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

AI is already reshaping retail in Uruguay: pilots like Grupo Éxito's employee-free store are streamlining checkout and operations, while a national push on capacity‑building and ethics is nudging the public and private sectors to adopt AI at scale (Grupo Éxito employee-free store pilot and checkout automation, Uruguay AI readiness and national AI strategy analysis).

Ranked third in the Latin American AI Index, Uruguay's reliable infrastructure and talent pool mean retailers can deploy AI for staffing optimization, loss prevention, and localized customer experiences without starting from scratch; picture a Montevideo shop that matches staff to footfall patterns hour-by-hour, cutting costs and improving service.

For retail leaders and managers who need hands-on skills to turn ideas into results, practical programs such as Nucamp's 15‑week AI Essentials for Work teach prompt writing and business use cases so teams can move from pilot to profit quickly (AI Essentials for Work registration).

Attribute	Information
Course	AI Essentials for Work
Length	15 Weeks
What you learn	Use AI tools, write effective prompts, apply AI across business functions
Cost (early bird)	$3,582
Syllabus / Register	AI Essentials for Work syllabus

"Nearly 80% of companies in Latin America are already working with AI, and 17.5% plan to start doing so in 2024, according to a September 2023 NTT Data and MIT Technology Review survey."

Methodology - How we picked these top 10 prompts and use cases: selections started with local reality - prioritizing ideas that fit Uruguay's national push on capacity‑building and AI ethics as described by Oxford Insights - then filtered for concrete pilot evidence and skills pipelines so suggestions can be adopted by teams on the ground.

Projects and trainings like the MIT‑MISTI collaboration in Montevideo helped surface practical, fairness‑aware solutions and informed the shortlist of use cases that reduce bias while improving impact (Oxford Insights: Uruguay AI readiness and ethics spotlight, MIT MISTI collaboration on AI for social policy in Uruguay).

Each prompt was then evaluated using a business‑value / technical‑feasibility lens and governance checkpoints inspired by established frameworks to rank and stage pilots for fast ROI and safe scale (data needs, ownership, human‑in‑the‑loop and compliance), drawing on use‑case management principles.

Practicality was non‑negotiable: examples had to be testable in stores from Montevideo to smaller towns - think optimized staff scheduling that matches team levels to footfall hour‑by‑hour - and sensitive to privacy and loss‑prevention constraints so retailers can move from experiment to repeatable operations.

“Our program in Uruguay was designed to empower students to use new AI technologies to address local challenges.”

Bilingual customer support in Uruguay should feel local - not like a translated script - and AI can help make that happen by mirroring the country's everyday Spanish and the pockets of English service that tourists and expat shoppers expect.

Training models on local-language resources and immersion-focused curricula - for example UTEC's Spanish everywhere program, which emphasizes practical language use across Montevideo's museums, parks and mate workshops (UTEC Learn Spanish in Uruguay program) - or the communicative, immersion approach at Academia Uruguay helps ensure replies sound natural in es‑uy and handle cultural references such as candombe or local dining norms (Academia Uruguay Spanish school and immersion programs).

Global contact‑centre reviews also underline the multilingual gap many operations face and the importance of measuring SLAs and language coverage when scaling support (UNGM contact centre multilingual assessment report).

The payoff is immediate: faster resolutions, fewer handoffs, and customer interactions that read like a local helped them - not a generic bot.

Personalized product recommendations are a practical, revenue-ready AI play for Uruguay's retailers: by using browsing signals, purchase history and product metadata, AI makes each homepage, product page or email feel like a tailored store aisle - driving repeat business (AI-driven personalized shopping experiences can boost repeat purchases by about 44% worldwide) and lifting conversion and average order value when done well (Insider report: AI-driven personalized shopping experiences).

Modern systems combine collaborative, content-based and hybrid models so a Montevideo shopper's session immediately surfaces relevant complements and alternatives, while smaller-city customers see local-fit suggestions that respect stock and seasonal demand; these approaches mirror how large platforms are using generative models to edit titles and surface features most important to each user (Amazon generative AI product search results and descriptions) and can be implemented incrementally alongside staffing and loss-prevention pilots common in Uruguay's retail landscape (AI in Uruguayan retail: cost savings and efficiency).

