AI in Retail: Examples, Trends, and Building Trust in Australia

AI in retail is the use of intelligent, data-driven systems to enhance customer experience, streamline operations, and support decisions across channels. Australia is already living this shift: major retailers are piloting tangible use cases, from Woolworths’ “Olive” chatbot for customer support to Coles Liquor’s advanced forecasting and automated ordering system.

Yet a visible trust gap shapes how Australians buy. In the 2025 EY AI Sentiment Index Report, Shoppers widely interact with AI in retail but often don’t recognise it, which weakens confidence (Nijssen-Smith, 2025). And even when AI speeds discovery and comparisons, most people still click through to retailer sites, search, reviews, and forums to verify details; only about half fully trust AI recommendations (IAB & TalkShoppe, 2023).

This article maps what’s working now, what’s next, and how to close the trust gap with responsible rollouts that connect to your ERP/CRM stack for real business outcomes in Australia.

What Is AI in Retail?

AI in retail uses intelligent automation, analytics, and machine learning to personalise experiences and optimise operations. Core technologies include:

• Machine learning: Analyses sales patterns to forecast demand;
• Computer vision: Enables checkout-free experiences and visual search;
• Natural language processing (NLP): Powers chatbots and assistants;
• Generative AI: creates marketing copy, recommendations, and product information.

AI matters for Australia because adoption is scaling quickly. 91% of retailers are investing in generative AI to stay competitive and meet consumer expectations (Australian Retailers Association, 2023). This growth highlights an inflection point: Australian consumers increasingly expect faster, more tailored, and more transparent digital experiences. At the same time, businesses are rethinking how AI supports both operational resilience and customer trust.

Australia’s retail sector is shifting from efficiency-driven automation, focused on cost and logistics, to experience-driven innovation that prioritises personalisation, ethical data use, and real-time service. As noted in the EY Sentiment Index Report, Australians engage most actively with AI through retail, yet many still don’t recognise it in use (Nijssen-Smith, 2025), underscoring both the potential and the trust challenge ahead.

The advantages of applying AI in retail are wide-ranging. The key benefits include:

• Operational efficiency: Automates forecasting, inventory, and replenishment processes to reduce waste and improve stock accuracy.
• Customer experience: Powers personalisation, chatbots, and visual assistants for faster, more relevant service.
• Profitability and agility: Enables dynamic pricing and predictive marketing to improve margins and ROI.
• Compliance and sustainability: Supports waste reduction and ethical automation through accurate data handling and traceable AI models.

These benefits explain why AI has become a defining factor in how Australian retailers modernise operations and align technology with transparent, human-centred service models.

5 Core Examples of AI in Retail Driving Growth in Australia

Across Australia, leading retailers are deploying AI in customer experience, omnichannel engagement, and intelligent operations. 

Artificial intelligence now underpins both the customer-facing and operational sides of retail in Australia. Major brands such as Woolworths, Coles, The Iconic, MECCA, and David Jones are integrating AI to power forecasting, personalisation, and real-time service delivery, transforming the sector from product-centric to experience-led. From supply chains to marketing, AI has become the connective layer linking retail strategy, technology, and customer trust.

Here are five core examples showing how AI transforms both customer and operational sides of Australian retail.

1. AI for Personalised Customer Experiences

AI personalises how retailers connect with customers, tailoring products, content, and interactions in real time

• Predictive and generative personalisation: Algorithms analyse purchase history and browsing behaviour to recommend products or content dynamically.
• Stylist and recommendation bots: Retailers are adopting virtual assistants to guide customers through personalised styling or purchase options.
• Generative discovery: AI systems generate product descriptions and search results aligned with customer intent.

Examples: The Iconic uses multimodal search and Snap-to-Shop powered by Google Cloud’s Vertex AI to improve discovery and recommendations (Google Cloud, 2025); David Jones’ AR in-store displays bring products to life digitally (Colley, 2025, discussing ‘’David Jones eyes AI super-agent opportunity’’ in iTnews).

2. AI for Omnichannel Retail and Customer Engagement

AI unifies customer journeys across digital and physical channels, creating seamless omnichannel experiences

• Conversational AI and chatbots: Tools like “Olive” by Woolworths assist with queries, returns, and order tracking.
• Loyalty and retention analytics: AI maps customer sentiment and lifetime value across CRM data.
• Unified touchpoints: Synchronises store, web, app, and social platforms for consistent experiences.

Examples: Adyen Uplift solution uses AI to help retailers optimise conversion, reduce fraud and costs across the payment funnel; MECCA supports customers 24/7 with its Einstein Bots–powered “Miss MECCA” and Service Cloud, reducing chat abandonment (Salesforce, 2025).

