Top 10 AI Prompts and Use Cases and in the Retail Industry in Modesto

AI is already reshaping Modesto retail: Modesto‑based The Save Mart Cos. - which operates more than 200 stores - is using SymphonyAI to localize assortments and personalize promotions, while pilots of Afresh's Fresh Operating System and inventory‑scanning robots have targeted produce freshness, waste reduction and on‑shelf availability.

Local pilots show tangible impact - Afresh reports stores cutting food waste ~25% and out‑of‑stocks ~80%, with modest sales uplifts - meaning smaller Modesto grocers can lower shrink, keep locally grown produce fresher, and redeploy staff to higher‑value service tasks.

For retailers ready to move from pilots to day‑to‑day gains, practical skills in tooling and prompt design matter: see Save Mart's AI rollout details at Progressive Grocer, the Afresh produce pilot at Supermarket News, and Nucamp's AI Essentials for Work bootcamp to train managers and staff in applying these technologies.

Bootcamp	Length	Early bird cost	Courses included
AI Essentials for Work	15 Weeks	$3,582	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Syllabus / Registration	AI Essentials for Work syllabus and registration - Nucamp AI Essentials for Work bootcamp

“By working with SymphonyAI Retail CPG's AI capabilities, we have access to leading retail technologies that span multiple functions to create a customer-first connected view,” said Mark Van Buskirk.

Selection prioritized prompts and use cases that combine proven business impact, broad adoption, and practicality for California-sized operations: first, measurable revenue or cost benefits (so prompts tied to forecasting, personalization, or loss prevention scored highest); second, adoption and scalability evidence from enterprise surveys; and third, low-barrier pilots for in-store rollouts that Modesto merchants can run with existing cameras and POS data.

Weighting leaned on NVIDIA's State of AI in Retail and CPG survey showing widespread AI adoption and clear ROI signals and on industry analysis reporting revenue and cost gains from early adopters - criteria that favored demand-forecasting, intelligent-shelf alerts, and generative-assistant prompts that drive personalized offers and faster restocking.

The practical test was feasibility: could a small chain or independent grocer stand up a pilot in 60–90 days and expect measurable results? That matters because NVIDIA's smart‑store findings show empty shelves can translate into a 46% loss of potential sales, making inventory and shelf-monitoring prompts a top priority for Modesto retailers.

Criterion	How applied	Evidence
Business impact	Prioritize prompts tied to revenue/cost metrics	ZK Research summary on revenue & cost gains
Industry adoption	Favor widely used, supported use cases	NVIDIA State of AI in Retail and CPG survey - 2025 industry adoption and ROI findings
Pilot feasibility	Quick deploy with existing store data/cameras	NVIDIA smart‑store stockout and shelf-monitoring guidance

“To ensure we can offer personalized recommendations at scale, we leverage machine learning for high-quality recommendations throughout the customer journey.” - Rick Bruins, Machine Learning Engineer, ASOS

Movable Ink's Da Vinci turns a single email send into millions (even “literally billions”) of individually tailored messages by using AI to match each recipient with the creative, subject line, and send time most likely to convert - an approach Victoria's Secret used to streamline workflow, save production time, and drive higher click‑through and conversion rates and revenue lifts; for Modesto retailers that means existing holiday or clearance creatives can be automatically repurposed and targeted to neighborhood segments without extra design cycles, delivering personalized offers when local shoppers are most likely to act.

Learn how Da Vinci powers these on‑brand, data‑driven experiences on the Movable Ink Da Vinci overview and read the Victoria's Secret personalized marketing case study to see the real‑world benefits.

Movable Ink Da Vinci overview Victoria's Secret personalized marketing case study

"Do you know what Victoria's Secret really is for their personalised, 1:1 marketing? We'll give you a hint: it starts with A and ends with I."

Salesforce Agentforce brings autonomous AI agents into the daily operations of California retailers - from Modesto independents to regional chains - by automating sales outreach, key‑account tasks, retail execution (visit scheduling, pre/post‑visit briefs) and 24/7 customer service so in‑store teams can focus on merchandising and personalized shopper interactions; implementations cite measurable service gains (Agentforce can speed case resolution and improve omnichannel response), and Salesforce's footprint is large - 76 of the Top 2000 North American online retailers run Salesforce platforms, representing more than $136.116 billion in web sales - showing this is enterprise‑grade tech local merchants can lean on.

