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

Taiwan's retail landscape in 2025 is a dynamic blend of dense convenience‑store networks, booming e‑commerce and fast adopters of AI: market analysis estimates roughly a $100 billion USD market in 2025 with a conservative ~4% CAGR, while online grocery and social commerce surge as omnichannel strategies become essential (see the Taiwan retail market report).

Urban hubs like Taipei, Taichung and Kaohsiung drive sales, and local quirks - over 10,000 convenience stores, roughly one for every 1,500 people - keep physical retail vital even as personalization, quick‑commerce and data‑driven inventory systems reshape operations.

National tech initiatives - the Ten Major AI Infrastructure Projects - plus cloud and supercomputing investments are accelerating AI use cases from recommendation engines to voice commerce, so retailers that pair local marketing and localization with practical AI skills will move fastest; consider short courses like the AI Essentials for Work bootcamp to build those prompt‑writing and applied‑AI capabilities now.

Bootcamp	Details
AI Essentials for Work	Length: 15 Weeks; Early bird: $3,582, After: $3,942; Courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; Registration: AI Essentials for Work registration

Selection began with business-first filtering: every candidate prompt or use case had to map to a clear retail objective (sales lift, fewer stockouts, faster store decisions) and survive a buyer‑side self‑assessment, mirroring Info‑Tech's stepwise vendor‑selection playbook to define buyer persona and expected value (Info‑Tech AI solution selection criteria).

Next came data and integration checks inspired by enVista's readiness checklist - data quality, governance, and pilotability were non‑negotiable so solutions wouldn't founder on messy POS or inventory feeds (enVista AI readiness checklist for retail).

Practicality ruled: prompts had to be plug‑and‑play for teams (see Spatial.ai's prompt library for site selection) and prove value in small, measurable pilots before scaling (Spatial.ai AI prompts for retail site selection).

Vendor vetting followed a strict rubric - technical fit, security/compliance, support model and transparent economics - plus a mandatory POC to validate claims. The result is a Top‑10 that blends high ROI, Taiwan‑relevant workflows (store audits, demand forecasting, localized content) and low‑risk deployment paths - essentially a shortlist built to catch problems in a single pilot instead of across an entire store network.

Agent One™ is the omnichannel shopping co‑pilot Taiwan retailers need: a conversational layer that knits together LINE chats, mobile apps, in‑store visits and fulfilment so shoppers can move from discovery to checkout without repeating themselves.

By combining local channel support (think LINE and WhatsApp) with product recommender logic and checkout links, these agents help shrink cart abandonment and automate routine buys - exactly the promise highlighted in the industry coverage of AI shopping assistants (AI shopping assistants).

In practice, Agent One™ mirrors successful O2O plays in Taiwan - staff nudging customers to download apps for an NT$100 instant discount and training store teams to sell across digital and physical channels, as Senao did (Senao's omnichannel O2O play).

Built on conversational platforms that route questions, suggest exact‑store pickup slots and send tracking updates, these agents turn fragmented touchpoints into a single personalized shopping journey that reduces friction and protects in‑stock conversion rates (Omni AI omnichannel agents).

“We're on the brink of a commerce revolution.” - Jason Goldberg, Chief Commerce Strategy Officer, Publicis Groupe (RetailTouchpoints)

Sirius AI™ Recommendation Engine turns Taiwan's rich omnichannel signals into real‑time, hyper‑personalized recommendations that reach customers where they already shop - mobile apps, LINE, email and in‑store touchpoints - by using unified profiles to pick the next best offer at the exact moment it matters.

Backed by a Customer Data Platform and fast decisioning, Sirius ingests purchase history, browsing, loyalty and contextual signals (time of day, location, weather) to tailor feeds, surface dynamic product arrangements and even trigger replenishment nudges before an item hits its re‑order point - a practical, measurable win Cognizant highlights for retailers looking to lift conversions and repeat purchases (Amplitude hyper-personalization guide, Cognizant guide to real-time orchestration with modern data platforms).

