The Complete Guide to Using AI in the Retail Industry in Papua New Guinea in 2025

Why AI matters for retail in Papua New Guinea is simple: the technology that lifted conversion rates and cut waste elsewhere can do the same for PNG merchants.

Global adoption is accelerating - Coherent Solutions reports generative AI use jumped from 55% to 75% in 2023–24 and firms see strong ROI - and industry specialists point to agentic shopping assistants, hyper-personalization, and smarter inventory as 2025's game changers (Coherent Solutions AI adoption trends report 2025, Insider AI retail trends 2025).

For PNG retailers facing logistics and customer‑experience friction, practical moves like chatbots, demand forecasting and tailored offers can boost sales and resilience - see local examples and prompts for PNG in this Nucamp guide to hyper‑personalized recommendations for PNG shoppers (Nucamp guide to hyper‑personalized recommendations for PNG shoppers).

In Papua New Guinea retail, AI is already being put to practical use across the customer journey and the back office: generative AI and conversational commerce can power smarter chatbots and personalized product search to raise conversion rates, while automated content tools streamline product descriptions and imagery for small e‑commerce sellers (see the Publicis Sapient overview of generative AI in retail for use cases and caveats: Publicis Sapient overview of generative AI in retail industry); local capacity is rising too - homegrown firm NiuPay has shown how cloud‑based, AI‑driven systems (like its visa-processing platform) can speed routine decisioning and free humans for complex work, a capability PNG retailers can mirror for payments, fraud checks and tourist-facing services (APNGBC coverage of NiuPay AI-powered visa system).

On the supply side, AI-driven forecasting and machine‑vision crop monitoring - tools already demonstrated for cassava disease detection and precision agriculture - help fresh‑produce retailers manage seasonal supply and reduce spoilage (Norton Rose Fulbright analysis of AI in agriculture and future applications).

Practical PNG prompts and use cases - like hyper‑personalized recommendation engines and vehicle valuation tailored to local market quirks - make adoption accessible for smaller traders and marketplaces, turning scattered data into tangible sales lifts and fewer out‑of‑stock moments.

“The modality of online shopping interactions, and e-commerce interfaces themselves, may soon change,” says Sara Alloy.

Top retail use cases for Papua New Guinea merchants cluster around three practical, revenue‑first applications: virtual try‑on and fit tools that let shoppers see watches, bags or clothes on their own photo to cut hesitation and returns (Google's new “try it on” AI Mode and WANNA's AR try‑on show how fast that can be), AI‑driven personalized recommendations and avatar‑based fit matching that raise average order value and relevance, and automated visual content creation - 3D models, on‑model photos and virtual photography - that replaces costly shoots and scales listings quickly (see Reactive Reality's PICTOFiT and WANNA for examples).

These use cases aren't just tech experiments: vendors report measurable lifts - higher conversion, more products viewed and lower returns - while lightweight APIs let marketplaces generate try‑on images per photo at small per‑image cost.

For PNG retailers juggling limited shelf space and diverse body types, a shopper seeing a virtual watch snap onto their wrist in seconds can be the difference between a sale and a return, transforming browsing into confident buying; explore practical vendor options at WANNA's virtual try‑on solutions, Reactive Reality's PICTOFiT platform, or Google's Shopping AI Mode for context and integrations.

“With AI Clothes Try‑on feature, we're not just visualizing clothes, we're rethinking how people connect with style. It gives users the freedom and power to explore, customize, and express their fashion choices instantly.” - Alice Chang, Perfect Corp.

For Papua New Guinea retailers the commercial case for AI is concrete: practical lifts to top‑line revenue, sharper margins and less waste. Global studies show companies leveraging AI see average revenue gains of 10–12% and personalization can multiply revenue and conversion - product recommendations alone can drive up to 300% more revenue, 150% higher conversions and as much as a 50% increase in average order value; these are the levers PNG merchants can use to turn informal market data into measurable sales growth (see the SellersCommerce AI in e‑commerce statistics).

Smarter demand forecasting and supply‑chain AI also pay off: planning boosts revenue by up to 4%, cuts inventory needs by about 20% and lowers supply‑chain costs by roughly 10%, which matters in PNG where perishable stock and long transport legs make spoilage costly.

Conversational AI and chatbots - already mainstream in retail - offer 24/7 service that customers value and that frees staff for higher‑value tasks, while generative AI enables dynamic pricing, targeted promotions and automated product content that shrink operating costs.

