Retailers are applying AI to decisions about demand, inventory and warehouse operations, as well as to personalization, product discovery and customer service. The aim is to make operations more responsive and shopping more relevant, but adoption is uneven: surveys report widespread experimentation alongside gaps in strategy, data readiness and governance. Open-source models appeal to some retailers because they offer more control and less dependence on one vendor, but they still require security, integration and oversight.
Retail AI adoption is broad, but a pilot is not the same as a scaled system
Two 2025 surveys point to strong interest. The National Retail Federation’s Center for Digital Risk & Innovation surveyed 56 U.S. retail AI leaders in summer 2025 about investment, use cases, challenges and expected value. NVIDIA’s 2025 survey described nine in ten retail and consumer packaged goods organizations as adopting or piloting AI. Those figures describe different survey groups and combine adoption with pilots; they do not establish that nine in ten retailers have AI embedded across day-to-day operations.
For shoppers, that distinction matters. A retailer may test an AI tool in one department without using it to make every inventory or service decision. The business case depends on whether a system improves a measurable outcome—such as fewer stockouts, more accurate forecasts or faster service—without creating new problems with data, customer trust or accountability.
How AI can streamline retail supply chains
Demand forecasting and inventory decisions
Retailers can use AI to analyze information relevant to demand and inventory planning, helping teams decide what to stock and where. Better forecasts may help reduce mismatches between supply and demand. The practical value depends on the quality and timeliness of the underlying data, as well as how well recommendations fit existing planning and replenishment processes.
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Process optimization
AI can help identify opportunities to improve supply-chain processes, including where work is delayed or resources are not being used effectively. Gartner reported in 2024 that top-performing supply-chain organizations used AI to optimize processes at more than twice the rate of low-performing peers. That comparison is an association between reported practices and performance, not proof that AI alone caused the performance gap.
Warehouse automation and physical AI
In warehouses, AI can support automation by helping systems interpret information and coordinate operational tasks. Physical AI extends the idea to machines that interact with the real world. The potential benefit is more responsive operations, but deployment also brings practical requirements: equipment must work safely, connect with existing systems and be supported by staff who can intervene when conditions fall outside the system’s assumptions.
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The strategy gap
Interest does not guarantee readiness. In a 2025 survey, Gartner found that only 23% of surveyed supply-chain organizations had a formal AI strategy. Without one, individual pilots can proliferate without a clear owner, shared standards or a way to compare results. That makes it harder to decide which tools deserve investment and how to manage their operational risks.
How AI can change the customer experience
Personalization and product discovery
AI can help retailers tailor recommendations, organize product discovery and make shopping assistants more useful. NRF’s 2025 findings identified IT application development (50%) and customer personalization (48%) as the areas with the strongest reported returns. These are reported return findings from NRF’s survey, not a guarantee that a particular personalization tool will produce the same result for a retailer or customer.
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Unified commerce: connecting channels
Unified commerce aims to connect a retailer’s customer-facing channels and operational information, so product, price and availability details are more consistent across interactions. Salesforce reported in 2025 that 88% of retailers surveyed said unified commerce would significantly affect their goals. For shoppers, the intended benefit is a less fragmented experience when moving between online shopping, apps and stores; whether that happens depends on the retailer’s data and systems actually being connected.
Shopping assistants and agentic service
Shopping assistants can help customers compare products, prices or availability and find relevant information. More autonomous AI agents may be designed to handle service tasks rather than only answer questions. Salesforce reported in 2025 that 75% of retailers surveyed expected AI agents to be essential by 2026. That is a forecast of retailer expectations, not evidence that agents are already widely deployed or reliably resolve customer issues without human help.
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Retailers considering these tools need to decide which interactions can be automated, when a customer should be handed to a person, and how errors will be corrected. For shoppers, a useful assistant should make it easy to verify product details and reach human support when an answer is incomplete or consequential.
Why open-source AI is attracting retailer interest
Open-source approaches can give retailers more choice in how they use models with proprietary business data, reduce reliance on a single vendor and let them benefit from community innovation. NVIDIA summarized this appeal in 2026: “Open source flips that script, allowing retailers to leverage their proprietary data, avoid vendor lock-in and benefit from open-source community innovation.”
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McKinsey’s January 2025 analysis identified Meta Llama and Google Gemma among the most commonly used enterprise open-source AI tools. It also reported that 81% of developers highly valued open-source AI experience. These indicators show growing interest, but they do not mean every retailer is using open models in production or that open source is automatically cheaper, safer or easier to operate.
What “open source” does—and does not—settle
Open models may give a retailer more control over deployment and customization, but that control comes with responsibility. Teams still need to evaluate model behavior, secure data and infrastructure, manage updates, and establish governance. Integration with sales, inventory and customer-service systems can be difficult regardless of model licensing. The label alone does not establish a model’s suitability, security or total cost for a specific use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How retailers should compare AI options
A retailer evaluating a hosted commercial model, an open model or a conventional non-AI system should compare them against the same business outcome. A lower apparent model cost is not useful if integration, oversight or error handling make the overall solution more expensive.
| Decision factor | What to ask |
|---|---|
| Business outcome | Which specific operational or customer problem should improve, and what baseline will show the difference? |
| Data readiness | Are the relevant product, inventory and customer records accurate, current and usable for this purpose? |
| Integration effort | How will the tool connect with existing planning, commerce, warehouse or service systems, and who will maintain that connection? |
| Explainability | Can staff understand enough about a recommendation or decision to check it, explain it and correct a mistake? |
| Deployment control | Does the retailer need to control where the system runs, which data it uses and how it is updated? |
| Vendor dependence | What would it take to change models or providers, and can the retailer move its data and workflows? |
| Total cost and time to value | What are the costs of implementation, computing, support, evaluation and human review, and how soon can the retailer measure a meaningful result? |
| Governance and trust | Who is accountable for errors, what safeguards apply to customer information, and when is human review required? |
A practical path from pilot to rollout
- Choose one measurable use case. Define a problem such as forecast accuracy, inventory availability or the time needed to resolve a specific kind of customer query. Set a baseline and a success measure before selecting a tool.
- Check data and controls. Confirm that the data is accurate enough for the task, that access is appropriately limited, and that staff can review and correct important outputs. Set a clear owner for the system and its outcomes.
- Run a bounded pilot. Limit the initial deployment to a defined workflow, team or customer interaction. Track the chosen business measure alongside errors, staff workload and customer impact.
- Scale only on evidence. Expand the system when results improve the target measure without unacceptable problems in reliability, integration, customer trust or operating cost. If they do not, revise the use case or stop the rollout.
What this means for shoppers
AI may help retailers keep products available, make online discovery more relevant or answer routine service questions more quickly. Those are intended benefits, not guaranteed outcomes, and the cited surveys measure retailer or developer views rather than shoppers’ experience. Customers should still check important product, price and availability details and use human support when an automated answer is unclear.
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Personalization and AI service also make a retailer’s handling of customer information important. A convenient recommendation is not a reason to assume that every data use is necessary or transparent. Retailers need clear governance and safeguards if they want efficiency gains without undermining customer trust.
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