Low‑code builders let managers tune agents for local assortments, promotions and store schedules, while service use cases (smart case routing, knowledge surfacing, telesales handoffs) have reduced resolution times and transfer rates in early deployments.

For playbooks and local hiring impacts, see detailed reporting on how Salesforce deploys Agentforce for consumer goods and Service Cloud use cases, and review local notes on chatbots handling high‑volume queries for Modesto stores.

“This is not at all a replacement of a merchant or a personal shopper… It's giving them superpowers, enabling them to do their jobs much better.” - Kelly Thacker

Inventory and demand forecasting at scale is no longer theoretical - Walmart's playbook shows how Modesto retailers can cut shrink and keep shelves stocked by combining granular data with ML: Walmart's 2021 deployment used GBMs and RNNs to forecast up to eight weeks ahead with ~85% accuracy, trimming excess inventory ~10% and improving on‑shelf availability ~15%, saving an estimated $1 billion in holding costs in 12 months; the same approach drives ZIP‑code‑level allocation and last‑mile routing in Walmart's AI systems that touch thousands of nodes across its network (Walmart retail machine learning case studies, Walmart AI-powered inventory system overview).

For Modesto grocers, the so‑what is concrete: forecasting that factors local promos, weather and holidays can turn a seasonal overstock into cash and a stockout into a lost loyal customer - provided models are monitored and segmented so slices (by store, SKU, or ZIP) don't silently underperform (demand forecasting production guidance and best practices).

Metric	Walmart-reported result
Forecast horizon / accuracy	Up to 8 weeks / ~85%
Excess inventory	Reduced ~10%
Shelf availability	Improved ~15%
Estimated holding cost savings	~$1 billion (12 months)

“AI is ‘always on' and ready to distribute, supply and deliver”

Dynamic pricing - used by major chains from Target and Best Buy to Walmart and Kroger - lets retailers shift prices in real time based on demand, location, inventory and competitor moves, and California merchants should treat it as both an opportunity and a compliance risk: consumer guides note Target, Walmart and Best Buy among adopters and warn shoppers to watch app‑based, location‑sensitive price changes (Taste of Home article on dynamic pricing and consumer guidance), while legal analysis flags the exact California risk - if a price rises between shelf display and checkout it could trigger claims under Business & Professions Code sections 17200 and 17500 and create weights‑and‑measures issues as stores roll out electronic shelf tags (Walmart has piloted digital tags in roughly 500 stores) (Hartman King analysis of AI-powered dynamic pricing and consumer protection concerns).

For Modesto independents the so‑what is practical: use clear price‑match rules, log and sync digital‑label updates, and test location or app rules before full rollout to avoid surprising shoppers and regulatory scrutiny - retailer transparency and operation checks matter as much as the algorithm.

For the high‑level industry debate and Target's own pricing examples, review reporting on in‑store vs. app pricing tests (RetailWire discussion of Target's dynamic pricing strategy and customer trust).

“Somebody at Target programmed in an algorithm which says someone who is 50 feet within the store is willing to pay more. ... That's why the price went up when you got closer to the store.” - George John, University of Minnesota

Visual search and image‑recognition-driven try‑ons are practical tools for California retailers: ZERO10's AR Mirror and Web Widget use 3D body tracking, pose and depth estimation, and cloth simulation to let shoppers in a Modesto storefront virtually try items, capture shareable photos, and narrow choices before asking staff for physical sizes - an experience that particularly resonates with younger, tech‑native customers and can lower costly returns; vendors report AR activations can lift in‑store engagement (ZERO10 and partners cite a 27% in‑store uplift and up to 4.37× higher engagement for AR storefronts) and AR‑assisted shopping has been tied to return‑rate reductions (reports show reductions up to ~28%).

