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Retail leaders can scale AI by starting with a business outcome, assigning accountable owners, rebuilding the workflow and data around the solution, and proving value against a baseline before expanding. The gap between ambition and evidence is still substantial: Deloitte’s 2026 retail and consumer packaged goods (CPG) executive survey found that 75% named AI a top strategic priority, while 16.5% said they could quantify a return. Enterprise-wide deployment remained in the single digits—7%–10% across the two sectors.
Those are survey results, not a census of retailers, and they combine retail and CPG findings. They point to a practical distinction: a convincing demo is not yet an operating capability. The path to scale is to make a use case valuable, manageable, adopted, and measurable in the real business.
What does “scaling AI” mean in retail?
Scaling is not simply putting a model in front of more users. It means making an AI-enabled process reliable enough to operate as part of the business: it has an accountable owner, connected data and systems, clear human responsibilities, controls for risk, support when something fails, and a way to measure whether the outcome justifies the cost.
Keep the technology scope clear. Retail AI includes predictive and optimization systems as well as generative AI (gen AI). A demand forecast, product recommendation system, and employee-facing gen AI assistant have different data needs, failure modes, and review requirements. A shared operating approach can help, but each use case needs controls suited to its job.
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McKinsey’s April 2024 survey of 52 global Fortune 500 retail executives illustrates the pilot-to-scale gap for gen AI: 90% said their organizations had begun experimenting, 64% had piloted gen AI in internal value chains, and 26% said they were scaling it there. The survey also found that only two executives said their organizations had successfully implemented gen AI across the organization. These are self-reported findings from that surveyed group, not estimates for all retailers. McKinsey’s survey and scale-up analysis also found customer-service reinvention was a common test area: 82% had piloted such use cases, while 36% said they were scaling them.
Which retail problem should come first?
Choose a business domain before choosing a model or vendor. Start with a costly, consequential problem that an operating team already owns, such as customer experience, marketing effectiveness, inventory performance, or store productivity. Then define a measurable change the business wants and identify where an AI-enabled decision or task could make that change possible.
Make the use case specific enough to test
“Use gen AI in stores” is not a testable objective. A better candidate names the user, the task, and the intended outcome—for example, helping store associates find approved product information faster, with a target for response quality and time saved. A merchandising use case might instead address a defined planning decision and track how recommendations affect availability or markdowns. These are examples of how to frame a test, not claims that a particular system will produce those results.
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For each candidate, write down:
- The operating problem: what is slow, costly, inconsistent, or difficult to serve today?
- The affected workflow: who makes or acts on the decision, and at what point?
- The business outcome: which result should change, and for whom?
- The constraints: what data, systems, policies, and human judgment are required?
- The baseline: how the current process performs before AI is introduced.
Prioritize the few candidates with meaningful potential value and a credible route into everyday work. Avoid choosing a use case solely because a model can produce an impressive demonstration; the question is whether the process can deliver a worthwhile outcome under real operating conditions.
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Who should own the outcome?
Give each initiative a business owner with authority over the affected process, supported by technology and data counterparts. The business owner is accountable for the result and adoption; technology leaders are accountable for a supportable implementation; data leaders help establish that the inputs are appropriate, usable, and governed. Risk, legal, security, and frontline operations should join when the use case requires their decisions—not only after the solution is built.
Set shared decision rights before development begins. Agree who can change the workflow, approve data access, set acceptable performance thresholds, pause a system, and authorize wider deployment. A technology team can deliver a working system, but it cannot substitute for the business accountability needed to change a commercial or operational process.
Deloitte’s 2026 survey reinforces why ownership needs to be explicit: it found low enterprise-wide deployment alongside a much higher share naming AI a strategic priority. These measures do not establish why any individual organization is stuck, but they make it unwise to treat executive sponsorship or a technology roadmap alone as proof of readiness. Deloitte’s retail and CPG survey discusses adoption, ownership, and investment findings.
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How should retailers choose the technical approach?
There is no single architecture that fits every retail use case. McKinsey describes three broad ways to use foundation models: adopt existing tools (“taker”), shape an existing model with proprietary data or customization (“shaper”), or build a model (“maker”). Most retailers are more likely to use ready-made tools or customize existing models than build foundation models from scratch. In its April 2024 survey, 60% of respondents said they opted for ready-made platforms; reported adoption was lower in procurement (18%) and commercial areas (25%). Those figures describe the surveyed executives’ reported choices, not a universal adoption rate.
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| Approach | What it means | Questions to test before committing |
|---|---|---|
| Taker | Use an existing product or model with limited customization. | Does it fit the task and workflow? Can the retailer apply appropriate data, access, privacy, and review controls? What ongoing service and operating costs apply? |
| Shaper | Adapt an existing model or tool with retailer-specific information, configuration, or integration. | Can the needed data be prepared and safely connected? Does customization improve the outcome enough to justify its maintenance and evaluation burden? |
| Maker | Build a model or substantial model capability in-house. | Is there a compelling requirement that off-the-shelf or adaptable options cannot meet? Can the organization sustain the specialized talent, infrastructure, governance, and ongoing model work? |
Compare options across business fit, data readiness, integration effort, total operating cost, risk and human oversight, and portability. A ready-made product may reduce initial development work but still require process change, integration, evaluation, and vendor oversight. A more customized route can provide a closer fit but may add maintenance obligations. Building from scratch should be a deliberate response to a genuine need, not a default marker of ambition. McKinsey discusses these strategic choices in its retail gen AI scale-up analysis.