The payoff is tangible: smarter cross‑sells, fewer abandoned carts, and product discovery that feels personal rather than intrusive.

Metric	Impact / Source
Repeat purchases	~44% increase (Insider)
Purchases from recommendations	~35% (Amazon example)
Conversion / Revenue / Retention	20% conversions, 50% higher revenue, 30% retention (VisionX)

“If the primary LLM generates a product description that is too generic or fails to highlight key features unique to a specific customer, the evaluator LLM will flag the issue,” said Mihir Bhanot, director of personalization, Amazon.

Localized marketing copy in Uruguay succeeds when it's more than translation: it's transcreation plus channel-savvy execution. Local teams or vendors - whether a boutique agency in Montevideo or a specialist like Marketing Uruguay marketing agency in Montevideo - shape tone, slang and offers so messages feel like a neighborly invite, not generic sales spam; with roughly 193 marketing agencies in Montevideo, retailers can tap local talent to craft regionally resonant campaigns.

At the same time, channel constraints matter: Uruguay's telecom rules mean SMS is limited (no two‑way SMS, short codes not supported, and MMS is converted to SMS with a link), so plan multichannel flows and consent-first opt‑ins rather than assuming a global template will work (Twilio Uruguay SMS guidelines).

Best practice blends segmentation, timing and legal guardrails - ask customers how they want to hear from you, send during daytime hours, and localize copy and offers for events or seasons to boost relevance, as recommended in international SMS playbooks (International SMS marketing tips from Yotpo).

The result: campaigns that convert because they respect culture, consent and the channels Uruguayans actually use.

SMS capability / rule	Uruguay (per Twilio)
Two‑way SMS	No
Short codes / long codes	Not supported
MMS handling	Converted to SMS with embedded URL
Compliance highlights	Require opt‑in; support HELP/STOP in local language; send during daytime hours

“Having 19 brands across several geographies requires a lot of organization. Having a dedicated resource with SMS experience that can bring in best practices and creative ideas has really been impactful.” - Daniel Lawman, Chief Digital Officer, Wolfson Brands

Inventory forecasting and demand planning in Uruguay works best at the SKU level: practical models that blend historical sales, local seasonality and on‑the‑ground signals so stores from Montevideo to resort towns don't saddle themselves with costly overstock - warehouse costs are already cited as rising about 12% in some analyses, a sharp reminder that excess inventory ties up cash (see the simple guide to SKU-level demand forecasting guide).

For Uruguay this means adding region‑specific drivers - holiday footfall in Punta del Este, inland buying patterns, campaign timing - and testing causal features like promotions, competitor moves and short‑term weather swings (Punta del Este's changing 10‑day outlook is a good example of why coastal demand can shift quickly) so reorder points and safety stock match real risk.

Start with a small basket of high‑impact SKUs, iterate models that combine time‑series and ML, and use driver‑identification to explain when forecasts deviate from reality so procurement, merchandising and marketing act in sync (SKU forecasting best practices guide, Punta del Este 10-day weather forecast).

The payoff is concrete: fewer markdowns, lower storage fees, and shelves that reflect what Uruguayans actually reach for - no guesswork, just smarter stock.

Driver	Why it matters
Historical sales	Baseline patterns for SKU demand
Seasonality / Weather	Shifts demand in coastal vs inland regions
Promotions & discounts	Causes short‑term spikes or cannibalization
Competitor activity	Affects local share and SKU substitution
Economic shifts	Changes buying power and category demand

Dynamic pricing and promotion optimization in Uruguay must start with local facts: prices aren't uniform - studies show retail price dispersion is split roughly 39.16% across markets, 36.90% across stores, and 23.94% over time - and price changes occur about five times a year and tend to be highly synchronized, often clustering on the first day of the month, so any algorithmic strategy that ignores local market and store-level variation risks amplifying volatility rather than capturing margin.

Practical pilots should combine price-elasticity models and business rules (segmented or peak pricing for inelastic SKUs, time-based markdowns for elastic items), expose the logic so teams can audit outcomes, and pair promos with inventory signals to avoid costly stock imbalances; done well, dynamic pricing boosts margins and reduces markdown cycles, but poor transparency or aggressive swings can erode trust, so start small, test on high-impact SKUs, and measure customer response as rigorously as price movements.

