Retail leaders can scale AI by choosing a few consequential business domains, assigning accountable business and technology owners, preparing data and workflows for production, and requiring measured value before expansion. A successful demo is evidence that a use case may work—not proof that it will improve retail performance at scale.
Why pilot activity is not the same as enterprise adoption
Recent surveys point to a gap between AI ambition and demonstrated, organization-wide results, but their findings describe different respondent groups and should not be treated as one industry-wide measurement.
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- In McKinsey’s April 2024 survey of 52 global Fortune 500 retail executives, 90% said their organizations had begun experimenting with generative AI (gen AI). Sixty-four percent had piloted it in internal value chains, while 26% said they were scaling there. For customer-service reinvention, 82% had piloted use cases and 36% were scaling them. Only two executives said their organizations had successfully implemented gen AI across the organization. These are self-reported results from that survey, not a census of retailers. McKinsey’s 2024 findings and recommendations provide the survey context.
- Deloitte’s 2026 retail and consumer packaged goods (CPG) executive survey found that 75% named AI a top strategic priority, while 16.5% could quantify a return. Enterprise-wide deployment was reported in the single digits—7% to 10%—for retail and CPG. Because some results combine the two sectors, they should not be read as retail-only figures unless Deloitte breaks them out. Deloitte’s survey reports the scope and findings.
The figures are not directly comparable: the surveys differ in timing, respondent populations, and measures. Together, they make a practical point for leaders: count production use and attributable business results, not pilots or strategic intent alone.
Choose a business domain before choosing a model
Start with a business area where an improved decision or workflow could materially affect an outcome. McKinsey points to domains such as customer experience, marketing, and store productivity. The right starting point is the one where the retailer can define the problem, access the relevant data, change the workflow, and measure results—not simply the use case that makes the most impressive demo.
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For each candidate, write down:
- The business outcome: for example, a change in conversion, service outcomes, margin, inventory turns, or labor productivity.
- The decision or task to improve: identify who makes or performs it today and how AI would change that work.
- The operating boundary: which locations, channels, categories, teams, or customer interactions are in scope.
- The baseline and constraints: current performance, data available, operating costs, quality requirements, and risks that need control.
Keep the initial portfolio small enough to give each candidate the attention it needs. A domain is the business area to transform; a use case is a specific intervention within it. This distinction helps avoid an assortment of unrelated pilots with no shared owner or path to adoption.
Give business leaders accountability for outcomes
A technology team can build and run a system, but it cannot by itself deliver a business result that depends on merchandising, store operations, customer service, or marketing changing how work gets done. Put a business leader with relevant operating or P&L accountability in the lead, paired with technology and data counterparts.
Agree on decision rights before build-out begins. The business owner should be accountable for the target outcome, workflow decisions, and adoption. Technology and data leaders should own the architecture, integration, reliability, and data controls within their remit. Risk, compliance, security, and legal teams should be involved where the use case requires their review. Set shared success criteria so that a technically functioning system is not mistaken for a successful business implementation.
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This operating model also makes it easier to resolve trade-offs: a workflow may need redesign, a data source may not be fit for use, or a proposed level of automation may require human review. Those decisions should have named owners rather than being left to an AI project team without authority over the process.
Build foundations around the workflow
Production readiness is a combination of data, integration, architecture, governance, and operational ownership. Improving only the model is unlikely to address a workflow that depends on fragmented systems or unreliable data.
Make data usable and traceable
Check whether the data needed for the task is accessible, sufficiently accurate, current, and documented. Clarify its meaning and ownership, including relevant metadata, and identify how the system will handle missing, inconsistent, or outdated inputs. Data quality should be assessed in the context of the decision the system supports, not treated as a general cleanup project with no link to business use.
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Plan integration and modularity
Map how the AI capability will connect to the systems and steps people already use. Determine where its output enters the workflow, what happens when the service is unavailable, and how changes can be tested without disrupting unrelated operations. Favor an architecture that allows components to be evaluated and replaced as needs evolve; avoid making a single pilot’s design an unexamined enterprise standard.
