Artificial intelligence is becoming more than just a back-end efficiency tool for retailers; it’s transforming the customer experience.
According to recent data from Australia’s Department of Industry, AI adoption across retail functions like inventory management, marketing, and customer service has reached 45 per cent, marking an 8 per cent year-on-year increase.
Embedding AI with a customer-centric mindset
One retailer leading the charge is Australian fashion brand Blue Bungalow, which serves a predominantly older demographic with a wide range of body shapes, style preferences, and digital comfort levels.
Rafaelle Champagne, e-commerce manager at Blue Bungalow, said that the company embedded AI into multiple touchpoints driven by a deep focus on customer needs.
That strategy started with “high-impact, low-friction areas” like product tagging, product recommendations, and basic customer support automation.
“Over time, we also encouraged everyone in the business to think about how they could leverage AI to streamline basic tasks and help reduce their workload, in the objective of giving them more time to think and do interesting work rather than spending time on repetitive tasks – this came in the form of custom GPTs to help with caption writing, digital PR pitches, blog writing and on-site content, for example,” Champagne told RetailBiz.
These initial wins created confidence to move into more advanced tools like a Virtual Try-On feature, Switch Model, and a Shop with AI assistant — all launched ahead of regional competitors.
Champagne said tools like Shopify Sidekick have improved reporting and insights significantly.
“This allows us to easily segment our customers using natural language, and creating collections with on-page and SEO descriptions that we can then tweak. It means more time spent on strategic work, and less on repetitive or time-consuming tasks,” Champagne said.
“It’s given all team members within the business the tools to answer questions and explore theories themselves, making everyone more empowered, curious and engaged.”
Balancing innovation with empathy
Blue Bungalow took a human-first approach when it introduced tech-forward features to a customer base that’s cautious towards technology.
The company’s messaging was framed around “making shopping easier” rather than showcasing the tech. That positioning, alongside consistent human support, helped bridge trust for users who are hesitant about the use of AI.
“These tools were positioned as optional and additional helpers, never as replacements for good old real and personalised customer support, which we’ve become known for over the years and take great pride in.”
“We built trust by making them easy to opt in/out, and by continuing to offer live help across all touchpoints – we have decided to be completely transparent as to what is AI-powered and what isn’t, for example using the copy ‘Shop with AI’ rather than giving the AI shopping assistant a human name, to avoid any confusion and deceit.”
Champagne emphasised that education plays a crucial role, with the company creating landing pages and videos that demystified the technology.
Blue Bungalow also regularly included those in their eDMs and ads, knowing that once customers use those tools, they will never look back.
The company received positive response from customers on the AI-powered features they use, especially for AI tools that reduce uncertainty, such as their Virtual Try-On solution.
Champagne and the team saw that it has influenced conversion and engagement metrics.
“We’ve seen improved engagement, longer session durations, and significantly higher conversion rates and average order value when these features are used. The number of outfits that are created daily using the Virtual Try-On tool is truly astounding.”
“It almost gamifies the whole online shopping experience and allows us to showcase our wide product catalogue, improving product discovery, which is amazing both for new and returning users. We’re also hoping to see our return rate decrease over time as we gradually include more of our product catalogue in those tools.”
To build the Virtual Try-On solution, Blue Bungalow did an open casting call with their customers to find women that would best represent their audience from an age, size and body type point of view.
“A customer who describes herself as mid-60s, short, with an apple body shape, can see our best-selling elastic waist jeans on someone that looks like her, thanks to generative AI, while still also seeing the clothes on a variety of models (as we’ve always shot our garments in size 8 and 18).”
“This not only enhances the experience, but it also helps build confidence in the purchase and reduces anxiety around sizing, which is especially key if they’re shopping with us for the first time.”
The retailer is also looking to add more models to that experience over the next few months.
Personalisation, data hurdles, and what’s next
Champagne explained that AI enabled the business to personalise the shopping experience for customers by adjusting featured items and learning first-hand from customers to give them more relevant suggestions.
“Personalisation at scale is where AI shines. With such a diverse customer base and thousands of products live from different brands on site at any time, it’s critical that people see styles available in their size and suited to their preferences.”
However, AI rollout does not come without roadblocks, with data quality and internal adoption still remaining among the top challenges.
“AI is only as good as the data you feed it, and we had to invest a good amount of time into cleaning and re-structuring our product and customer data.”
“There was initially some hesitation around the customer-facing tools, mostly uncertainty as to whether our customers were ready for AI and if timing was right but we’ve always prided ourselves on being technology-first and early adopters so we decided to charge ahead and we’re so glad we did! Everyone was onboard as soon as results started coming in.”
To ensure that AI tools enhance the human element of retail rather than replace it, especially in customer service, Blue Bungalow designs its AI systems to purely support its people.
“AI handles the repetitive, high-volume tasks like FAQs or initial product queries, while our human team focuses on empathy, complex scenarios, and relationship-building. This delivers efficiency without losing the personal touch we’re known for.”
Additionally, Blue Bungalow’s customer experience team leverages those customer-facing tools themselves to offer superior service, whether it’d be to recommend similar styles if a product is sold out, help a customer picture themselves in a dress to wear for a special occasion, or simply to quickly find complementary pieces.
So what sets successful AI adopters apart?
“I’m fairly confident that the retailers seeing the best outcomes aren’t necessarily the ones with the flashiest tools. They’re the ones using AI to solve real, measurable problems – they’re not afraid to experiment and they value speed over perfection, as customer feedback will ultimately guide UX improvements,” Champagne explained.
Champagne offered three tips for retailers looking to follow the company’s lead. First is to start with the customer and their pain points, not the technology.
“[Second,] pilot in a low-risk area, measure results, and iterate fast. Bonus points if you can have a close relationship with your tech partners so that you can both move fast (shoutout to the Preezie team who thrives on feedback). [Third,] upskill your team early, AI adoption is a team sport, not just a tech project and everyone needs to be interested, curious and on-board. And give your team freedom to experiment!”
Looking ahead, Champagne said she is excited to see how AI evolves to support natural dialogue, emotional intelligence, and personalised service.
“The future of Shopify working directly with AI chatbots and conversational agents is what really gets me excited. Imagine AI understanding customer needs through natural dialogue and then seamlessly accessing our store’s catalog, inventory, and customer data to provide personalized recommendations and complete purchases.”
“We invest a lot of time and resources in product enrichment and are constantly reviewing our product data, so I’m hoping this will pay off.”