The Future of Intelligent Retail Starts With a Connected Foundation

The retail industry is increasingly adopting agentic artificial intelligence into its task execution processes. Industry analysts predict the market size for agentic AI in retail and e-commerce will grow from $60.43 billion this year to $218.37 billion by 2031. Yet when investing in AI, many retail technology leaders overlook the one area that determines whether the technology can scale successfully: the integration layer connecting enterprise systems.

Organizations traditionally treated middleware as an out-of-sight, out-of-mind function. However, a business process can now pass through an API, a file transfer, an event stream, a broker, or a partner gateway. Each step introduces another failure point where bottlenecks can delay or halt data. As these processes become more distributed, AI is only as effective as the operational context it can access. Without connected systems, even the most advanced models struggle to make reliable decisions. This enables teams to identify exactly where delays, failures or exceptions occur before they affect suppliers, inventory, or customer fulfillment. Industry executives who want to integrate AI successfully must address this disconnect and unify visibility across the systems that support it.

Connecting the Dots Across the Enterprise

Current middleware systems often limit what autonomous agents can do — and retailers feel it. Twenty percent of sales organizations report negative AI investment returns of 50 percent or higher, while only 25 percent report positive investment returns of 50 percent or higher. These findings reinforce a broader reality: successful AI initiatives depend less on access to AI itself and more on the operational foundation supporting it.

For retailers, AI usually needs two interconnected capabilities to work effectively throughout middleware environments:

1. Analysis capabilities for growing telemetry: Intelligent systems need the capacity to sort through telemetry volumes reaching the petabyte scale to separate routine system activity from real issues. This detection relies on identifying where a problem originated and how data flows across live environments.
2. Operational context: AI agents need to understand the relationship between brokers, routes, topics, dependencies, and queues throughout production systems. This background allows AI to understand what an event means in the context of business data. Without it, middleware can only produce generic architecture signals that AI has trouble interpreting.

This framework is increasingly important as information volumes exceed what operators can decipher by hand. Agentic AI is no longer just useful for accelerating workflows. It is now one of the most practical ways to interpret data at scale. This means organizations must prioritize observability into how data flows between platforms so that AI can tie telemetry to clear business outcomes.

Rethinking Observability at Scale

Creating transparency throughout middleware retail environments often demands a stronger base layer, one that can connect systems and accommodate growing telemetry volumes. A unified base should provide visibility across interconnected middleware systems and partner platforms so predictive intelligence can read operational signals in context.

In retail specifically, true middleware observability goes beyond infrastructure health — it means transaction-level visibility across the entire business process. Technology leaders should be able to follow a transaction from PO to ASN to invoice or from refund to settlement across B2B gateways, cloud connectors, and downstream systems. This enables teams to identify exactly where delays, failures, or exceptions occur before they affect suppliers, inventory or customer fulfillment. Once organizations establish this operational transparency, AI can leverage all message flows, transaction paths, and production history to surface precise, organization-specific recommendations.

Building Retail Infrastructure for the Agentic Era

When AI has end-to-end insight into transaction movements and middleware patterns, it can surface optimization suggestions tailored to a specific retail organization’s processes. This allows agentic AI to effectively evaluate telemetry volumes that operators can no longer handle.

Armed with this foundation, technology leaders can move from reactive operations to predictive decision-making. As agentic AI becomes more embedded across retail, organizations with connected middleware will be far better positioned to scale automation, improve resilience, and deliver better customer experiences. Over time, identifying failures before they cause downstream impacts reduce over-receipts, unclaimed rebates, duplicate credits, and pricing drift, allowing retailers to deliver better customer and buyer experiences.

Greg DeaKyne is vice president of product management, meshIQ, a provider of multi-middleware observability and Apache Kafka® management solutions to streamline your DevOps and reduce operational costs.