These integrations show how AI supports cohesive engagement and improves response accuracy across multiple sales channels.

3. AI for Retail Operations and Supply Chain Optimisation

AI enhances retail efficiency through accurate demand forecasting, inventory management, and logistics optimisation.

• Predictive forecasting: Analyses historical data and seasonality to predict demand.
• Inventory & logistics: Optimises warehouse routing and delivery scheduling.
• Waste reduction: Automates markdowns and replenishment.

Example: Coles harnesses over 2,000 diverse data sets to make 1.6 billion predictions each day across 20,000 SKUs and 850 stores with AI operational efficiency (Bencic, 2024, ‘’Coles accelerates its AI journey to further enhance CX’’ in Retailbiz).

AI provides measurable ROI through better stock accuracy, improved availability, and reduced waste, critical in sectors where margins depend on real-time demand visibility.

4. AI for Merchandising, Dynamic Pricing, and Marketing Automation

AI enables retailers to align pricing, promotions, and marketing content with real-time customer and market data.

• Dynamic pricing adjusts to competitive data and stock levels.
• Predictive segmentation optimises audience targeting based on behaviour and channel data.
• Generative ad content creates personalised copy or imagery at scale.

For a deeper dive into campaign automation, segmentation, and AI-generated content workflows, see AI Marketing Automation Examples and Top AI Marketing Tools — a practical guide for Australian teams evaluating platforms and use cases.

Examples: JB Hi-Fi uses Google Cloud Recommendations AI to deliver personalised product recommendations, increasing average transaction value and conversion on recommended products (Google Cloud, 2021).

These capabilities enable retailers to shift from reactive promotions to continuous, insight-driven marketing cycles.

5. AI in Smart Stores and Workforce Optimisation

AI transforms in-store operations with smart devices, computer vision, and workforce scheduling that align human and digital intelligence.

• Computer vision enables checkout-free stores and improved security.
• Smart trolleys and AR try-ons enhance product exploration.
• AI scheduling optimises staff allocation based on traffic and sales data.

Examples: Coles applies Computer Vision at checkout to identify fresh produce and uses in-store CV to monitor deli queues (Bencic, 2024, ‘’Coles accelerates its AI journey to further enhance CX’’ in Retailbiz).

Together, these examples illustrate how AI connects every layer of modern retail, from insight and automation to human-centred interaction, making Australia’s retail sector one of the most dynamic in its regional transformation.

Emerging Trends in AI for Retail

The next phase of retail AI focuses on agentic intelligence, ethical automation, and sustainable commerce. It is moving beyond automation toward reinvention, with agentic and generative systems capable of reasoning, creating, and self-optimising processes across the retail value chain. These advances reflect a broader transformation: from static prediction to adaptive intelligence that learns and acts continuously.

Generative and Agentic AI in Retail

Retailers are adopting generative and agentic AI models to design new experiences and automate decision-making at scale. This trend is most visible in predictive customer experience design and AI-led product innovation, where generative tools analyse shopper intent to create new products, campaigns, or experiences at scale (Masters, 2025, discussing ‘’How Autonomous AI Shopping Agents Will Transform Retail’’ in Forbes).

Such models allow businesses to move from analysing data to letting AI agents recommend, test, and iterate real-time solutions, transforming how retail decisions are made.

AI for Payments and Trusted Commerce

AI enhances payment systems through fraud detection, secure checkout, and real-time transaction analysis.

Australian retailers are combining frictionless checkout with embedded AI for fraud prevention and compliance. Examples include Adyen’s Uplift initiative, which boosts payment performance using AI-driven anomaly detection.

These systems analyse transaction data in real time to enhance both speed and safety, aligning financial trust with customer convenience.

AI for Visual, Voice, and AR Shopping Interfaces

AI enables multimodal shopping experiences where customers can search, try, and buy using images, voice, or augmented reality. Australian retailer The Iconic is redefining online fashion discovery through Google Cloud’s Gemini-powered multimodal search on Vertex AI, enabling customers to find items using natural-language queries like “beach-themed party” (Google Cloud, 2025).

At the same time, voice-assisted commerce and AR interfaces are creating seamless, conversational checkout experiences, where shoppers can browse, compare, and buy with a few spoken or visual cues.

These technologies expand accessibility and make discovery more intuitive, bridging the gap between digital engagement and in-store satisfaction.

