As Artificial Intelligence (AI) transforms retail supply chains, Stuart Greenfield from Advanced Supply Chain highlights the need for a reality check during pre-retail logistics
Retail supply chains are moving toward an AI tipping point, shifting from experimentation to embracing new platforms in core operational capabilities. 41% of retailers plan to be using AI within 12 months to support supply chain visibility, according to Deloitte’s 2026 Retail Industry Global Outlook.
There’s a broad push throughout retail to build smarter, faster and more resilient supply chains, and AI is seen as a priority solution for accelerating change. C-suite leaders recently converged at the Data Driven Value Chain Springboard, hosted by The Consumer Goods Forum, and AI was a hot topic of discussion for retailers considering operational readiness, trust and governance, to determine who wins in the next decade of retail.
Adoption and utilisation of AI is becoming a strategic priority for optimising supply chains, with decision makers exploring ways to enhance demand forecasting, quality control and next-generation sustainability insights. These are exciting times defined by new possibilities, and it’s important that momentum doesn’t lose sight of the practical realities during pre-retail logistics. This part of supply chains where products are readied for sale is something of a ‘square peg, round hole’ for AI.
The operational realities of pre-retail logistics
Many pre-retail logistics tasks still rely heavily on hands-on execution and human judgement. Activities such as re-labelling, ticketing, kitting, re-packing, and sorting mixed SKUs require flexibility and responsiveness. Processes involve dealing with a variety of different product types and preparation requirements, which doesn’t naturally align with the standardised, repeatable processes that automation typically depends on.
As a result, integrating AI into these operating conditions can feel like forcing a square peg into a round hole. When tasks remain largely manual, there can be limited, structured data, consistency and predictability for AI systems to learn from. In contrast, automated environments can create standardised processes that generate high-quality data and clear patterns, which AI can feed off.
In many ways, automation acts as a critical enabler of AI by reducing operational friction, improving consistency and creating structured conditions. Without these foundations, even the most advanced AI solutions risk falling short of expectations and their full potential.
In addition to a lack of automation during pre-retail logistics, there are also data challenges that can limit the successful integration and performance of AI. Many pre-retail processes still use manual methods for recording stock inventory management data. Paper-based systems tend to suit operatives constantly on the move around warehouses, with data input into digital systems occurring at the end of a shift or at a scheduled interval. This type of batch processing simply isn’t real-time enough to unlock the potential of AI, and is also more prone to error. Slow, and possibly inaccurate data input, is more likely to impede machine learning, as it disrupts patterns and predictions, making artificial intelligence outputs more unreliable.
Automating the way for AI
Despite the variety of tasks throughout pre-retail logistics, it is possible to automate and digitalise processes during this stage of retail readiness. Technology, such as mobile kiosks and handheld label printers, can replace analogue, paper-based methods. These solutions are designed for the fast-paced, mobile nature of warehouse environments, enabling operatives to work more efficiently on the move, while capturing data in real time. Connectivity, visibility and the continuous flow of data can be improved to create a solid base for supporting AI applications.
This level of automation and digitalisation can also reduce friction caused by non-compliance with a retailer’s supplier standards, and non-compatibility between a retailer’s systems and those used by multiple different suppliers. For example, labelling and barcodes can be standardised to improve workflows, data accuracy and the real-time sharing of information.
Ultimately, getting retail supply chains AI-ready is less about adopting advanced algorithms and more about building the right operational groundwork. For pre-retail logistics, this means creating consistent, connected and data-driven processes that will unlock the full potential of AI.
Click here to find out more about optimising pre-retail logistics or email: enquiries@advancedsupplychain.com