For a compact playbook and local examples on tailoring recommendations to PNG shoppers, consult the Nucamp AI Essentials for Work syllabus on hyper-personalized recommendations and BCG Retail in 2025: Five imperatives to win.

Imagine one accurate forecast preventing an entire truckload of fruit from spoiling - small operational wins like that add up to real commercial resilience.

When choosing technologies and vendors for Papua New Guinea retail, pragmatic, edge-ready platforms matter most: NVIDIA's Metropolis and its vision-AI stack (DeepStream, TAO, TensorRT) power loss‑prevention cameras, shelf‑level stockout alerts and in‑store analytics, while Jetson devices and the Metropolis Showcase let small chains trial smart‑store demos without buying racks of kit; explore Metropolis for intelligent stores and trials at NVIDIA's retail pages (NVIDIA Metropolis for Smart Stores, NVIDIA Metropolis Showcase proof-of-concept (POC) offering).

For PNG's logistics and forecasting needs, RAPIDS, Merlin and cuOpt accelerate data pipelines, recommendations and route optimisation on GPU‑accelerated hardware, while NeMo, Riva and NIM microservices make multilingual shopping assistants and localised conversational agents feasible for Tok Pisin and other PNG accents by supporting customization and low‑latency deployment; see NVIDIA developer resources for retail toolkits (NVIDIA developer resources for Retail and CPG).

Complementary vendors already proving use cases - AiFi for autonomous checkout, RadiusAI for real‑time in‑store insights, and Cooler Screens for digitised cooler doors - offer turnkey options PNG retailers can pilot to cut shrinkage, reduce stockouts and scale product imagery and personalization without heavy internal R&D, turning limited store footprints into highly efficient, data‑driven outlets.

“Real-time 3D technology and platforms like NVIDIA Omniverse™ have helped us create product imagery that's two times faster, 50% cheaper, and at a level of realism we've never achieved before. This has led us to 100% brand consistency all across the world.” - Esi Eggleston Bracey

A practical AI implementation roadmap for Papua New Guinea retailers begins with a formal AI readiness assessment - benchmarked diagnostics that clarify where to focus scarce resources and which quick wins to chase; EY's platform highlights the value of benchmarking across more than 500 measurement points to create a strategic mobilization plan (EY Consumer Products and Retail AI readiness assessment).

Next, follow a factory‑like, phased approach: discovery and prioritization of high‑value use cases (opportunity discovery), a data‑quality and infrastructure audit, risk and governance checks, and an adoption plan that brings staff along; RSM's Readiness Assessment lays out these phases - initiation and discovery, AI analysis and gap assessment, prioritization and a clear roadmap, then implementation and post‑launch optimization (RSM AI readiness assessment for digital transformation).

Treat data as the non‑negotiable foundation - assess accessibility, quality, and storage choices (cloud, on‑prem or hybrid) and budget for GPU/accelerated compute as models move from prototype to production, following the practical data and infrastructure checklist recommended by expert guides (AI data and infrastructure assessment guide (Aliz.ai)).

Start small with a tightly scoped pilot - demand forecasting for one perishables route or an automated recommendation engine for a single product category - measure outcomes against business KPIs, then scale successful pilots using MLOps, standardized pipelines and clear governance.

Build in training and change management so store teams use AI outputs confidently, and expect measurable returns within 6–12 months when projects are well scoped and resourced; this disciplined, phased playbook turns the promise of AI into repeatable operational gains for PNG's unique retail landscape.

Retail infrastructure, operations and cost considerations in Papua New Guinea point to an edge‑first, hybrid cloud play: stores and market stalls that face spotty bandwidth, long transport legs and weather‑driven outages benefit from local processing that keeps point‑of-sale, refrigeration alerts and inventory logic running even when links to distant data centres falter.

Edge Cloud architectures are designed for the very low‑latency, compute‑heavy workloads retailers need - real-time video for shrink detection, instant POS authorisation, and on‑store AI for dynamic pricing and personalised displays - so pushing compute and storage closer to customers reduces bandwidth bills, lowers spoilage risk and improves uptime (see Ciena insights on edge computing applications).

An edge‑to‑cloud platform also makes scaling cheaper and less risky: pay‑as‑you‑go models and managed edge stacks let small PNG chains trial smart shelves or self‑checkout without heavy capex, while cloud training and model updates stay centralised for efficiency (HPE edge-to-cloud architecture guidance and Scale Computing retail edge use cases show how to balance local resilience with cloud analytics).