AI for Predictive Sustainability and Ethical Automation

Retailers are also using AI to enable sustainable operations through smarter resource use and waste reduction. Coles utilises AI models that “harness insights from over 2,000 diverse data sets to make 1.6 billion predictions each day” across 20,000 SKUs and 850 stores (Bencic, 2024, ‘’Coles accelerates its AI journey to further enhance CX’’ in Retailbiz).

Beyond efficiency, this signals a growing commitment to ethical automation, where AI systems are deployed not only for profit but for long-term environmental and social impact.

AI-Driven Retail Data and Media Ecosystems

A new revenue frontier is emerging through retail data monetisation. Businesses are turning internal insights into data-as-a-service offerings for suppliers and advertisers, forming retail media networks that leverage first-party customer data responsibly (Kohan, 2025, discussing ’’The 5 Biggest Retail Trends For 2026’’ in Forbes).

This shift represents a reinvention of retail’s core model: from simply selling products to also selling intelligence, reinforcing how data-driven ecosystems will define Australia’s next retail growth chapter.

Why Building Trust Will Define the Future of AI in Retail

As AI adoption accelerates, trust has become the defining factor separating innovation from hesitation. The Adyen 2025 Retail Report shows a 45% surge in shoppers using AI to buy, while adoption among Baby Boomers and Gen X has grown by 65% year-on-year (Adyen, 2025). Yet, this rise is tempered by scepticism: only 27% of shoppers believe brands are transparent about AI recommendations, and most still verify information before purchase (IAB & TalkShoppe, 2023).

Transparency and reliability have shifted from ethical ideals to strategic imperatives. Retailers that earn trust through data clarity and explainable AI will shape the next competitive edge in Australia.

The Trust Gap: Where Retail AI Confidence Breaks Today

Despite growing engagement, confidence falters in four recurring areas:

• Algorithmic bias and poor recommendations: Mismatched pricing, irrelevant results, or hallucinated data.
• Opaque data handling: Lack of clarity about how customer data powers AI.
• Over-automation without human oversight: Leading to errors and broken experiences.
• Uneven readiness among SMBs: Many pilot AI tools without governance or explainability.

While Gen Z leads in usage (47%), older demographics are closing the gap—proof that transparency and accountability can rebuild trust over time (Adyen, 2025).

How to Adopt Retail AI Responsibly

Building trustworthy retail AI begins with design, not deployment. Responsible adoption requires a phased, transparent approach that aligns innovation with compliance, accountability, and customer confidence.

Phase 1: Data & Consent Foundations

Ensure every AI use case complies with the Australian Privacy Principles (APPs) and, where relevant, GDPR standards. Retailers must obtain explicit consent, clarify how data is collected and used, and maintain audit trails for transparency.

Phase 2: Low-Risk, High-Impact Pilots

Start small with applications that deliver measurable value and minimal risk, such as AI chatbots, demand forecasting, or dynamic pricing. These projects help teams establish workflows and consumer trust without overexposing sensitive data.

Phase 3: Scale With Guardrails

As adoption expands, introduce AI governance frameworks that define model accountability, human oversight, and data-quality controls. Regular testing ensures accuracy, fairness, and explainability across all customer-facing systems. For ERP-centred rollouts, explore our AI in ERP guide, outlining common ERP AI technologies and five foundational practices to scale responsibly in real operations.

Phase 4: Unified Commerce & Continuous Assurance

Integrate trust signals across every retail touchpoint, online, in-store, and mobile, reinforcing transparency through clear disclosures, verified reviews, and consistent experiences. Continuous monitoring and feedback loops keep AI aligned with both ethical and commercial standards.

For e-commerce-specific orchestration of these trust signals and AI workflows, read the guide to AI in E-Commerce, which details platform integrations, implementation steps, and risk controls tailored to Australian retailers.

From Trust Gap to Trusted AI: The Future of Australian Retail

AI in retail is moving beyond automation toward assurance, where success depends not just on data volume, but on transparency, explainability, and consumer confidence. As Australian retailers expand their use of AI across commerce, logistics, and experience design, trust will define the next wave of competitiveness. Future leaders will not be those with the most advanced algorithms, but those whom customers believe in.

Australia is uniquely positioned to lead this transformation, with strong privacy frameworks, a tech-ready retail sector, and growing consumer awareness of ethical AI. By embedding trust at the core of digital operations, businesses can turn innovation into loyalty.

At Havi Technology, we help businesses build that foundation through ERP and CRM systems powered by Odoo, Microsoft Dynamics 365, and tailored AI solutions, transforming retail operations into intelligent, trusted ecosystems ready for the future.