Start small: pilot recommendations for high‑traffic SKUs, measure conversion and CLV, then scale across Taiwan's dense convenience‑store and social‑commerce networks for a clear ROI that customers actually notice.

“[We see] AI for personalization & predictive analytics: Top companies use AI in CDPs to personalize experiences & predict customer behavior.”

NetSuite Demand Planning turns sales history, seasonality and pipeline signals into actionable replenishment so Taiwan retailers can keep fast‑moving SKUs on shelves without overstocking backrooms; the cloud ERP ties forecasts directly to supply plans and will even create purchase, transfer or work orders once a plan is approved.

Native methods span moving averages, linear regression and seasonal averages while sales‑forecast inputs let teams fold in promo calendars or B2B orders, and multi‑location planning helps allocate stock across dense convenience‑store networks and e‑commerce channels.

For retailers facing viral moments - remember that a single TikTok or influencer mention can clear out stock in seconds - NetSuite provides the traceable demand plan and reorder point formulas needed to react quickly.

Where NetSuite's out‑of‑the‑box models aren't enough, its ecosystem supports SuiteApps and external ML engines to push higher accuracy back into the ERP. See the NetSuite Demand Planning product overview and the NetSuite inventory forecasting guide for best practices and replenishment rules to implement in pilot projects.

“Our job is to figure out what they're going to want before they do.”

PriceIntel™ brings dynamic pricing and competitive intelligence into Taiwan's fast-moving retail mix by feeding AI pricing models with real‑time competitor, inventory and sentiment signals so prices can be tuned across e‑commerce, LINE commerce and dense convenience‑store networks; the goal is simple and practical - capture price opportunities the moment demand spikes, avoid underpricing during viral moments and clear slow stock without killing margins.

Dynamic pricing relies on continuous data streams - competitor pricing, supply-and-demand shifts, customer behavior and market conditions - to adjust prices by the hour (or faster), a capability Nimble calls out in its guide to powering pricing with real‑time pipelines (Nimble real-time dynamic pricing guide for retail), while in‑store implementations can be supported by electronic shelf labels and fast repricing workflows to keep physical prices in sync with online offers (how retailers can implement electronic shelf labels for dynamic pricing).

Start with high‑traffic SKUs, guard transparency and test segmented rules so customers see better deals - not just fluctuating numbers - and finance teams can protect margin when the market pivots.

"The single most important decision in evaluating a business is pricing power... If you've got the power to raise prices without losing business to a competitor, you've got a very good business." - Warren Buffett

OpenAI GPT can speed up creation of on‑brand product copy for Taiwan, but the real win comes from linguistic adaptation and transcreation rather than literal translation: output must use Traditional Chinese characters, local phrasing and platform‑appropriate tone so descriptions feel native to Taipei shoppers and LINE users, not machine‑translated echoes of a Mainland ad (see guidance to prioritize Traditional Chinese for Taiwan).

AI prompts should specify script, desired register (formal vs. youthful), and local references because Chinese varies by locale - one famous pitfall is a single word like

土豆

meaning

peanut

in Taiwan but

potato

on the Mainland, a slip that can turn a campaign from charming to confusing (examples in Chinese marketing translation).

Finally, include technical constraints in prompts (character encoding, date/currency formats) and A/B test variants as part of

performance linguistics

to measure which phrasing lifts clicks and conversions; platforms like Smartling and localization playbooks can help structure those tests for Traditional‑Chinese markets.

Region	Preferred Script	Notes
Taiwan	Traditional Chinese	Prioritize local vocabulary, transcreation and Taiwan Mandarin nuances (Localization in Taiwan guide).
Mainland China	Simplified Chinese	Use Simplified for PRC audiences; avoid using it for Taiwan/HK.
Hong Kong / Macau	Traditional Chinese (Cantonese influence)	Adapt tone and terms to local usage (Chinese marketing translation best practices).
Technical	UTF‑8, local formats	Specify encoding, date/time and currency formats in prompts (see Traditional vs Simplified guide: Smartling guide to Traditional vs Simplified Chinese).

