Unified commerce built the infrastructure; AI makes it intelligent enough to actually work at scale.
Retailers spend billions connecting their systems. They’ve made strides in unifying inventory across channels, synchronizing order management, and integrating customer data. They're building the infrastructure for seamless commerce.
And customers still can't get what they want, when they want it, how they want it.
According to Incisiv's 2025 State of the Industry: Unified Commerce in Specialty Retail research, only 17% of retailers rate themselves as having "Leading" unified commerce maturity, even though 38% say their transformation initiatives are in advanced stages.
The problem isn't the ambition to be better. It's the execution gap between unified systems and intelligent operations. Retailers are building the pipes, but they're still manually controlling the flow.
Unification Without Intelligence Is Just Expensive Plumbing
Connecting systems was supposed to solve the omnichannel problem. It hasn't.
A retailer can have perfectly synchronized inventory across 300 stores and still disappoint customers. Their systems know a product exists in a store 12 miles away, but can't predict the customer won't drive there. They can see cart abandonment happening in real-time but can't intervene with the right offer at the right moment. They have unified customer profiles that sit largely unused because manually interpreting signals across touchpoints is impossible at scale.
Incisiv research shows 64% of retailers acknowledge critical gaps in core unified commerce capabilities.
The few leaders pulling ahead aren't just connecting systems—they're beginning to use AI to make those connections intelligent. Real-time inventory visibility can become predictive allocation. Unified customer profiles can enable anticipatory personalization. Connected order management can evolve into autonomous fulfillment optimization.
This is the difference between infrastructure and intelligence.
AI Doesn't Replace Unified Commerce. It Has the Potential to Make It Work.
The paradox of modern retail: you need unified data to make AI useful, and you need AI to make unified data actionable.
Most retailers are trapped on the wrong side of this equation. They spent years unifying data but don't yet have the AI capabilities to operationalize it. Or they've deployed AI pilots that struggle because fragmented data makes accurate predictions impossible.
Consider what's possible when a customer browses online, adds items to their cart, then walks into a store. Without AI, that remains three disconnected events that a store associate might manually piece together if the customer volunteers information. With AI operating on unified data, the system could instantly recognize the customer, understand purchase intent from browsing behavior, know which cart items are in stock locally, and surface personalized recommendations that complement their digital selections.
The infrastructure makes it possible. AI could make it happen automatically, at scale, in real-time—though few retailers have reached this level of execution.
According to McKinsey research, personalization can lift revenues by 5-15% and increase marketing ROI by 10-30%. But those gains only materialize when AI operates on truly unified customer data across every touchpoint—a reality most retailers are still building toward.
The New Economics Run on Intelligence, Not Just Integration
The ROI of unified commerce has always been compelling in theory. In practice, most retailers haven't captured the value because execution complexity overwhelms their organizations.
AI changes the economics entirely.
Incisiv's research reveals that retailers who rate their unified commerce maturity as high report 23% higher inventory turnover and 22% lower customer acquisition costs. These aren't incremental improvements from simply connecting systems. They represent step-function gains possible when intelligent automation operates across unified infrastructure.
With mature AI implementation, systems could dynamically route orders to optimal fulfillment locations based on real-time network capacity, predicted delivery windows, and customer value. They could personalize promotions by analyzing purchase patterns, browsing behavior, and lifecycle stage across every touchpoint. They could predict inventory needs by store, by SKU, by day based on local events, weather patterns, and historical demand signals that no human could synthesize.
The systems integration creates the possibility. AI can capture the value but most retailers are still in early stages of this journey.
But here's what separates leaders from laggards: the winners aren't treating AI as a feature bolted onto legacy unified commerce platforms. They're rebuilding their operating models around AI-native unified commerce.
Invisible Commerce Requires Visible Intelligence
Customers don't want to think about channels, inventory locations, or fulfillment methods. They want to browse, decide, and receive. Everything else should be invisible.
Making commerce invisible requires operations to be hyper-visible. AI provides that visibility.
When a customer expects same-day delivery, AI is calculating real-time feasibility based on current inventory positions, carrier capacity, traffic patterns, and weather forecasts. When they modify an order post-purchase, AI is instantly re-optimizing fulfillment routing and updating delivery promises. When they return an item at any location, AI is determining optimal disposition, updating inventory availability, and triggering replenishment if needed.
Every friction point customers no longer experience is an AI decision happening invisibly in milliseconds.
Incisiv found that 73% of retailers say seamless cross-channel cart and wishlist functionality are essential for customer convenience, but only 32% rate their capabilities as mature. The gap between aspiration and execution has never been wider. Closing it requires more than system integration. It requires intelligence that scales with complexity.
The store experience reveals the same pattern. While 69% of retailers identify real-time clienteling as a key enabler for personalized experiences, only 26% rate their unified customer profiles and mobile capabilities as mature. Associates need AI-powered tools that surface the right customer context at the right moment, not dashboards they'll never have time to check.
From Unified Systems to Unified Intelligence
The next chapter of retail isn't about connecting more systems. It's about making connected systems autonomously intelligent.
This means AI that doesn't just react to customer behavior but anticipates it. Fulfillment networks that don't just execute orders but optimize them. Service interactions that don't just resolve issues but prevent them. Inventory allocation that doesn't follow rules but learns from outcomes.
The cost pressures are mounting. Incisiv research shows 77% of retailers say customer acquisition costs have risen in the last year, while 63% report increased store labor costs. The retailers beginning to win in this environment aren't throwing more people or more promotional dollars at these problems. They're testing & deploying AI across unified commerce infrastructure to fundamentally change the cost equation.
They're beginning to use intelligent automation to deliver experiences that would be economically impossible with manual processes. Retailers with mature unified commerce capabilities report 18% lower cart abandonment and 27% lower fulfillment costs. Those aren't rounding errors. They're survival margins.
The technology exists. The infrastructure is being built. The gap is execution.
The Window Won't Stay Open
The 17% of retailers who achieved "Leading" unified commerce maturity aren't just ahead on a curve. They're building compounding advantages that will be difficult to overcome. Every AI model gets smarter with more data. Every intelligent automation improves with more interactions. Every personalization algorithm gets better with customer history.
The unified commerce infrastructure you built over the past five years was table stakes. The AI you deploy on top of it over the next two years will determine whether you lead or follow for the next decade.
Which side of that divide will you be on?