Price Dispersion in Uruguay (research study on retail price decomposition)

Retail Price Setting in Uruguay (evidence on synchronized monthly price changes)

Metric	Value / Finding
Across‑markets price dispersion	39.16%
Across‑store price dispersion	36.90%
Within‑store (over time)	23.94%
Typical price change frequency	About 5 times per year (highly synchronized)

Visual search and image-based merchandising close the gap between inspiration and purchase for Uruguayan retailers by letting shoppers point a camera at a look they love and land on matching stock in seconds - no tricky keywords required.

Tools from vendors like Clarifai visual search solution and best-practice playbooks such as Intelistyle visual search product discovery guide show how image embeddings, object detection and “more like this” recommendations turn social photos or in-store snaps into product discovery engines that boost cart conversion and average order value; imagine a tourist spotting a jacket at a market and finding locally available alternatives immediately.

Start small with high-impact categories, enrich catalog images and metadata, and tie visual queries back to merchandising and buying decisions so stock matches what customers actually seek - visual search not only speeds purchase paths but also reveals what styles and patterns are resonating with shoppers.

Metric	Impact / Source
Order value lift	10% increase (Clarifai)
Cart conversion uplift	31% increase (Clarifai)
Checkout speed	2x reduction in checkout time (Clarifai)
Conversion & AOV uplifts	25–45% conversion uplift; up to 10% AOV increase (Grid Dynamics)

“Visual search removes hurdles, taking the customer directly from inspiration to gratification.” - Wanda Gierhart, Neiman Marcus, CMO

Fraud, returns abuse and loss prevention are immediate, revenue‑sapping concerns for retailers in Uruguay, and the best defense combines AI‑driven detection with practical business rules: automate decisioning with machine learning to spot patterns (like repeated sub‑$1 “card‑testing” attempts), layer device intelligence and digital‑footprint checks at signup and checkout, and route high‑risk flows to step‑up authentication so genuine shoppers keep a smooth path to purchase - recommendations that mirror Mastercard/Ekata's industry playbook for 2024 (Mastercard/Ekata: Industry best practices to prevent e‑commerce fraud (2024)).

Returns and chargeback abuse matter: recent analyses flag huge global losses from fraudulent returns and claims, so add real‑time anomaly detection and rules that link returns velocity to order and delivery telemetry to stop wardrobing and bricking schemes early (SEON guide to eCommerce fraud types and detection).

For Uruguayan teams, start with a small, high‑impact pilot - protect top SKUs, instrument device + shipping checks, and partner with trusted payment processors - then scale with explainable models so loss‑prevention decisions are auditable and reversible (AI‑driven loss prevention for Uruguay retail (2025)), turning suspicious spikes into fast, defensible actions rather than blind guesswork.

“SEON significantly enhanced our fraud prevention efficiency, freeing up time and resources for better policies, procedures and rules.”

In-store computer vision turns everyday shelves into a live dashboard for Uruguayan retailers - from Montevideo neighbourhood grocers to coastal shops serving seasonal tourists - so teams spot empty facings, misplaced items or planogram drift the moment it happens and act before a sale is lost; modern systems combine edge cameras, OCR and real‑time alerts to shrink manual audits and free staff for customer service (Computer Vision Shelf Monitoring (Software Mind)).

Pilots show vision tools cut out‑of‑stock events and monitoring time dramatically, and mobile solutions like autonomous inventory robots can scan a typical grocery in about three hours - capturing roughly 400 images per aisle and even distinguishing a regular from a fat‑free soup from across the store - turning hundreds of pictures into precise restock tasks and daily task lists for staff (Computer Vision Use Cases in Retail (LumenAlta), Computer Vision Implementation Playbook (LumenAlta), Tally Robot Real‑Time Inventory and Computer Vision (Simbe Robotics)).

Start with a single high‑impact aisle, pilot alerts into the store ops workflow, and pair vision with POS and replenishment rules so Uruguay's retailers capture sales, cut shrink and make shelves reflect actual local demand - not guesswork.

