Five ways retailers are using AI to handle EOFY demand spikes

By Michael Powrie, Founder and CEO at NeonNow

Periods of high demand, especially the end of financial year (EOFY) sales in Australia, are a constant challenge for retailers. During EOFY, customers expect fast, seamless service at all times, yet long queues, overwhelmed agents, and slow systems can make that difficult to deliver. Businesses must continuously manage surges in orders, queries, and complaints across web, mobile, social, and voice channels.

Australian shoppers are tipped to spend around AUD 10.5 billion during the mid-year and EOFY sales.

Today’s consumers expect speed, personalisation, and convenience at every touchpoint, yet many legacy systems and stretched support teams struggle to keep up.

Artificial intelligence (AI) and automation are helping retailers meet these expectations by handling routine enquiries, anticipating spikes in demand, and freeing staff to focus on higher-value interactions. Broader research into AI adoption shows firms are increasingly using intelligent automation to improve efficiency and customer experience.

Here are five practical ways retailers are using AI to manage demand spikes and improve customer experience during peak sales cycles like EOFY:

1. Scaling customer service across channels

Retailers regularly experience spikes in enquiries across web, mobile, social, and voice channels during EOFY sales. AI powered omnichannel customer service can respond instantly to routine questions about order status, delivery updates, return policies, or product availability.

By automating simple tasks, retailers ensure faster responses and consistent service across channels, which helps reduce wait times for basic enquiries and alleviates pressure on live support teams.

2. Anticipating demand and informing offers

AI driven predictive analytics helps retailers forecast demand patterns, including spikes in inquiries or product interest, based on historical data and real-time signals. Retail turnover in June 2025 rose 4.9 % compared with June 2024, reflecting elevated consumer activity during the EOFY sales period.

Embedding predictive intelligence into operational planning supports more confident decisions about resource allocation and promotions, driving smoother service delivery and stronger conversion.

3. Enhancing peak support with AI agent assist

Even with automation, human support remains essential for complex issues, like detailed product queries or nuanced return cases. AI agent assist tools support staff by surfacing relevant information, such as order history and recommended resolutions, and suggesting responses in real-time. 

This not only improves first contact resolution but also helps reduce agent burnout during intense demand periods by streamlining workflows.

4. Personalising experiences at scale 

Customers increasingly expect tailored interactions rather than generic responses. AI enables retailers to personalise experiences by analysing purchase history, browsing patterns, and individual preferences. This can inform dynamic recommendations, personalised messaging, and relevant offers across digital and voice channels.

Research on customer experience trends shows brands leading in experience now embed AI informed personalisation into their interactions to meet rising expectations.

5. Improving fulfilment and returns handling

Returns and fulfilment become particularly important during high demand periods like EOFY. AI tools help customers track shipments, locate relevant deals, and navigate returns more efficiently. For returns processing, AI can guide customers through self-service steps and escalate complex cases appropriately.

Reducing friction in fulfilment and returns helps boost satisfaction, keeps customer journeys fluid, and reduces the operational load on customer service teams at scale.

Demand spikes will always challenge retailers, but AI and automation now provide scalable, data-driven solutions that make service faster, more responsive, and more personalised. Research into AI adoption highlights both the efficiency gains and organisational shifts required to unlock its full potential, with many organisations increasingly investing to scale AI from pilots into core operations.