ZERO10's software‑first approach lets small chains repurpose existing screens and integrate virtual try‑on near real product displays to drive conversion, making this a measurable, low‑risk pilot for Modesto merchants looking to turn window browsers into buyers - see ZERO10's product overview and industry analysis of AR try‑on trends for implementation pointers.

Metric	Reported uplift / source
AR Storefront vs. traditional window	~4.37× higher engagement - Business of Fashion / ZERO10 projects
In‑store AR Mirror engagement	~27% increase - Retail Insight interview with ZERO10
Return rate reduction (virtual try‑on / body scanning)	Up to ~28% lower returns - FashionIndex industry analysis

“Augmented reality is the only way to wear digital clothes, adding utility to them and bringing a similar use to them as with physical pieces but without actually producing them. It's a game‑changer to the future of fashion.” - George Yashin

Amazon's Just Walk Out combines computer vision, sensor fusion and optional RFID to let shoppers grab items and leave while the system automates payment and analytics - making it a strong fit for California small‑format stores, stadium concessions and pop‑up retail where speed matters; the official Amazon Just Walk Out overview explains how camera and sensor tracking plus virtual carts remove checkout friction (Amazon Just Walk Out overview - frictionless retail technology), while Amazon's RFID lanes roll‑out shows pilots delivering checkout speeds up to four times faster, a ~40% reduction in labor needs, and 96% faster cycle counts - results that translate to handling Modesto's event‑day crowds or campus rushes with fewer staff (Amazon RFID lanes pilot results and performance metrics).

Balance is required: recent industry reporting notes Amazon scaled back full-store Just Walk Out in larger Fresh grocers in favor of hybrid smart carts amid cost and customer‑preference concerns, so Modesto retailers should pilot RFID lanes or kiosk hybrids first to validate uplift before a full store conversion (Analysis of Amazon Just Walk Out strategic pullback and customer feedback).

Pilot metric	Reported result
Checkout speed vs. POS	Up to 4× faster
Labor required	~40% reduction
Time to complete cycle counts	Reduced 96%

“The technology has drastically improved the fan experience by eliminating checkout lines. I'm also looking forward to implementing the RFID tagging across our venue for the inventory management capabilities before the next season. That's just as big, if not a bigger opportunity, than speeding up checkout.” - Lauren Gurley, Senior Manager of Retail Operations, Miami Dolphins, Hard Rock Stadium & Formula 1 Crypto.com Miami Grand Prix

Master of Code Global's ShopJedAI combines a Shopify LLM assistant, a conversational shopping assistant and an “ABC” ad tool to automate high‑conversion marketing for small and mid‑sized retailers - built on embeddings and a vector database and reporting ~86% answer accuracy - so Modesto merchants can generate SEO‑ready product descriptions, run localized ad variations, and serve 1:1 shopper help without hiring an agency; the tangible payoff is fewer creative cycles and faster, measurable campaign launches that free staff for in‑store service.

For budget planning, generative‑AI marketing SaaS sits in a range many SMBs can test (Aeologic cites roughly $500–$5,000/month for marketing SaaS tiers), making ShopJedAI a practical pilot on existing Shopify stores.

Read ShopJedAI's real‑world use cases and the broader generative‑AI marketing context to plan a 60–90 day pilot tailored to Modesto ZIP‑level offers. ShopJedAI generative AI chatbot for eCommerce - Master of Code Global Generative AI use cases and pricing in retail - Aeologic

Computer vision systems like Amazon Fresh/Go combine ceiling and cart cameras, shelf sensors and sensor fusion with AI to track who picked what and deliver an automated receipt within about 30 minutes - technology designed both to prevent shrink and to surface real‑time shelf data that speeds restocking and optimizes product placement; the stack (cameras, sensors, computer vision, ML) also enables substitute recommendations and promotional targeting from the same event stream, so a Modesto grocer can pilot intelligent‑shelf alerts on existing cameras and see immediate benefits in fewer out‑of‑stocks and faster audit cycles (Amazon Fresh AI shelf monitoring case study).

