Retail organisations across Australia are entering a new phase of AI adoption, one that moves beyond experimentation and into operational reality.
At a recent executive roundtable in Melbourne, Blue Yonder brought together leaders from some of the country’s top retailers to discuss how AI is reshaping decision-making across the retail value chain. The room represented a significant portion of the consumer market. Attendees included supply chain executives and retail leaders from Australia's largest department stores, hardware retailers and supermarket chains.
What emerged was clear. While AI is accelerating speed and insight, it is also exposing new friction points in the way retail organisations operate in today’s business environment.
At the center of this tension sits merchandising.
AI is scaling—but so is complexity
Retailers are no longer approaching AI as a collection of isolated use cases. Instead, there is a shift toward enterprise-wide adoption, connecting customer engagement, supply chain execution, and store operations.
On the front end, AI is driving more personalized and intuitive customer experiences, from guided interfaces for business users to improved online-to-store journeys and conversational engagement.
On the back end, it is helping organisations respond faster to disruption. Whether it’s geopolitical instability, weather events such as fires or flooding, or ongoing cost and availability pressures, AI is enabling more dynamic, informed and responsive decision-making.
But as these capabilities scale, so too does the volume and complexity of the decisions that retailers are required to make every day.
This is where the cracks begin to show.
Merchandising: The bottlenecks in a faster system
Merchandising sits at the intersection of customer demand, inventory, pricing, and supply chain execution. As such it is the epicenter of where strategy and decision-making turns into action.
Yet, many merchandising functions were built for an era that was defined by slower planning cycles, siloed systems, and manual workflows.
As AI accelerates decision-making across the business, merchandising teams are increasingly under pressure to keep up. At the roundtable, leaders consistently pointed to the same challenges - reconciling data across multiple systems, managing exceptions, a lack of vision to interoperable process flows and relying heavily on spreadsheets to maintain operational flow.
The result is a growing disconnect. While AI can generate insights at speed, the ability to act on those insights is often constrained by legacy processes and tools.
In many cases, retailers are responding by adding more people to manage the complexity—an approach that is not scalable, desirable nor sustainable.
The real opportunity lies elsewhere.
From manual workflows to guided decision-making
What we are seeing is the emergence of a new operating model for merchandising, one where AI does not just inform decisions but actively supports how they are made.
This is where AI agents will play a critical role.
Rather than expecting teams to manually monitor data, identify issues, and determine next steps, AI can take on much of this workload. It can continuously track key metrics, surface anomalies, identify root causes, and guide users toward the right actions.
This shift is not about removing humans from the process. It is about reducing friction, improving consistency, and enabling teams to focus on higher-value decisions.
In effect, it transforms merchandising from a reactive function into a more proactive, insight-driven capability.
Balancing automation with retail reality
Despite the enthusiasm for AI, retail leaders are clear on one point; not every decision should be automated.
It is evident that there is an ongoing need to balance efficiency with context.
With around 76% of retail spend still happening in physical stores, the in-store experience remains critical. AI must enhance this by ensuring optimal product availability and efficient inventory flow, meeting customer expectations for product range and availability.
This is particularly relevant in merchandising, where the merchant’s judgement, knowledge, and local context play a critical role.
Therefore, the question is no longer about whether to use AI, but when it should be used and how to apply it most effectively.
Data trust: The foundation for scale
If merchandising is the visible bottleneck, data is the underlying challenge.
Across the roundtable, executives highlighted the difficulty of working with fragmented data environments. Information is often spread across multiple systems, requiring integration of data between systems (often nightly batch) and subsequent reconciliation before it can be used with confidence.
As a result, the conversation is shifting. It is no longer just about data quality; it is now about data trust as well.
Questions at the forefront include things like do teams have full visibility? Can I rely on the data in front of me? Can I act on it quickly? Does my data balance with data from others?
Without this foundation, scaling AI becomes significantly harder.
At the same time, a heightened awareness of governance and risk is taking hold. Australia’s regulatory landscape is still evolving, enabling organizations to innovate and experiment rapidly. However, as AI becomes increasingly integrated into core business processes, retail leaders are placing significant emphasis on robust governance and risk management to ensure responsible and sustainable adoption.
Rethinking roles, skills, and decision models
As AI continues to evolve, so too will its various roles and functions across retail organisations.
Merchandising, supply chain, and store operations are already beginning to shift from instinct-led decision-making towards more insight-driven models. This will require new skills, new ways of working, and a rethink of how decisions are owned and executed across the business.
More importantly, it will also require a real cultural shift.
The retailers leading this transition are those already embracing a test-and-learn mindset – in other words, those challenging established processes, moving quickly, and adapting as they go.
Removing bottlenecks in the AI era
AI has the potential to unlock significant value across retail, but only if organisations can translate insight into action.
That means addressing the bottlenecks that sit between data and execution.
For many retailers today, merchandising is where that work begins.
Removing these constraints is not just about adding new technology, it is about connecting systems, building trust in the organisation’s data, and enabling teams to make faster, more confident decisions.
In an environment defined by speed, complexity, and constant change, those capabilities will be what sets leading retailers apart from the crowd.
Want to go deeper on merchandising?
To explore how retailers are improving execution across stores visit Blue Yonder’s Merchandise Operations overview.
