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Retailers are moving from isolated experiments to connected, accountable systems. A group of 2023–2024 industry studies points to five linked shifts: generative AI is entering daily workflows, practical customer and commerce uses are taking priority, personalization is narrowing from “mass to micro,” loyalty and physical stores are regaining strategic weight, and contextual commerce is exposing the need for better integration and consent.
1. Generative AI is moving into operating workflows
Google Cloud’s survey of 274 US retail decision makers, commissioned from NewtonX and fielded October 23–November 14, 2023, found that 81% felt an urgent need to adopt generative AI and 72% felt ready to deploy it in 2024. The respondents worked for organizations with at least 500 employees. The release contrasts 2023 experimentation with expected production use in 2024.
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Amy Eschliman, Google Cloud’s managing director for Retail Industry Solutions, said generative AI had “come so far in one year,” moving from a barely known concept to an urgently needed answer to business challenges. The figures describe executive sentiment and readiness, not proof that every retailer has achieved a safe or profitable rollout.
2. Retailers are choosing revenue-adjacent use cases
The same survey shows where adoption is concentrating. These applications sit close to customer interactions, content production, selling and frontline productivity rather than being purely experimental demonstrations.
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| Use case | Share of surveyed retail decision makers | Additional deployment detail |
|---|---|---|
| Customer-service automation | 59% | 25% were piloting and 22% were deploying it, making it the leading deployment path |
| Marketing support and product-description generation | 49% | Not separately stated |
| Creative assistance | 44% | Not separately stated |
| Conversational commerce | 40% | Not separately stated |
| Store-associate knowledge and support | 38% | Not separately stated |
For retailers, the operational question is no longer whether a model can produce a striking demo. It is whether the system can retrieve current product, policy and inventory information; hand off uncertain cases to employees; and leave an auditable record of what customers were told.
3. Personalization is shifting “from mass to micro”
Deloitte describes an industry moving “from mass to micro”: away from one broad message and toward experiences tailored to specific consumer groups or situations. Half of the retail executives in its 2024 US outlook prioritized AI-driven personalized recommendations.
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The opportunity is relevance, but the constraint is trust. Only five in ten retail executives said they were confident in their ability to use AI effectively. In Deloitte’s holiday consumer study, eight in ten consumers reported little to no trust in retailers’ responsible use of AI. Personalization therefore has to be judged on two axes: whether it improves discovery or service, and whether shoppers understand the data use, can control it and receive reliable results.
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4. Loyalty, digital commerce and stores are growth priorities
Deloitte’s 2024 executive survey ranks customer-retention and experience investments ahead of a narrow focus on new technology. The reported growth opportunities are:
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| Opportunity | Share of retail executives selecting it |
|---|---|
| Strengthening loyalty programs | 54% |
| Strengthening digital-commerce offerings | 44% |
| Enhancing the in-store customer experience | 36% |
| Enhancing the omnichannel experience | 32% |
The ordering matters. Loyalty programs are being treated as an economic and data asset, while stores remain a place to improve service rather than merely a fulfillment cost. Digital and omnichannel efforts depend on continuity: a customer’s preferences, offers, service history and inventory context need to carry across app, website, contact center and store without forcing the shopper to start again.
5. Contextual commerce is exposing integration and consent gaps
RRD defines contextual commerce as personalized, location-based, platform-integrated shopping that responds to a customer’s activity. A September 2023 industry survey of 536 respondents, including 102 retail respondents, identifies the barriers that make this model difficult to operate:
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- High system-maintenance costs: 44%
- Integration complexities: 35%
- Slower market adaptability: 35%
- Demand for integrated solutions: 33%
Those results suggest that relevant offers require more than a recommendation engine. Product, customer, location, consent, pricing and fulfillment data must connect quickly enough to support a useful interaction, and the underlying systems must remain maintainable as channels change.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Privacy concerns can limit the value of location and in-store signals. In RRD’s August 2023 consumer study of 1,809 US adults, 33% said they were comfortable with in-store video cameras and 29% were indifferent. Many baby boomers and financially stressed consumers expressed pronounced discomfort. A convenient experience can therefore become a liability if shoppers do not know what is collected, why it is collected or how to decline it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the wider retail outlook fits
Coresight Research’s January 2024 US Retail 2024 report organizes its outlook around five forces and five trends. The paid, 10-page report includes proprietary US consumer surveys on inflation, retail theft and sustainability, along with estimates covering fashion resale, retail-media advertising and the Federal Reserve rate. These topics place the technology shifts in a harder operating environment: retailers are balancing household budgets, loss prevention, new advertising economics, interest-rate pressure and environmental expectations at the same time.
Coresight’s material is an outlook and data product, not a causal test of why sales move. Deloitte’s figures likewise represent executive expectations rather than a controlled evaluation, while the Google Cloud and RRD results are publisher-commissioned surveys. Their value is directional: taken together, they show where investment attention is converging and where execution risk remains.
What retailers should take from the five shifts
- Start with a measurable workflow. Choose a service, content or associate task with a defined baseline for speed, accuracy, cost or conversion.
- Connect authoritative data first. Give AI access to current catalog, policy, inventory and customer-service sources, with permissions that prevent inappropriate exposure.
- Build human escalation into the design. Customer-service automation and conversational commerce need clear handoffs when the model is uncertain or a transaction is sensitive.
- Make personalization legible. Explain why a recommendation or offer appears, provide meaningful controls and test whether the result is actually more useful for the intended group.
- Unify the journey across channels. Loyalty, store and digital teams should share identity and consent rules so that an interaction started online can be completed in a store or service channel.
- Measure trust as an outcome. Monitor complaints, opt-outs, correction requests and demographic differences alongside revenue and productivity metrics.
What is established—and what is not
The studies consistently document urgency, priorities and perceived obstacles, but they do not establish that generative AI causes higher retail revenue, that every loyalty investment pays back, or that contextual targeting is accepted by a majority of shoppers. Results are US-focused, use different samples and dates, and in Google Cloud’s release the 274-person US figures are distinct from a separate group of 104 UK respondents. Retailers should use the findings to set questions and controls, then validate outcomes in their own channels and customer segments.
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