Closing the Retail AI ROI Gap With Connected Process Chains

Retailers are moving fast on artificial intelligence. Research from Eversheds Sutherland and Retail Economics suggests that around nine in 10 retail decision-makers are exploring AI agents, and roughly a third are already deploying them in areas like chatbots, forecasting, and personalization. Yet the majority of leaders still report limited returns from those efforts.

The issue is rarely due to a lack of ambition, and more so application. Many deployments remain point solutions, optimizing single tasks, while the wider workflow remains fragmented. With tight margins and high customer expectations, quick fixes can feel safer than reworking core processes. But isolated wins don’t add up to reliable, scalable return on investment.

From Front-End to Connected Process Chains

AI conversations tend to focus on customer-facing tools like chatbots and personalization. However, this is only part of the opportunity. The bigger shift is operational; using AI to connect whole process chains and coordinating decisions across planning and execution.

In practice, this means aligning inventory, labor, fulfilment capacity and transport so the organization can respond faster, more consistently, and reduce the impact of disruption when conditions change.

For example, take a beauty brand launching a limited-edition summer bundle. If inbound stock is delayed, the impact isn’t limited to supply, it affects availability, promotions, staffing and fulfilment priorities. A connected approach can help coordinate actions such as reallocating stock, adjusting promotional plans, and rescheduling labor to protect service levels without expensive last-minute workarounds.

'Production AI' and Orchestration Across Mixed Automation

In mixed operations, “production AI” runs at scale in live environments, dynamically coordinating automation and robotics. It preserves context across handovers and enables systems to be configured in real time, ensuring seamless operations.

A key principle to operational excellence is vendor-agnostic design. This allows technologies from different manufacturers to work together without locking operations into one proprietary stack. Done well, orchestration improves throughput, accuracy and resilience.

Ahead of summer launches, production AI can synchronize warehouse automation, directing picking robots, managing conveyor routing, and triggering vision-based quality inspections. By keeping these systems aligned, it helps maintain fulfilment accuracy, allocate stock effectively, and protect the customer experience when demand peaks.

Turning Operational Data Into Continuous Performance Gains

Operational AI depends on high-quality data, but this is often lacking. Synthetic data can accelerate training by simulating hard-to-capture scenarios, such as damaged packaging, unusual orientations, and edge-case lighting, making models more reliable.

Once deployed, automation also generates richer operational signals (e.g., cycle times, exceptions, quality indicators), creating a feedback loop. Better models improve execution, and better execution produces better data.

Maintaining the Human Layer

Retail’s toughest challenge isn't technology, it’s coordination. ROI comes from connecting decisions across the chain and reducing friction at handovers.

Throughout this process, human oversight remains essential for managing exceptions and ensuring AI supports consistent outcomes, backed by targeted training and clear ways of working.

Dietmar Guhe is vice president of research and innovation at Arvato, a 3PL logistics and supply chain management firm.