UBS: WES leads AI adoption in Australian retail and consumer, with COL, WOW, EDV, and BRG ranking near the top

Overview

UBS conducted a benchmarking analysis of AI adoption across its covered Australian retail and consumer companies. The report argues that AI is a transformative technology that could alter the industry’s competitive landscape, particularly through its effects on costs, revenue, customer experience, supply chain efficiency, and working capital efficiency. AI usage already spans all 18 companies, but the leaders typically possess first-party data built through long-term loyalty programs, large-scale organizational capabilities, formal governance frameworks, and deployment depth that extends from isolated use cases to full business processes.

Core views

The core view is that the competitive advantage from AI adoption is increasingly concentrating among companies with larger scale, stronger data assets, and more mature organizational governance. WES is the standout leader, while COL, WOW, EDV, and BRG also demonstrate relatively comprehensive AI deployment. By contrast, PMV, UNI, and LOV score lower based on public evidence. UBS also cautions that public disclosure is not a complete mapping of actual capabilities, so the scorecard should be viewed as an initial comparison framework rather than a final investment conclusion.

Analysis framework

The report draws on the UBS U.S. softlines retail team’s AI scorecard and scores each company across six dimensions: supply chain and logistics, marketing, sales and customer experience, store operations, organization and support functions, and product development and innovation. Each applicable dimension receives a score of 0, 0.5, or 1, and the total score is calculated as the average across applicable dimensions; non-applicable items are excluded from the average.

Methodology notes

• AI adoption benchmarkUBS AI scorecard

  Six-dimensional AI adoption assessment

  The scoring covers supply chain and logistics, marketing, sales and customer experience, store operations, organization and support functions, and product development and innovation; 0 indicates no evidence, 0.5 indicates partial or early evidence, and 1 indicates clear adoption.

• Evidence constraintsPublic information disclosure approach

  Disclosure bias

  The data is based on public information and may not reflect all AI progress; companies may disclose less due to commercial confidentiality, or may overstate the importance of AI as investor attention rises.

• Investment impact frameworkROI driver attribution

  AI financial impact pathway

  The report attributes AI’s potential benefits to lower CODB, lower COGS, higher revenue, and higher working capital efficiency, while also noting that competition, consumer acceptance, and cost inputs may reduce the benefits.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• WES

  Leader in AI adoption

  Strengths

  It has clear use cases across supply chain, marketing, customer experience, store operations, organizational capability, and product innovation; OneData has 12.5 million unique customer records, and Bunnings, Kmart, and Officeworks all have AI applications.

  Weaknesses

  Its leading position depends on continued investment, governance, and cross-business execution, and public disclosure may still not fully quantify the financial contribution.

  Comparison

  It is the only company in the coverage universe with a total score of 1.0/1.0, clearly ahead of peers.

  Risks

  AI project payback periods, data governance, privacy, security, and the complexity of store execution.

• COL

  High-scoring AI adopter

  Strengths

  It has shown clear progress in areas such as Ocado CFC, predictive AI last-mile delivery, Flybuys personalized marketing, ChatGPT Enterprise, and mycoles assistant.

  Weaknesses

  There is no clear disclosure of AI use in the product development dimension.

  Comparison

  With a total score of 0.83, it is in the top tier.

  Risks

  Complexity of fulfillment automation, acceptance of store shrink-control technology, and the risk of scaling AI tools.

• WOW

  High-scoring AI adopter

  Strengths

  AI picking optimization reduced picking routes by 15%, Auburn automated CFC can process up to 60,000 orders per week, and Quick Assist covers about 6,000 store leaders and saves around 10 hours per store per week.

  Weaknesses

  Evidence related to product innovation is relatively limited in some areas.

  Comparison

  With a total score of 0.83, it is in the same high-scoring group as COL and EDV.

  Risks

  Large-scale internal use-case selection, employee adoption, system integration, and governance costs.

• EDV

  High-scoring AI adopter

  Strengths

  There is evidence across Criteo advertising, AI promotion recommendations, EndeavourX personalized recommendations, image search, scheduling and store process optimization, back-office automation, and category optimization.

  Weaknesses

  Some ERP and next-generation AI insights remain more forward-looking, and the report did not count them as current AI usage.

  Comparison

  Its total score of 0.83 shows multidimensional deployment capability.

  Risks

  Technology separation, ERP restructuring, and the pace of AI implementation may affect the realization of benefits.

