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

Ukraine's retail scene is quietly becoming a global AI lab: chains from VARUS using ChatGPT for scalable product descriptions and Midjourney for on‑demand images, to Fozzy's Kissa AI that scans a tray and prints a receipt in 1–2 seconds - reportedly 10x faster than a conventional checkout - show practical, revenue‑focused deployments that cut costs and speed service.

Grocery and mall operators experiment with iBeacon, geofencing and chatbots to deliver localized offers, while predictive platforms like Num8erz.Customer Insights have driven a 13% lift in average basket for pilot retailers by surfacing “frequently bought together” pairs.

Regulators and industry groups are racing to align data protection and AI guidance with EU norms, and a growing crop of Ukrainian AI SaaS startups supplies tools for pricing, social listening and visual search - a rich playbook for retailers ready to move from pilots to scaled value.

Read more on how local operators are adopting AI and the results they're seeing from Deloitte and RAU.

2024 will be a year of opportunity amidst challenges for the retail sector. Climate change, a shortage of workers and pressure on supply chains - these challenges will force retailers to adapt and invest in new technologies for them to remain competitive. This year, investing in innovation and adapting to new conditions are the key for retailers to survive and thrive.

The methodology for this piece leaned on in‑country reporting and real pilot results: primary reporting from LIGA.Business and a Deloitte Ukraine overview provided the backbone for Ukrainian retail examples, while industry context came from the Retailers Association of Ukraine and local case studies cited in those reports.

Reporting drove a pragmatic focus - looking for concrete deployments, measurable gains and lessons for scale - so examples like VARUS's use of ChatGPT and Midjourney to replace costly photo shoots and Fozzy's Kissa AI (which recognizes trays and prints a receipt in 1–2 seconds, reportedly 10× faster than a conventional checkout) were prioritized over abstract theory.

Where possible, findings were cross‑checked against RAU market analytics and Nucamp's local writeups to ensure the roundup reflects operating realities in Ukraine rather than foreign hype, highlighting what works now for chains, customers and operations.

Read the original coverage on LIGA.Business and Deloitte Ukraine for full case details.

“Artificial intelligence is only gaining momentum, but it clearly already is and will be an indispensable assistant in forecasting, prioritizing, model building, and interacting with customers.”

VARUS has made product descriptions and catalog copy a production line instead of a bottleneck by automating creative tasks with AI - replacing costly photo shoots and scaling creative production while tailoring copy for Ukrainian shoppers and local search terms.

By applying prompt engineering best practices - providing clear goals, return formats, locale-specific keywords, and few‑shot examples - teams can get consistent, SEO‑friendly snippets that fit category pages, meta descriptions and mobile feeds; guides from V7 Labs and PromptLayer explain how techniques like iterative refinement, negative prompts and structured examples lock in tone and reduce hallucinations for catalog work.

The practical payoff is a catalog that reads native, loads faster for search, and frees merchandisers to focus on assortment and pricing rather than endless copy edits - read more about VARUS's marketing automation with AI and prompt guidance in the VARUS case study and the V7 prompt engineering guide.

For VARUS, Midjourney has become the visual workhorse that turns one reference photo and a clear brief into multiple catalog‑ready images without another studio booking: using Midjourney Image Prompts lets teams submit an existing product shot (or several) alongside a text prompt so the model borrows core elements like composition and color, while the image weight (--iw) gives tight control over how closely variants follow the reference (see the Midjourney image prompts guide for retailers: Midjourney image prompts guide for retailers).

From there, the Vary / Variations tools make it simple to generate subtle or strong alternatives - high‑key white backgrounds, low‑key dramatic portraits, flat‑lay knolling or lifestyle frames - so merchants can iterate thumbnails, hero images and social cuts without restarting production (learn how the Midjourney variations tool works: Midjourney variations tool documentation).

The practical lift is obvious: catalog managers can rapidly produce multiple localized image styles and sizes on demand, a direct complement to VARUS's AI copyflows and a fast way to scale visuals while cutting photo‑shoot costs; read the VARUS marketing automation with AI case for background: VARUS AI marketing automation case study.

Fozzy's Kissa AI - tested with Cantin trays - turns a camera feed into a full self‑checkout workflow, reportedly scanning a tray and printing a receipt in 1–2 seconds (about 10× faster than a conventional checkout), but real performance depends on engineering the camera UX and edge cases right.

Lessons from camera‑integration work show why: reliable device enumeration, a camera picker, robust permission handling, and a hidden canvas crop routine keep images consistent across phones, while a simple visual overlay and kiosk mode dramatically reduce alignment and detection errors (see a practical camera controls implementation guide for details: Service Portal camera controls implementation guide for reliable mobile camera UX).