Appinventiv's visual‑search playbook is a must for Taiwan retailers that want to turn images into instant purchase paths: multimodal engines index product shots, catalog text and user queries so a shopper can upload a photo (or tap an influencer's post) and get relevant SKUs fast - a capability shown in visual‑search primers that link images to shoppable results and decrease dead ends in discovery (see Semrush visual search optimization guide).

Practical wins for Taiwan's mobile‑first shoppers include using high‑quality, single‑subject photos, next‑gen WebP/AVIF formats, descriptive filenames and alt text, product/schema markup and an image sitemap so crawlers and Lens‑style apps actually find and index your catalog pages (Google image SEO best practices).

Under the hood, feature extraction from CNNs plus fast similarity measures (Cosine Similarity, Euclidean Distance) turn visual fingerprints into accurate matches, while multimodal systems fold in surrounding copy to avoid false positives - think matching a pair of sunglasses from a crowded street shot rather than returning a sofa.

Start with high‑traffic SKUs, test multi‑angle images and measure conversions; visual search is less novelty and more conversion lever for Taiwan's dense convenience, social‑commerce and mobile channels (read the Dynamic Yield multimodal search primer for more on intent‑aware matching).

Dialogflow CX is a practical foundation for conversational commerce in Taiwan because it supports locale‑specific Chinese (zh‑TW) and tools to build voice flows that feel natural and task‑focused - start with the short, clear welcome prompts and error‑handling patterns from Google's voice agent design playbook to keep calls on task and avoid repeated questions (Dialogflow CX voice agent design best practices).

Add zh‑TW training phrases and use Dialogflow's multilingual features or AI‑assisted phrase generation to create Traditional‑Chinese intents and entities efficiently, then test voice interactions in the CX simulator and track operational KPIs (first‑call resolution, misroutes, number of turns) to refine performance (Dialogflow CX multilingual agents documentation).

For commerce, pair Dialogflow with secure webhooks, tokenized payments and voice‑auth fallbacks so shoppers can find an item, confirm availability and complete checkout hands‑free; Artech's implementation guide shows these integrations can lift conversions and cut service costs when done right (Artech guide to building custom AI voice shopping assistants for commerce).

In short: prioritize short, actionable prompts, robust no‑match repair, careful locale training and iterative A/B testing so a Mandarin or Taiwanese‑language voice flow becomes a measurable revenue channel in dense, mobile‑first markets like Taipei.

“The quality of the work I received was absolutely extraordinary. They posed very important questions and customized the final product to suit my preferences perfectly.”

DFIN Fraud Detection Prompts for Taiwan should focus on the practical controls regulators and platforms now demand: real‑time transaction monitoring and automated risk scoring that flag velocity spikes, anomalous shipping/billing mismatches and account‑takeover patterns; identity‑verification hooks that escalate to FIDO/OTP or biometric checks for high‑risk flows; and operational prompts to generate the audit trails and annual transparency reports required under the Fraud Crime Prevention Act - non‑compliance can mean fines up to NT$100 million and reputational harm, so automation is table stakes (Tookitaki guide: Navigating Taiwan's new anti-fraud law and business compliance).

Include ad‑safety prompts that trigger 24‑hour takedown workflows and advertiser identity rechecks to meet the proposed MODA rules (Baker McKenzie analysis of Taiwan anti-fraud regulations for online advertising platforms), plus federation prompts to share anonymized typologies with the NCCC and industry partners so models learn cross‑institution patterns (NCCC guidance on fraud prevention reporting and operations).

Make outputs explainable and regulator‑ready, and design prompts to apply dynamic friction only when risk scores justify step‑up authentication - this balances security with the smooth checkout experience Taiwan's mobile shoppers expect.

Lee and Li's practical checklist for Taiwan‑focused content compliance makes the legal guardrails clear: insist on verifiable human authorship for anything claimed as copyrighted, document creative contributions in contracts and logs, and bake Article 64 attribution rules into publishing workflows so every reused image, product photo or translated blurb carries a “clear indication of source” (failure to do so can trigger fines under Article 96).