• BRG

  High-scoring AI adopter

  Strengths

  BRG AI is used in customer service, with disclosed quantitative results including a 75% reduction in onboarding time, a 65% reduction in customer service attrition, a 25% reduction in call handling time, and a 60% improvement in troubleshooting effectiveness.

  Weaknesses

  There is no clear AI disclosure in the product development dimension.

  Comparison

  Its total score of 0.80 is below WES, COL, WOW, and EDV, but it still remains among the leaders.

  Risks

  Sustainability of customer service AI results, global expansion, governance, and quality control.

• TWE

  Specialized AI user

  Strengths

  It uses AI and machine learning for 14-day weather forecasting, spray-window planning, and phenological stage forecasting; it also uses GenAI to support content generation, consumer trend analysis, and new product development.

  Weaknesses

  There is no clear disclosure of AI use cases in the sales and customer experience dimension.

  Comparison

  It has differentiated applications in agriculture, content, and product innovation scenarios.

  Risks

  Weather forecast accuracy, data quality in agricultural scenarios, and bias in consumer trend models.

• PMV、UNI、LOV

  Low-scoring group based on public evidence

  Strengths

  The report does not provide sufficient evidence showing that these companies have established broad AI deployment.

  Weaknesses

  There is less public disclosure of AI adoption, with total scores of 0.08, 0.17, and 0.33, respectively.

  Comparison

  They lag significantly behind WES, COL, WOW, EDV, and BRG.

  Risks

  If actual adoption is insufficient, they may fall behind in efficiency, customer experience, and data capability; if disclosure is simply limited, then the scores may underestimate actual progress.

Key data

• Number of covered companies18 companiesThe report says AI is already used by all companies in the coverage universe, but implementation and disclosure vary widely.
• Highest total scoreWES: 1.0/1.0WES shows clear evidence of adoption across all applicable AI dimensions.
• High-scoring companiesCOL, WOW, EDV: 0.83; BRG: 0.80These companies show relatively systematic AI deployment across multiple business functions.
• Low-scoring companiesPMV: 0.08; UNI: 0.17; LOV: 0.33Based on public information, these companies show less evidence of AI adoption.
• Adoption rate in organization and support functions83%Includes internal LLM tools, strategic partnerships, governance systems, and employee enablement.
• Marketing adoption rate83%Includes personalized marketing, promotion optimization, retail media, content generation, and use of first-party data.
• Sales and customer experience adoption rate72%Includes customer agents, search tools, contact centers, and quality control.
• Supply chain and logistics adoption rate72%Includes demand and inventory forecasting, fulfillment execution, AI in distribution centers, e-commerce fulfillment, and weather forecasting.
• Store operations adoption rate44%Includes frontline AI assistants, dynamic markdowns, security and shrink control, scheduling optimization, and agentic systems.
• Product development and innovation adoption rate21%Includes product design, speed to market, category optimization, and consumer trend identification.

Impact & implications

The investment implication is that AI is not just an efficiency tool, but may gradually become a structural competitive variable for retail and consumer companies. Companies with first-party customer data, cross-business deployment capabilities, and governance systems are more likely to convert AI benefits into cost advantages, revenue growth, and higher customer stickiness; however, for companies with limited disclosure, smaller scale, or only pilot-stage adoption, AI’s contribution to financial performance still requires further validation.

Risks

• Public information may underestimate or overestimate the true extent of AI adoption.
• Companies may disclose less AI progress due to commercial confidentiality, or may overemphasize the AI narrative because of investor attention.
• AI benefits may be partly offset by intensifying competition, consumer acceptance, implementation costs, and governance costs.
• Large-scale AI deployment involves risks related to data privacy, model governance, cybersecurity, employee adoption, and system integration.
• Some projects are still at the pilot or future-planning stage and cannot be directly equated with current financial benefits.

What to watch

• Whether leading companies can expand AI use cases from isolated efficiency gains into cross-business compounding effects.
• Whether CODB, COGS, revenue growth, and working capital efficiency show quantifiable improvement.
• Whether low-disclosure or low-scoring companies later add AI strategy, governance, and implementation case studies.
• Whether U.S. retail AI use cases continue to lead Australia and form replicable pathways for Australian companies.
• Whether AI adoption in product development and innovation rises from its currently low level.
• Whether AI governance requirements increase in regulation, data privacy, and store safety scenarios.