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McKinsey reports that 60% of its surveyed retail executives opted for ready-made platforms; adoption was lower in procurement (18%) and commercial areas (25%). These are survey responses, not a universal prescription. A ready-made tool may fit a common task, while a retailer may need to shape an existing model with proprietary data for a differentiated workflow. Building a foundation model from scratch is a separate, resource-intensive choice, not the default route to useful AI.
Compare options against the same criteria:
- Fit with the business outcome and workflow.
- Data readiness and integration work required.
- Total cost and continuing operational burden.
- Privacy, compliance, quality, and human-oversight needs.
- Portability and the ability to change vendors or components.
The best choice depends on the use case and the retailer’s constraints; the cited evidence does not establish a universally superior architecture.
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Redesign work and prepare people to use AI
Scaling changes the work around a system as much as it changes the software. Define which steps AI performs, which decisions remain with employees, when a person must review or override an output, and how exceptions are handled. Make the new process explicit in procedures and role expectations; otherwise, employees may ignore the system, duplicate its work, or rely on it where judgment is still required.
Plan training for both technical and nontechnical staff. Employees need to understand how to use the capability, recognize unsuitable or uncertain outputs, escalate problems, and apply the required review. Technical teams need the skills and operational practices to maintain and monitor it. Include frontline users in workflow design and rollout so that implementation accounts for the conditions in which the work actually happens.
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Skills, compliance, integration, and ownership are recurring concerns in the available retail discussion. Genpact reports that 72% of respondents cited talent availability, 51% regulatory or compliance hurdles, and 45% fragmented ownership as barriers. The page does not expose enough methodological detail to assess the sample composition and survey dates fully, so treat these as reported survey figures rather than precise estimates for the retail sector. Genpact’s article also names margin uplift, inventory turns, conversion, and labor productivity as useful value-backed measures.
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Measure value before expanding
Set the measurement plan before launch. Record a baseline, define the comparison period and population, and distinguish the effect of the AI-enabled workflow from other changes that could affect results. Track operating costs as well as benefits; a gain that depends on substantial recurring review, integration, or support may have a different business case from one that runs reliably within the operating model.
Use measures suited to the business problem, rather than a generic AI scorecard. Depending on the use case, that may include margin uplift, inventory turns, conversion, labor productivity, or service outcomes. Pair the outcome measure with quality and risk checks—for example, whether outputs meet the required standard and whether employees are escalating the right cases. The objective is not merely to show that the system is used, but to establish whether the changed process is producing the intended result at an acceptable cost and risk.
Survey results from production users can offer context, but they are not a substitute for a retailer’s own evaluation. In Google Cloud’s 2024 global survey, 57% of retail and CPG organizations already using gen AI in production reported improved customer experience. Among 177 respondents who reported productivity improvements, 48% said employee productivity had at least doubled. The latter is a self-reported survey response, not a controlled productivity measurement; the survey’s production-user sample does not represent retailers still at the pilot stage. Google Cloud’s survey page describes its findings.
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Expansion should be a decision earned through evidence, not an automatic reward for completing a pilot. Before widening deployment, ask whether the use case has cleared these checks:
- Business value: results are measured against a baseline, with operating costs included and a credible link to the target outcome.
- Operational readiness: data, integration, support, monitoring, and exception handling are adequate for the next deployment scope.
- Governance: accountable owners, risk controls, and required human review are clear and workable.
- Adoption: affected employees are prepared, the workflow is usable, and training and escalation paths are in place.
- Repeatability: the next location, channel, or team can adopt the capability without assuming that local conditions are identical.
If a check fails, fix the specific gap or narrow the scope before expanding. That may mean improving data, revising the workflow, clarifying ownership, or reconsidering the solution. Scaling responsibly is a sequence of evidence-based operating decisions—not a one-time transition from pilot to enterprise.
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