associated with large-scale transformation projects.

Cybersecurity and talent complete the challenges.

The more connected the retail sector becomes, the greater the potential exposure to technological risks. Stores, warehouses, mobile devices, cloud applications , digital platforms, and customer information become part of the same ecosystem, increasing the need to protect identities, access, and data.

ARBENTIA therefore argues that cybersecurity should be integrated from the design stage of processes and not added only at a later stage.

Technology, however, represents only one part of the transformation. Employee adoption is equally crucial: new systems, automation, and AI tools only yield returns when teams know how to use them and incorporate them into their work routines.

The transformation should proceed in phases.

The six challenges identified converge in the same direction: the future of retail is less about accumulating new tools and more about ensuring that the entire chain shares information and operates within the same reality.

ARBENTIA therefore advocates a phased transformation, starting with an analysis of operations and identifying areas where technology can generate the most value.

From there, projects can progressively integrate business management, inventory, in-store and warehouse mobility, analytics, automation, logistics, customer support, and artificial intelligence.