The model isn't perfect - accuracy is critical in crowded stores and the system's design choices raise practical loss‑prevention tradeoffs - so review loss‑prevention reporting and app‑sign‑in flows before full rollout (Amazon Go loss‑prevention analysis).

Local pilots in Modesto that pair camera-based shelf alerts with POS reconciliation and smart‑shelf sensors can streamline restocking and reduce shrink without a complete store rebuild (Modesto smart‑shelf and sensor technology case study).

“Accidental shoplifting happens so rarely that we didn't even bother building in a feature for customers to tell us it happened.” - Gianna Peurini, Amazon Go VP

Mercari's Merchat AI brings conversational commerce to secondhand shopping by using ChatGPT to search Mercari's millions of listings and surface real‑time item recommendations based on a shopper's natural language prompts - helpful for Californians looking for niche gifts or budget finds without combing pages of search results.

The assistant (branded Merchat AI) supports follow‑up questions and context, and Mercari has extended that capability via a ChatGPT plugin so users can ask about items by use, features or price range in plain English; these tools sit behind a marketplace with broad reach - more than 50 million U.S. downloads and roughly 350,000 new daily listings - so Modesto retailers and shoppers can both discover and source unique inventory faster.

Learn more from Mercari's Merchat AI personal shopper announcement (Mercari Merchat AI personal shopper announcement), the Mercari ChatGPT plugin press release (Mercari ChatGPT plugin press release), and coverage of Mercari AI ad targeting and performance metrics (Mercari AI ad targeting and metrics coverage).

Metric	Value / feature
U.S. downloads	More than 50 million
New listings per day	~350,000
Key AI features	ChatGPT‑based Merchat AI assistant; ChatGPT plugin for natural‑language item search

“We are excited to partner with Rokt to enhance our customers' ecommerce shopping experience. By leveraging Rokt's technology, we will be able to offer our users even more relevant recommendations and create a more personalized shopping journey. This partnership reinforces our commitment to providing the best possible experience for our customers.”

Walmart's pilot shows that AI vendor‑negotiation chatbots can turn neglected “tail‑end” contracts into cash flow and time: a Pactum‑built bot closed agreements with roughly 64–68% of suppliers (well above the 20% baseline), cut average negotiation time to about 11 days, and delivered measurable savings - about 1.5% in the pilot and ~3% in later rolls - while negotiating extensions of payment terms by roughly 35 days, outcomes that matter to California independents where every percentage point of margin and extra days of payables improve local cash management; Modesto retailers can start small (Pactum clients often begin with a simple Excel upload and see results in under two weeks) and use a bot to handle routine, low‑value negotiations so buyers focus on strategic suppliers (HBR article on Walmart automating supplier negotiations, Pactum negotiation agent commercial terms page).

Metric	Result
Agreement rate	64–68% of suppliers
Average negotiation turnaround	~11 days
Average savings	~1.5% (pilot) → ~3% (expanded)
Payment‑term extension	~35 days

“Instead of replacing human employees, we are eliminating the mundane, low‑input aspects of a procurement executive's job, freeing up time for strategic negotiations.” - Martin Rand, Pactum CEO

Modesto retailers should convert lessons from pilots into a disciplined, low‑risk rollout: pick one measurable goal (reduce shrink or cut out‑of‑stocks), run a 60–90 day pilot using existing cameras and POS data, and require clear metrics and governance before scaling - small pilots already show real impact (local Afresh pilots cut food waste ~25% and out‑of‑stocks ~80%).

Use implementation playbooks like Euristiq's AI in Retail guide to match software to use case and heed fairness and cost warnings in industry analysis (see Innovating with AI on discrimination and infrastructure costs) so personalization and dynamic pricing don't create regulatory or community harm.

Train store managers and ops staff on prompts, vendor selection and monitoring (Nucamp's AI Essentials for Work syllabus and course details and Nucamp AI Essentials for Work registration is built for that), start with narrow pilots tied to daily KPIs, and scale only after proving ROI and governance tools are working in California's market and regulatory environment.

Bootcamp	Length	Early bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work syllabus and course details

“There's a cost to the infrastructure… margins aren't that great to begin with, so it's a huge capital investment.”