Adding another marketplace, sales channel or fulfillment partner is no longer enough to make a retailer truly omnichannel. A new global study from Anchanto and Demand Metric argues that the harder challenge is coordinating the technology and operations underneath those channels — and that gap could determine how effectively enterprises turn AI investments into measurable commerce gains.

Enterprise retailers have spent years expanding their digital commerce footprints across marketplaces, direct-to-consumer stores, social channels and increasingly complex fulfillment networks. Yet a new global study suggests that many organizations have reached a point where adding more channels may be easier than coordinating the infrastructure behind them.

Anchanto, an ecommerce operations technology company, has released The State of Omnichannel Commerce 2026/2027, a study conducted with Demand Metric that examines how brands and retailers manage systems, inventory, fulfillment, data and market-specific requirements across their commerce operations.

Its central finding is what the report calls the Omnichannel Execution Gap: the difference between how organizations perceive their omnichannel maturity and how consistently they can execute across the systems and processes connecting those channels.

The distinction matters because omnichannel commerce is increasingly less about storefront proliferation and more about operational coordination. A customer may purchase through a marketplace, a brand website or a physical store, while inventory, pricing, order management, warehouse systems and last-mile fulfillment operate elsewhere.

When those systems are poorly connected, the customer sees the consequences first — inaccurate availability, delayed fulfillment, inconsistent promotions or an uneven experience between channels.

Anchanto’s research points to disconnected systems, fragmented workflows, limited visibility and manual coordination as continuing obstacles. The findings arrive as retailers are simultaneously being encouraged to adopt artificial intelligence across merchandising, forecasting, marketing and supply-chain operations.

AI was the leading commerce transformation priority in the study, selected by 53% of respondents, compared with 46% who selected cost reduction and efficiency.

That ranking is notable, but it also exposes a dependency that enterprise technology leaders have increasingly encountered: AI cannot compensate indefinitely for fragmented operational data.

“The research shows that omnichannel maturity cannot be measured by channel presence or confidence alone,” said John Follett, Co-Founder and Head of Research at Demand Metric. “Many organizations have built extensive commerce ecosystems, but the systems, workflows, and data behind them are not yet fully coordinated.”

The most valuable AI use cases identified in the study are relatively practical rather than futuristic. Respondents highlighted promotion and pricing effectiveness, marketplace performance visibility, and inventory and fulfillment prediction.

These applications require access to reliable, timely operational data. A retailer attempting to predict inventory requirements, for example, needs more than historical sales data. It needs a connected view of inventory positions, orders, fulfillment capacity, marketplace demand and potentially regional constraints.

That makes the underlying commerce stack increasingly important.

For enterprise retailers, the architecture can span ecommerce platforms such as Shopify and Adobe Commerce, enterprise resource planning systems from SAP or Oracle, customer and marketing platforms from Salesforce, marketplace integrations, warehouse management systems and third-party logistics providers.

The challenge is not necessarily that any individual system is inadequate. It is that the systems may not share information quickly or consistently enough to support coordinated decision-making.

This is where Anchanto’s argument for “connected execution” fits into a wider shift in enterprise commerce technology.

Historically, digital transformation programs often focused on adding capabilities: another marketplace integration, a new warehouse platform, a customer data system or an AI tool. The next phase may instead focus on orchestration — ensuring that those technologies operate as parts of a coherent commerce infrastructure.

Vaibhav Dabhade, Founder and CEO of Anchanto, framed the shift around that operational layer.

“Brands and retailers have made significant progress in expanding across channels, marketplaces, and fulfillment models. But expansion alone does not create omnichannel maturity,” Dabhade said.

For retailers, the financial implications are significant. Poor coordination can create excess inventory in one location while another market experiences stockouts. Manual reconciliation can raise operating costs. Inconsistent pricing or promotions can undermine margins. And disconnected customer data can make it harder to understand the full commercial journey.

The report’s findings also have implications for technology procurement. Instead of evaluating AI, commerce, inventory and fulfillment platforms as independent investments, enterprise teams may increasingly need to assess how well those systems exchange data and trigger actions across the broader operating model.

That does not mean retailers should delay AI projects until every legacy system has been replaced. In many enterprises, that would be impractical. It does mean that AI initiatives need an explicit data and integration strategy.

The distinction is increasingly relevant as companies such as Amazon, Google, Microsoft and Salesforce embed AI deeper into enterprise workflows. Generative AI can make recommendations, generate content and interact with business data, but the quality of those outputs depends heavily on the systems and information available to the model.

For commerce organizations, the next competitive advantage may therefore come less from having the newest AI capability and more from being able to connect AI to accurate operational signals.

Anchanto’s study ultimately makes a broader argument about the definition of omnichannel maturity. Operating across multiple channels is no longer a sufficient measure. The more consequential question is whether a retailer can coordinate those channels as one commercial system.

That shift could reshape the priorities of ecommerce, supply-chain, IT and operations leaders over the next several years. The winning architecture may not be the one with the largest number of channels, but the one capable of turning those channels, systems, inventory and fulfillment networks into a coordinated operation.

Market Landscape

The global commerce stack is becoming more fragmented at the same time that retailers are under pressure to make it behave as a single system. Marketplaces, DTC storefronts, retail media, physical stores, third-party logistics and international expansion each introduce new operational dependencies.

The result is an architectural problem as much as a commerce problem.

The AI investment cycle adds another layer. Retailers are moving from experimentation toward applications such as demand forecasting, dynamic pricing, customer segmentation, supply-chain prediction and automated decision support. Yet these use cases require high-quality data and dependable integration with operational systems.

The competitive landscape consequently extends beyond ecommerce software. Platforms from Amazon, Salesforce, Adobe, Microsoft, SAP and Oracle increasingly overlap with specialized commerce, order management, inventory and fulfillment technologies.

For enterprise teams, the strategic question is shifting from How many channels do we support? to How reliably can we coordinate the channels we already operate?

That makes integration, data governance, inventory visibility and workflow orchestration increasingly important measures of commerce maturity.

Top Insights

• Anchanto and Demand Metric identify an Omnichannel Execution Gap, highlighting fragmented systems, workflows, inventory and fulfillment as enterprise commerce barriers.
• AI ranked as the leading commerce transformation priority at 53%, but retailers still need connected operational data to make AI useful at scale.
• Pricing, promotion, marketplace visibility, inventory and fulfillment prediction emerged as practical AI applications with direct implications for commerce performance.
• Enterprise retailers increasingly need coordinated commerce architectures connecting ecommerce platforms, marketplaces, ERP, inventory, fulfillment and customer data systems.
• The next stage of omnichannel maturity may depend less on channel expansion and more on operational orchestration, visibility and automated decision-making.

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