Vet generative AI outputs against TIPO's guidance and the 2025 legal digests - because TIPO and recent practice treat AI‑only works as non‑protected unless a human exerts creative control - so require human transcreation, sign‑offs and retained source files before commercial use (see Lee and Li's analysis and the TIPO fair‑use primer).

For edge cases like parody, follow the conservative judicial tests Lee and Li outline: the humor must be instantly recognizable to Taiwan consumers and avoid consumer confusion with well‑known trademarks (ICLG and Lee and Li both flag courts' strict stance).

Make the checklist operational: clauses for assignment/licence scope, attribution templates, pre‑publish audit steps, and an evidence folder per asset so compliance becomes a repeatable store‑level habit rather than a one‑off risk.

parody must not only possess the sense of humor, satire, or criticism, but also must be instantly distinguishable and recognizable by consumers in Taiwan.

Taiwan's sustainability playbook for retail logistics is now squarely practical: a National Cheng Kung University study evaluated a reuse‑model across 10 supermarket products and found measurable reductions in packaging waste and CO2 when reuse replaces single‑use packing (National Cheng Kung University reuse packaging CO2 and waste study), while local innovators like PackAge+ are already wiring circular flows between e‑tailers, stores and consumers to slash what they estimate as over 80 million pieces of e‑commerce packaging a year in Taiwan (PackAge+ circular packaging initiative in Taiwan last‑mile e‑commerce).

On the delivery side, intelligent platforms that combine right‑sizing, dynamic multi‑drop routing, geocoding and ML‑driven address validation can cut empty miles, reduce failed deliveries and materially lower emissions - FarEye's analysis highlights dynamic routing and delivery orchestration as levers that together can save thousands of tonnes of CO2 and trim trip volumes while protecting service levels (FarEye dynamic routing and delivery orchestration analysis for sustainable last‑mile delivery).

• Reuse packaging: Study of 10 supermarket products in Taiwan: reuse models reduce packaging waste and CO2 (National Cheng Kung University reuse packaging CO2 and waste study).
• Reusable packaging startup: PackAge+ connects e‑commerce, consumers and stores to cut >80M single‑use packages/year in Taiwan (PackAge+ circular packaging initiative in Taiwan last‑mile e‑commerce).
• Route & delivery optimization: Dynamic routing, right‑sizing and ML address validation reduce trip volumes and emissions (FarEye dynamic routing and delivery orchestration analysis for sustainable last‑mile delivery).

The “so what?”: pair reuse packaging pilots with route optimization tech and Taiwan retailers can turn dense urban networks and mobile commerce demand from a pollution problem into a repeatable savings and brand‑value advantage.

Ready-to-run AI prompting in Taiwan retail begins with small, measurable steps: pick one business pain (reduce cart abandonment on LINE, speed reorders for convenience-store SKUs, or automate localized product copy), craft clear R‑O‑C prompts (Role, Output, Context) and iterate with tight KPIs - conversion lift, time‑to-serve, or fewer stockouts - so proof-of-value arrives inside a single pilot, not across the whole network; Vendasta's practical prompting playbook is a great place to learn the mechanics and templates (Vendasta AI prompting best practices).

Parallel to pilot work, prioritize language and localization (Traditional Chinese, platform tone) and omnichannel flows that mirror Taiwan habits - LINE, mobile apps and in‑store touchpoints highlighted in retail trend research - then scale what moves the needle (Insider 2025 AI retail trends for retail).

For teams wanting structured skill-building, short courses that combine prompt writing with governance and hands‑on pilots accelerate adoption; consider an applied course like the AI Essentials for Work bootcamp (Nucamp) to turn vendor checklists and prompt frameworks into repeatable store-level practice.

The real edge is simple: treat prompts as repeatable recipes, measure the outcome, and teach the same recipe to the next store so gains compound across Taiwan's dense retail landscape.

Program	Length	Early Bird Cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work bootcamp

“The habits we develop now, patience or impatience, curiosity or dismissal, collaboration or domination, will shape how we navigate an increasingly AI-integrated world.”