90 percent of UK retail decision makers say they are exploring AI agents, and a third are already using them across chatbots, prediction and personalization, according to research from Eversheds Sutherland and Retail Economics.
With billions of pounds being invested in technology, it feels like we have to reach a tipping point. But why do 96% of managers not see ROI?
Vice President of Research and Innovation at Arvato.
The problem is not a lack of ambition. How to use the investment.
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Most deployments of AI tools are still point solutions, optimizing one task while the rest of the process remains fragmented and dependent on collaboration between systems and teams.
Sales jobs are not reorganized at the end, they are canceled. And that is understandable given the market conditions. Time pressures are great, customers expect more, and boundaries are tight. Going faster feels safer than going back to rewire the system.
But in the rush to act, many marketers are risking missing out on the returns that AI can bring. Until AI is embedded throughout the entire process chain, ROI will remain elusive.
The background revolution, how AI enables end-to-end process chains:
Much of the current discussion about AI in retail focuses on customer-facing applications, such as virtual shopping assistants, personalized recommendations, discount discovery, and product comparison. These are exciting developments, but they only scratch the surface of AI’s potential.
Big changes are happening behind the scenes. I believe the next wave of change will come from AI pilots to end-to-end, AI-enabled process chains, connected workflows where AI agents not only advise on one step, but orchestrate decisions and outcomes across multiple steps.
In other words, AI becomes the glue, enabling faster throughput, consistent quality and robust performance across variable volumes and complex service needs.
This means that retailers can be resilient in the face of disruption. For example, a beauty company launching a limited edition SPF skincare set for summer can rely on AI to ensure the integrity of the entire chain of procedures.
If a key ingredient is delayed or a shipment is disrupted, AI alerts to reorder stock, review promotions, and reschedule employees. By overseeing the entire process chain and connecting the dots, AI ensures product availability, consistent quality, and robust performance even under supply disruptions and seasonal spikes in demand.
“Manufacturing AI” in logistics: flexible, vendor-agnostic automation:
“Manufacturing AI” refers to AI systems fully deployed in real-world operations to actively support business decisions. It allows automation and robotics to be configured more flexibly and at a granular level, enabling complex environments and complex offerings to work at scale.
Effectiveness depends on performance. It requires translating digital decisions such as availability, promised delivery dates, replacements, re-routing, into reliable tangible results across warehouses, stores and carrier networks.
Process chains are not marching toward fulfillment, but manufacturing AI is expanding the possibilities, especially as retailers roll out promises of fast delivery, wide assortments, high profits and high volatility.
The main goal in this is the belief in the knowledge of the seller, which allows different automation technologies – often from different manufacturers – to work with each other and in close proximity to people, rather than locking the sales activities into a single proprietary stack that is difficult to adapt as the needs change.
As retailers prepare for summer launches, manufacturing AI can coordinate robots that pick different sizes and styles, conveyor lines move items to packing stations, and quality checks, based on image recognition.
All of this ensures that orders are fulfilled accurately and on time, stock is properly distributed, and customers receive a seamless shopping experience ahead of peak demand.
At Arvato, we build an IT platform for automation with this in mind, seamlessly connecting technology so sales operations can adapt to changing volumes, assortments and service offerings, while protecting reliability, speed and operating costs.
Feeding statistics “supercycle”:
In sales operations, data is the fuel behind better forecasting, faster fulfillment, and more reliable service, and it becomes even more valuable as organizations train and refine their AI models. But one of the biggest obstacles is still the scale.
Rarely do you have enough high-quality, labeled performance data to cover the full range of products, packaging types, seasons, promotions, and real-world variations.
This is where artificial data becomes a great accelerator.
Artificial data is artificially generated information that mimics real-world situations, allowing AI models to “learn” from situations that may be rare, difficult to capture, or expensive to replicate.
It can be used to train vision models and robots at scale by generating millions of realistic variations from a single image, in all different lighting conditions, packaging finishes, shapes, damage conditions, and edge cases that often appear in warehouses.
The result is models that work more reliably when assortment changes, peaks hit, or processes deviate.
Once deployed, better-trained automation generates more and better performance data, such as exception logs, cycle times and quality signals, which can be leveraged with AI to continuously improve performance.
This creates a self-reinforcing statistical supercycle where better models improve performance, and better performance creates better data to train the next iteration.
To maintain the human layer:
Stores don’t have a technical problem, they have a communication problem. Point solutions can improve forecasting, customer service or a single warehouse operation, but value is still lost in delivery, where variations and changes in priorities affect performance.
Real ROI will come from end-to-end, AI-enabled process networks that maintain context, streamline decisions across teams and systems, and translate insights into action across the entire fulfillment chain, improving reliability, speed and cost of use in a dynamic market.
In all of this, human control is still important.
Technological change must be accompanied by targeted training programs to equip employees with the necessary skills, from working effectively with AI tools, to directing automated processes, to managing variables, so that AI can strengthen daily operations and help teams deliver consistent results.
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