Practically, PNG retailers should map which workloads must stay in‑store (POS, temperature control, real‑time alerts) and which can be batched to the cloud, prioritise resilient edge hardware and partner with local providers to avoid costly one‑off deployments - so a single island shop can keep selling during a link outage and still feed valuable telemetry upstream for smarter forecasting and lower total cost of ownership.

“In a retail environment, without an Edge strategy, any Cloud strategy is at best incomplete, and at worst, destined for failure.”

Preparing Papua New Guinea's retail workforce for AI is as much about people and governance as it is about platforms: local capacity‑building - already visible in ITI's hands‑on AI literacy workshops that drew 14 participants and practical demos of tools like ChatGPT and Google Colab - shows how immediate skills gains can be (ITI AI literacy workshops in Papua New Guinea); national and shop‑floor programs should pair that local training with a role‑based, measurable upskilling plan such as Forrester's triad of data literacy, AI fluency and continuous learning and its emphasis on baseline assessments, micro‑certifications and ethical guardrails (Forrester upskilling public sector workforce for the AI era).

Complementary frameworks like the Digital Workplace Group's AI Literacy model (AWARE, KNOW, APPLY, EVALUATE, UPHOLD) help embed responsible use and explainability into everyday tasks, so sales assistants, inventory clerks and managers alike move from passive users to confident collaborators with AI (Digital Workplace Group AI literacy model (AWARE, KNOW, APPLY, EVALUATE, UPHOLD)).

Practically, PNG retailers should map skills to use cases, fund short accredited courses and micro‑credentials, require scenario‑based tests tied to real store data, and publish simple governance rules (who can auto‑price, who reviews RAG outputs) so trust grows and risks fall; when only a small share of organisations feel “AI‑ready,” targeted local training plus measurable governance turns technology into everyday, trustworthy productivity - so one confident cashier can use a recommendation tool to serve ten customers faster without sacrificing ethics or accuracy.

“You can't live without the technology these days, and this actually will bring a lot of efficiency to operations.” - Joe Agavi

What's next for AI in Papua New Guinea in 2025 is unmistakably practical: expect a steady push from generative AI for product descriptions, personalised offers and in‑chat assistance, backed by an industry-wide race to get data under control first; as Columbus' Retail Trends 2025 notes, larger retailers are already configuring generative systems for text and personalisation, but the real work begins with a clear data strategy and leadership education (Columbus Retail Trends 2025 report on generative AI in retail).

Local PNG merchants can follow the global momentum - NVIDIA's 2025 survey shows nearly nine in ten respondents are using or trialling AI and most report positive revenue impact - by starting small with pilots (product copy automation, a Tok‑thinking friendly chatbot for tourists, or a single‑category recommendation engine) and then scaling winners (NVIDIA State of AI in Retail and CPG 2025 survey).

APAC signals - mobile‑first shoppers and the rise of conversational commerce highlighted in the Adyen and WNS trendsets - mean PNG retailers should prioritise lightweight, mobile‑ready pilots, partner for content and payments, and rely on practical how‑tos like Nucamp AI Essentials for Work syllabus on hyper-personalized recommendations to convert experiments into measurable sales uplift; with data first and a handful of focused pilots, AI can move from buzzword to everyday tool in markets from Port Moresby to remote islands.

“We're still waiting to see a truly great example of AI in action. While some examples from larger retailers have been more concrete, showing how AI could be used, the focus is now on how it will be configured and implemented. We're seeing bits and pieces of how AI can improve productivity and knowledge, especially in-store,” says Ole Johan, Lindøe, Vice President of Digital Commerce at Columbus.

Looking ahead to 2026, Papua New Guinea retailers should expect AI to be both more useful and more tightly governed: the EU's staged AI rules already require basic AI literacy and bans on unacceptable practices (Article 4, in force from February 2025) and impose heavier obligations for general‑purpose models and high‑risk systems as the law phases in - details and timelines are usefully summarised in the Norton Rose Fulbright overview of the AI Act (Norton Rose Fulbright EU AI Act overview) and in practical compliance updates that cover penalties and supplier timelines (Manatex AI Act compliance guide for exporters).

For PNG merchants the takeaway is operational, not theoretical: treat vendor compliance as part of procurement (providers will carry many new obligations), run a fast inventory of every AI touchpoint, and fund basic staff training so people can safely use outputs.

Practical next steps are straightforward - audit systems and contracts, prioritise data governance and small, measurable pilots, and embed role‑based training for staff - training that can be sourced locally or through structured courses such as Nucamp's 15‑week AI Essentials for Work program (Nucamp AI Essentials for Work syllabus) - so by 2026 PNG retailers will be running smarter stores that also meet rising transparency and safety expectations.