For Ukrainian retailers scaling Kissa‑style checkout, those engineering patterns pair neatly with catalog automation use cases - fast receipts only translate to lower queues and higher throughput when camera reliability, error states and on‑site kiosks are baked into the rollout (read how AI is replacing costly studio work in local retail pilots: VARUS AI marketing automation case study for Ukrainian retail cost savings).

One memorable payoff: getting the UX right can turn a scanned tray into a printed bill before a customer finishes bagging, turning speed into a competitive edge.

Deloitte's NetSuite integrations give Ukrainian retailers a practical path from pilots to production by centralizing sales, inventory and supplier feeds in a single cloud ERP and then layering AI‑driven demand forecasting and replenishment on top; NetSuite's roundup of AI in retail shows how improved forecasting, guided discovery and inventory management cut waste and tighten “just‑right” stocking strategies, while Deloitte brings the integration, localization and change‑management muscle to make those models work in enterprise environments (NetSuite: 16 AI in Retail Use Cases and Examples).

That combo matters for Ukraine's chains: rather than react to shortages, retailers can use unified data to predict demand shifts and trigger procurements or ship‑from‑store flows before shelves run empty, turning missed sales into near‑misses.

Deloitte's NetSuite practice documents the end‑to‑end services - architecture, data migration, testing and operational runbooks - that shorten the runway from forecast to fulfillment (Deloitte NetSuite services for retail integration and modernization), so reconstruction funding and modernization projects can deliver measurable inventory and service gains instead of another disconnected pilot.

Auchan Ukraine's early experiments show how proximity tech can move a generic flyer into a hyper‑relevant, in‑aisle nudge: in Kyiv the chain installed an IoT iBeacon network - about 200 Bluetooth® beacons across roughly 33,000 sq.

ft. - so shoppers receive location‑based promo notifications as they pass departments, and those messages are often acted on within minutes (consumers tend to activate offers in the first two minutes after delivery).

While Auchan's team has noted the local market wasn't immediately ready for constant personalization, the infrastructure is a clear foundation for smarter in‑store campaigns that combine geofencing, push messaging and shelf‑aware signals; pairing beacons with real‑time shelf monitoring (see the Neurolabs ReShelf case study on on‑shelf availability) turns a discount ping into an immediate, fulfillable customer win.

For a practical primer on beacon ROI and indoor use cases, see the Navigine overview of beacon ROI and indoor use cases, and read the Deloitte roundup of AI use in Ukrainian retail for how these pieces fit together in pilots and rollouts.

“It is a system of sensors located inside the store. When customers walk past certain sensors, they receive a personalized notification on their mobile devices about specific promotions or offers available in a particular store and in a particular department that they passed by.”

Gulliver's next phase of digital service can lean on conversational AI to turn every simple question into a conversion: 24/7 chat and voice assistants answer “Is this in stock?”, guide to the nearest pickup, and surface tailored add‑ons in seconds - actions that industry studies tie to measurable lifts (IBM research shows ~12% CSAT gains) and a fast‑growing commerce channel (Juniper pegged conversational commerce near $290B by 2025) - see AIMultiple roundup of conversational AI use cases in retail (AIMultiple roundup of conversational AI use cases in retail).

For a Ukrainian rollout that must juggle languages, local store inventories and peak‑season spikes, platforms proven to deliver omnichannel chat, voice and video assistants help preserve brand tone, automate routine refunds and order tracking, and feed rich insights back into marketing and inventory teams - exactly the capabilities Firework and Yellow.ai highlight when they show how bots reduce friction, cut support costs and boost sales (Firework conversational commerce and retail chatbots, Yellow.ai overview of conversational AI in retail).

The practical “so what?”: well‑tuned agents can rescue a sale the moment intent appears, turning hesitation into a completed checkout before the customer moves on.

For a large Ukrainian retailer like Fozzy Group, sentiment analysis and social listening turn noisy streams of reviews, comments and mentions into clear operational signals - precisely the goal described in Sentiment Analysis in Social Networks, which frames the task as extracting subjective information such as opinions from natural‑language text (Sentiment Analysis in Social Networks (book on opinion mining)).

In practice that means automated review‑tagging to surface recurring product complaints, social‑listening dashboards that flag emerging PR or supply issues, and prioritized themes that feed merchandisers, category managers and customer service so corrective action happens before a problem scales.

Paired with local analytics teams or labs (for example, Laboratory Zi–style partners), these models can compress thousands of daily mentions into a short morning briefing - turning social noise into three actionable items for buyers and store managers - while linking back to catalog and promo flows already modernized in Ukraine's pilots (How AI Is Helping Retail Companies in Ukraine Cut Costs and Improve Efficiency (retail AI case study)).

