Artificial Intelligence (AI) is reshaping retail supply chains, creating new possibilities to streamline processes and boost productivity. However, there’s a crucial part of retail logistics that’s a few steps away from even getting close to unlocking the potential of AI. Stuart Greenfield from Advanced Supply Chain explains why a reality check is needed during pre-retail logistics.
Adoption and utilisation of AI is becoming a strategic priority for optimising retail supply chains, with senior leaders exploring ways to enhance demand forecasting, quality control and next-generation sustainability insights.
At the recent Data Driven Value Chain Springboard, hosted by The Consumer Goods Forum, retail professionals looked at how AI agents are accelerating workflows, uncovering actionable insights and creating entirely new opportunities for growth. Retailers want to build smarter, faster and more resilient supply chains, and AI is seen as a priority solution for accelerating change.
Deloitte’s 2026 Retail Industry Global Outlook highlights that 41% of retailers plan to be using AI within 12 months to support supply chain visibility. There’s a trend of retailers pivoting from experimentation to embracing new platforms in core operational capabilities, 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 a bit of blind spot for AI.
The practical realities of pre-retail logistics
There are a wide variety of different tasks taking place during pre-retail logistics. Activities such as re-labelling, ticketing, kitting, re-packing, and sorting mixed SKUs all require flexibility and responsiveness. They are tasks that create different requirements to get products retail-ready for sale and tend to rely heavily on hands-on execution and human judgment. This can limit automation, which impacts the integration of AI.
Where processes remain largely manual, there is often limited consistency and structure, resulting in fragmented and low-quality data. Without reliable patterns to learn from, AI systems can struggle to deliver meaningful insights and results. By contrast, automation can standardise workflows, generating repeatable and reliable workflows that generate high-quality data streams. Automation is, essentially, an enabler of AI. It reduces operational friction to introduce levels of consistency, clarity and predictability that AI needs to perform and scale.
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.
Laying the foundations for AI
Despite the complexity and variety of tasks throughout pre-retail logistics, there is a clear pathway to automation and digitalisation. Solutions, such as mobile kiosks and handheld label printers, can replace paper-based workflows. These technologies are designed for operatives constantly on the move around warehouses, allowing them to work efficiently while capturing data as they go. The result is improved connectivity, real-time visibility and a continuous flow of data – all critical foundations for harnessing AI.
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, making 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.
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Contributor: Stuart Greenfield, Advanced Supply Chain