Metric	Value / Source
Typical scan time per store	~3 hours; ~400 images per aisle (Simbe)
Monitoring / audit time reduction	~80% faster automated monitoring (Perimattic / Simbe)
Out‑of‑stock reduction	Up to ~45% decrease reported in CV pilots
Payback period (example ROI)	Often ~6 months in mature pilots

“The BJ's brand and mission are all about creating an exceptional member experience. Tally is an amazing robot that allows us, with computer vision, to see exactly where our stock is every single day in every place in the store.” - Krystyna Kostka, SVP Store Operations (Simbe)

Explainability and governance are the safety net that lets Uruguayan retailers scale AI without sacrificing trust: practical programs should pair explainable model outputs and lineage with retail-ready controls so a price change, personalized offer, or inventory forecast can be traced, audited and rolled back if needed.

Start by mapping work to proven frameworks - embed the AWS Well‑Architected Framework: six pillars for cloud deployments (operational excellence, security, reliability, performance efficiency, cost and sustainability) into cloud deployments, while adopting data governance pillars - data quality, security & privacy, metadata, lifecycle and stewardship - to make datasets AI‑ready and auditable (Data governance pillars and best practices for AI-ready datasets).

Retail specifics matter: apply retail-focused policies for data minimization, consent and PCI/GDPR scope so loyalty and payment data stay protected and customer trust holds up (Retail data governance for privacy, PCI and GDPR compliance).

Finally, bake in continuous AI security testing, explainability checks and human‑in‑the‑loop gates so models help frontline teams execute the basics - promotions, stocking and store appearance - without creating surprise risk, turning governance from a compliance chore into a competitive advantage.

Framework	Core pillars (examples)
AWS Well‑Architected	Operational excellence, Security, Reliability, Performance efficiency, Cost optimization, Sustainability
Data governance (Atlan / industry)	Data quality, Security & privacy, Architecture & integration, Metadata, Lifecycle, Regulatory compliance, Stewardship, Data literacy

Post-purchase analytics in Uruguay turns after‑sale noise into clear actions: AI-driven sentiment analysis classifies reviews, support tickets and social chatter so teams can spot product defects, returns trends or advocacy opportunities before they scale.

Start by combining structured metrics like NPS and CSAT with unstructured signals - Qualtrics explains how sentiment analysis extracts feeling from text - and use platforms that consolidate feedback across channels so regional patterns (Montevideo vs.

coastal resorts) are visible in one view; for example, Talkwalker ingests feedback from 300 sources to surface real‑time sentiment and intent. Track post‑purchase behavior (reviews, returns, support contacts) and tie it to retention: Mailchimp highlights that small service wins matter - 66% of customers cite free shipping as a key purchase factor - so rapid detection of negative sentiment can prevent churn and convert satisfied buyers into repeat customers.

In practice, feed alerts into WhatsApp/Telegram support bots and ops dashboards to close the loop fast and measure whether fixes raise CSAT and NPS over time.

“We were able to establish a direct and immediate relationship with our customer base, to truly understand their needs and find a solution that works for them.” - Ahmed Soliman, Social Media Manager, ADIB

Getting started in Uruguay means pairing practical pilots with real readiness work: begin with a short AI audit, clean and align your retail data, then run a focused pilot on a high‑impact area (SKU forecasting, bilingual support or shelf monitoring) so outcomes and governance can be measured quickly; Uruguay's strong national push on capacity and ethics makes this sequence realistic, as shown in the Uruguay AI readiness spotlight - Oxford Insights (Uruguay AI readiness spotlight - Oxford Insights).

Prioritize data readiness and explainability - use the Parker Avery playbook for data hygiene to avoid common pitfalls and to make forecasts trustworthy (Retail data readiness best practices - Parker Avery: Retail data readiness best practices - Parker Avery) - and run pilots with clear rollback and human‑in‑the‑loop gates so teams keep control.

Invest in people as much as tech: role‑based training and an internal champions program turn uncertainty into capacity. For retail leaders ready to move from trial to repeatable value, a practical training route is Nucamp's 15‑week AI Essentials for Work, which teaches promptcraft and business use cases and streamlines the path from idea to impact (AI Essentials for Work bootcamp registration: AI Essentials for Work bootcamp registration); start small, measure fast, and scale with governance in place.

Attribute	Information
Course	AI Essentials for Work
Length	15 Weeks
What you learn	Use AI tools, write effective prompts, apply AI across business functions
Cost (early bird)	$3,582
Register / Syllabus	AI Essentials for Work Registration • AI Essentials for Work Syllabus