What foundations need to be production-ready?
Technical readiness is more than model access. The system must work with the data, applications, permissions, and operating processes it depends on—and be maintainable when those change.
Prepare data and context
Identify which data sources the use case needs, who is responsible for them, how current and consistent they are, and what metadata or permissions make them interpretable. Address quality problems at the source where possible. For gen AI, determine which approved documents or records the system may use and how users can tell whether an answer is grounded in appropriate information. Do not assume that connecting more data automatically improves the result.
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Map the handoffs between the AI component and existing retail systems. Decide where inputs originate, where outputs go, and how an employee or downstream process can correct an error. Favor an architecture that lets teams replace or update components without rebuilding the whole workflow, where feasible. This supports changes in models and vendors while preserving the controls and business logic that matter.
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Evaluate the whole operating burden
Estimate the work required to deploy, monitor, support, secure, and update the solution—not only the cost of a pilot. Assess data access, integration, service dependencies, vendor terms, and how the organization can retrieve its information or move to another option. Treat privacy, security, compliance, and resilience as design questions for the specific use case.
McKinsey’s retail guidance and Genpact’s discussion of enterprise AI both emphasize that data quality, integration, modular architecture, and governance are connected scale requirements. Genpact’s retail AI article reports that 72% of its respondents cited talent availability, 51% regulatory or compliance hurdles, and 45% fragmented ownership as barriers. Its page does not provide enough methodological detail to fully assess the sample composition and field dates, so treat these percentages as reported barriers among its respondents, not as precise estimates for the retail sector as a whole.
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Production AI changes who does what, when, and with which information. Map the current process and design the new one explicitly. Define which tasks the system performs, which decisions remain with people, when review is mandatory, and how staff can challenge or correct an output. A human-review step is useful only when the reviewer has the time, context, and authority to act on it.
Train both technical and nontechnical teams. Users need practical guidance on suitable tasks, limits, escalation routes, and how to verify outputs. Managers need to understand how responsibilities and performance measures change. Technical and operational teams need a shared route for handling incidents, monitoring quality, and feeding recurring problems back into the system.
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Adoption should be measured as part of implementation, not assumed from access or login counts. Look for evidence that the intended users can complete the redesigned task, that the process fits their work, and that exceptions are handled safely. EY’s enterprise retail AI strategy guidance similarly frames AI as a reason to revisit how a process should be designed, rather than simply inserting a tool into an unchanged workflow.
How can leaders prove value before expanding?
Set the measurement plan before the pilot starts. Record the baseline, specify the period and population being evaluated, and decide how to distinguish AI’s contribution from other changes in demand, staffing, promotions, or process. Track costs as well as benefits, including implementation, integration, oversight, training, and ongoing operations. A measured improvement in one metric does not establish that the overall initiative pays for itself.
Use metrics that reflect the use case’s economics and operating quality. Potential measures include:
- Commercial and inventory outcomes: margin uplift, inventory turns, availability, markdowns, or conversion, where relevant to the workflow.
- Workforce and process outcomes: labor productivity, task completion time, exception rates, or rework.
- Customer and service outcomes: service quality, resolution, customer experience, or conversion, as appropriate.
- Quality and risk: accuracy, error severity, policy compliance, escalation rates, and the frequency of human corrections.
- Net value: the improvement attributable to the changed process compared with its full operating cost.
Genpact names margin uplift, inventory turns, conversion, and labor productivity as useful value-backed retail measures. Choose only measures that match the use case; do not combine unrelated outcomes into a single headline return.
External survey results can suggest what to measure, but they are not a substitute for a retailer’s own baseline. A 2024 Google Cloud global survey of 248 respondents, fielded February 23–April 5, found that 57% of retail and CPG organizations already leveraging gen AI in production reported improved customer experience. Among 177 production users reporting productivity improvements, 48% said employee productivity had at least doubled. These are self-reported responses from organizations already using gen AI in production, not controlled measurements or evidence about pilot-stage retailers. Google Cloud’s survey report provides that context.
What should the scale-up gate require?
Use a decision gate that separates an interesting experiment from an operable business capability. Expand only when the accountable owners can answer these questions with evidence:
- Value: Did the use case improve a relevant outcome against a credible baseline, after accounting for the costs of operating it?
- Reliability: Does it perform adequately across the situations it will encounter, and are known failure modes understood?
- Governance: Are access, privacy, compliance, security, review, and escalation responsibilities clear for this deployment?
- Integration: Can the solution work within the target workflow and systems without fragile manual workarounds?
- Adoption: Are intended users prepared to use it, and is there a plan to address resistance or workflow friction?
- Operations: Is there an owner, support path, monitoring approach, and budget for updates and ongoing costs?
If the answer is no, keep the initiative bounded and resolve the specific gap before broadening access. A successful pilot validates a hypothesis under its test conditions; scaling requires evidence that the business can repeat the result with appropriate controls and support.
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