NetSuite's SuiteCommerce turns scattered product, inventory and customer signals into a single guided‑discovery engine that helps Ukrainian retailers surface the right SKU at the right moment - pairing powerful site search and faceted navigation with AI‑driven “intelligent item recommendations” so shoppers see relevant upsells and cross‑sells based on purchase history and behavior (NetSuite SuiteCommerce B2C ecommerce platform).

Backed by real‑time ecommerce dashboards and saved searches, merchandisers and category teams get instant visibility into conversion rates, AOV and low‑stock alerts so recommendation models stay honest to what's actually available in Kyiv‑area stores and warehouses (ecommerce dashboard best practices; NetSuite saved searches guide).

Add visual search on top and the shopping loop tightens: a customer snaps a photo and, because image search narrows choices faster, research shows visual search can lift conversion likelihood dramatically - sometimes by as much as ~80% - turning discovery into immediate, measurable basket growth for omnichannel Ukrainian retailers (visual search in e-commerce benefits).

Rising theft and violence make loss prevention a front‑line AI use case for Ukrainian retailers: NRF's 2024 Impact of Retail Theft & Violence flags a 93% jump in annual shoplifting incidents (2019→2023) and a roughly 90% rise in dollar loss, while the National Retail Security Survey showed shrink at 1.6% of sales in FY2022 - numbers that argue for a data‑driven, layered response rather than ad‑hoc security fixes (NRF Impact of Retail Theft & Violence 2024 report, NRF National Retail Security Survey 2023 report).

For Ukraine, computer‑vision systems tied to transaction analytics and RFID can correlate camera events with POS anomalies, flag suspicious patterns across stores, and escalate only verified incidents to staff - combining the cameras, sensors and smart shelving Wesco recommends yields faster, actionable signals instead of noisy footage (Wesco article on technology to reduce retail shrinkage).

The practical payoff is clear: when analytics spot a coordinated “smash‑and‑grab” pattern or repeated POS voids, stores can lock high‑risk items, reroute staff, and preserve both safety and margins rather than reacting after the fact.

"It is not what you look at that matters, it's what you see."

Michaels' playbook for marketing optimization shows a practical path Ukrainian retailers can copy: by marrying first‑party data with generative AI to create a custom language model, Michaels scaled omnichannel personalization across email, SMS and social - raising personalized email coverage from 20% to 95% and lifting email CTRs ~25% and SMS CTRs ~41%.

The brand paired AI‑driven language experiments, multivariate testing and tight audience segmentation to send truly relevant subject lines and time‑sensitive texts, while linking SMS opt‑ins to loyalty and in‑store flows to close the loop on intent; learn more in the Persado case study on Michaels' personalization and the Attentive SMS case study detailing how text drove $63M+ in revenue.

For Ukrainian chains juggling reconstruction budgets and rapid digital adoption, the lesson is concrete: prioritize clean first‑party signals, invest in small iterative AI experiments, and measure by conversions - not vanity metrics - so a localized, generative campaign becomes a repeatable revenue engine rather than a one‑off pilot.

“We had all of this really rich data, but we needed to figure out a way to use it that allowed us to produce more relevant content that would inspire and enable creativity for each and every one of our Makers... With millions of Makers in our community who all have unique needs and preferences... it was a challenge to do this at scale.”

Conclusion: Ukrainian retailers such as VARUS, Fozzy Group and Auchan Ukraine should treat AI as a staged conversion, not a one‑off experiment - start with a clear strategy, shore up data and governance, run tight pilots, and only then scale the winners.

Practical next steps are familiar in the research: use enVista's planning checklist to define business goals and data requirements, build AI‑ready pipelines and security controls, and adopt a vendor evaluation playbook that stresses transparency, compliance and pilot performance (enVista: 10 Steps to Be Ready for AI in Retail, AI vendor evaluation checklist).

Pilot designs should mirror real ops - measure throughput, accuracy and edge cases (for example, Kissa AI's promise of a tray scan to receipt in 1–2 seconds shows how UX engineering turns speed into a competitive edge), then scale what reliably moves KPIs.

Invest in in‑house skills so teams can own models and prompts; short applied courses like the AI Essentials for Work bootcamp offer a practical path for merchandisers and managers to learn prompt design, governance and vendor oversight (AI Essentials for Work bootcamp registration).

Finally, treat reconstruction funding and modernization projects as catalysts: prioritize measurable pilots (demand forecasting, visual search, self‑checkout) and a repeatable vendor/pilot checklist so AI delivers repeatable ROI rather than one‑off wins.

“Artificial intelligence is only gaining momentum, but it clearly already is and will be an indispensable assistant in forecasting, prioritizing, model building, and interacting with customers.” - Oksana Zhebchuk, Director of Transformation, Fora (Fozzy Group)