How an enterprise retail AI strategy can enable confident outcomes

Retailers are investing in AI, yet few see enterprise value. Leaders are redesigning retail AI strategies by focusing on outcomes, not pilots

In brief

• Leaders capturing impact are redesigning how decisions are made by blending human expertise with enterprise AI operating models in retail.
• A value-led, enterprise approach positions retailers for agentic commerce and sustained growth.

The retail AI moment: Urgent, promising and rapidly evolving 

This agentic commerce model will change how demand is created, how influence works and how retailers compete for share of wallet. 

Opportunity rich, value poor: Why retailers aren’t seeing the AI lift

Retail should be one of the biggest beneficiaries of AI. It has the scale, first-party data, high decision velocity and operational complexity that make AI transformation a board-level mandate. The upside is real: improving demand accuracy, optimizing promotions, strengthening inventory flow, increasing store productivity and personalizing digital journeys while collapsing cost-to-serve.

Yet many retailers are still behind the curve, not because they lack tools or ambition, but because they pursue AI through disconnected pilots instead of enterprise design. With functions spanning merchandising, supply chain, stores, digital and marketing, fragmentation is easy and value becomes hard to repeat.

Across categories, the pattern is consistent: initiatives start with good intent but aren’t anchored to measurable business outcomes; pilots run in isolation; ownership and funding are unclear; success metrics vary by team; and there is no path to embed models into day-to-day workflows. Without integration into how decisions get made, even strong use cases stall. 

Retailers tend to overengineer early builds, investing in complex custom models where simpler, more practical approaches would have driven faster, more reliable value. Meanwhile, foundational needs such as data quality, inventory visibility and governance remain uneven. AI becomes an overlay rather than influencing how the business actually works.

The result is predictable: activity without compounding value. The retailers that pull ahead will treat AI as an enterprise capability designed into processes, decisions and operating models. 

The mindset shift: A foundation for AI success

Retailers seeing real impact share a fundamentally different mindset: they recognize that AI cannot deliver meaningful value inside processes, decision cycles and operating structures designed for a manual world. AI becomes transformative only when the organization is willing to rethink how work gets done across merchandising, supply chain, stores and digital and redesign processes for a model where human expertise and AI intelligence operate together.

Retailers who consistently capture value translate this mindset into a set of seven nonnegotiable and foundational practices that guide how the enterprise applies, governs and scales.

This forms the core ethos of retailers who succeed with AI to transform their way of working that aligns ambition, process, data, people and governance, so intelligence can scale and create compounding enterprise value.

Process reimagination: The engine of AI impact

AI removes many of those constraints and reshapes the underlying AI operating model.

Reimagining these processes starts by asking a simple question:

If AI had existed when we designed this, what would we have built?

That single shift reframes:

• How merchants plan assortments
• How promotions are targeted
• How supply chains flow
• How store tasks are prioritized
• How digital journeys adapt in real time

It elevates the entire operation: decisions move faster, inventory flows smarter and customer experiences become more adaptive and relevant.

A value-led approach: Designing an AI-enabled processes

Once retailers adopt an AI-first perspective on process design, the question becomes how to activate it. A structured, value-led methodology keeps the work grounded and confirms AI is deployed where it matters most.

It begins with clarity on outcomes, defining the business results the process must achieve and working backward from there. Future-state workflows are designed with AI-native capabilities in mind. Roles are redesigned to reflect a world where humans and agents collaborate. And a roadmap is built that aligns data, technology, skills and change management to support activation. 

Activating the strategy: How retailers scale AI across operating models

Scaling AI in retail requires a disciplined, repeatable framework:

• Identify opportunities across customer, employee, operational and innovation domains.
• Validate hypotheses using proven accelerators and diagnostic tools.
• Evaluate each opportunity based on value, feasibility, data and operating model implications. 
• Sort into big bets, quick wins and longer-term redesign pathways. 
• Prioritize the top opportunities that deliver near-term results and build momentum.

This structured approach helps leaders champion AI and agentic decisioning across core retail functions.

The way forward: Where retailers need to go from here 

The retailers that pull ahead will be those who ground their AI agenda in clear value and outcomes, redesign core processes for an AI-first world and build the operating model to scale intelligent decision-making across merchandising, supply chain, stores and digital. The shift toward agent-driven commerce will only accelerate this need.

Those prepared to capture value today and aligned on how their organization will operate in an AI-enabled environment will be best positioned for the transformation already taking shape across the industry.