Generative AI in Retail’s Shift from Talk to Transformation
- Juanita Neville-Te Rito
- Apr 2
- 6 min read
Updated: Apr 2
I was delighted to atted and moderate the first half of the day for the inaugural NORA Retail and Consumer Goods Gen AI Summit in Melbourne. And if there’s one conversation defining the future of retail right now, it’s AI.
Let me start with a confession: I’m already AI-exhausted.
In the past two months, I’ve been to events in New York, New Zealand, Amsterdam, and Chicago. AI has dominated every keynote, every fireside, and every coffee chat. Everyone’s talking about it. Everyone has an opinion. But very few are giving us a clear path forward.
That’s exactly why this summit mattered.
We didn’t come for hype — we came for clarity. And to cut through the noise.
It was New Zealand’s own Andrew Moseley from Superhuman AI who nailed it with the most accurate (and unforgettable) line of the day:
“Gen AI in retail is like teenage sex. Everyone’s talking about it, no one’s quite sure how to do it, and those who are doing it probably aren’t doing it well.” 🤭
A laugh, yes — but also a reality check. AI is everywhere: disrupting industries, reshaping customer expectations, and making us rethink how we operate. But for many retailers, the gap between talk and action is still wide.
That’s why this wasn’t just another day of panels. We’re here for real stories, real lessons, and a no-BS approach to Gen AI.
We opened with Patrick Rechsteiner, who’s spent the past year on a global AI pilgrimage — dissecting how leading retailers from Silicon Valley to Europe are getting it right (and wrong). His message? Before you chase the shiny tools, get your house in order.
Then came Nicola Clement, a digital transformation heavyweight. She cut through the hype with case studies showing where AI is truly delivering — and where it’s falling short.
Nicola also referenced MAD 2024 MAD (Machine Learning, AI & Data) Landscape landscape alone sparked a dozen follow-up chats. If you are more data inclined and want to deep-dive on the “state of the union” of the data, analytics, machine learning and AI ecosystem is evolving, this review by Mark Turck from FirstMark is a great overview (especially for the generalist business person).
To view the interactive version of this landscape click here
And, one of the most grounded and inspiring speakers of the day: Goran Stefovski, CTO of Kogan. No fluff — just lessons from the field. He’s not theorising; he’s building. And his insights into how Kogan is driving AI transformation were among the most practical takeaways of the summit. There were many more speakers as well and I will unpack their fairy dust to give you a taste of where you should be exploring and why.
But the core question I kept coming back to was this:
Where does the hype end — and where does the real opportunity begin?
That’s what this article unpacks. Let’s go.
What Generative AI Actually Is — and Isn’t
Gen AI isn’t a mystical black box. It’s a set of tools built on large language models (LLMs) — software trained on mountains of data to generate text, images, music, even code. Think of it as a turbocharged autocomplete, but with reasoning capabilities and the potential to replace or augment whole workflows.
This includes:
Writing product descriptions, emails, ads.
Summarising reports or contracts.
Creating personalised shopping journeys.
Generating images, audio, or synthetic humans for marketing.
Gen AI isn’t one thing. It’s an evolving toolbox — one that, used well, can streamline operations, unlock creativity, and make teams radically more efficient.
But — and it’s a big but — only if you know what you’re doing.
The State of Play: What Retailers Are Actually Doing
Most don't. Nicola Clements said it plainly:
Retail teams are overwhelmed by the sheer number of tools available.
Her presentation pulled from a list of over 100 AI-for-work platforms, showing that while options abound, very few companies know how to make meaningful use of them.
And the examples we do see? They're often surface-level:
A chatbot here.
Some product copy automation there.
Maybe a marketing team playing with synthetic influencers or AI-generated social content.
A few brands stand out. Hugo Boss, for example, now develops over 65% of its products digitally with the help of AI — aiming for 90% by 2025. That’s not dabbling — that’s integration.
Closer to home, Christelle Young CEO of T2 shared a refreshingly grounded case study. Her team has used Gen AI to save 40–55 hours a week across internal tasks like summarising reports, preparing board decks, refining comms, and even supporting leadership decisions. Not flashy, but highly valuable. And scalable.
But they are exploring further and she showed some incredible tools which have taken their creative video production for 2 weeks to 1 day.
Where Retailers Should Actually Start With Generative AI
So where do you begin if you're not Hugo Boss or T2? Patrick Rechsteiner laid out one of the clearest frameworks at the Summit. Here's the condensed version:
1. Get Leadership Involved
This isn’t a side project for your innovation team. It needs top-down buy-in — from the CEO to finance and HR. Otherwise, it’ll stall.
2. Audit Your Existing Tech Stack
Many SaaS platforms already have Gen AI capabilities. Don’t reinvent the wheel — start by turning those features on.
3. Build One Tool Everyone Can Use
Start with something universal — an internal AI assistant that helps write emails, summarise meetings, or generate FAQs. A single shared tool builds AI literacy across departments.
4. Start with Admin, Not Marketing
Yes, AI can create cool content. But it can also slash the time spent on documentation, scheduling, onboarding, and reporting. That’s your low-hanging fruit.
5. Don’t Forget the Boring Stuff
Security, governance, and data structure aren’t sexy, but they’re foundational. Without clean data and responsible policies, your AI efforts won’t scale — and could backfire.
💥 Nicola Clement added one more crucial piece: stop thinking in silos. The real value comes not from a clever tool here or a pilot there — but from orchestrating AI across the business.
What’s Next for Generative AI: From Pilots to Platforms
Right now, retail’s Gen AI adoption feels like a collection of isolated experiments. A chatbot in customer service. A copy generator in marketing. A data analyst playing with prompts in the corner.
But Gen AI's real power kicks in when you connect the dots. Imagine:
Your AI summarises customer feedback, flags emerging trends, and pushes those insights to the product team.
Your logistics system uses AI to dynamically update delivery ETAs based on weather, traffic, and warehouse status.
Your marketing team runs AI-optimised tests on product bundles, pricing, and seasonal campaigns — adjusting in real time.
That’s orchestration. That’s transformation.
And yes, it’s hard. But, as Mike Knapp (ex-Google genius) noted, it starts with mindset and CONTEXT IS KEY. “Write longer prompts. Give your intern context. Solve step-by-step.” A great mindset?
Treat AI like a junior team member — capable, but needing clear direction.
Final Thought: Embrace the Awkward Phase
Right now, Gen AI in retail is in its teenage years — full of potential, occasionally brilliant, but mostly awkward.
Too many businesses are still treating it like a novelty or a checkbox. And when it’s rushed or misused — think synthetic humans in clumsy ad campaigns — it risks doing more harm than good.
But that’s okay.
No one gets this right on the first go. What matters now is moving beyond the hype and doing the real work: building capability, shaping policy, embedding AI into how the business actually runs.
Because the retailers who lean in now — who build muscle while everyone else is still experimenting — will be the ones leading when the dust settles.
Everyone’s still talking about AI.
The smart ones?They’re already figuring out how to make it work.
Want to Start Exploring?
Here Are a Few Gen AI Tools Worth Checking Out
If you’re keen to move beyond the buzz and start experimenting, several tools mentioned during the NORA Summit are already delivering real value across retail use cases. Here are a few worth exploring:
🔧 For Content Creation & Marketing:
Copy.ai / Jasper – AI writing tools for product descriptions, emails, ad copy.
Canva Magic Studio – Now infused with Gen AI for design, copy, and image generation.
Runway / Midjourney – For AI-generated video and image content.
BrandComms.AI - Gen AI platform for effective advertising
🛒 For Ecomm & Dynamic Merchandising:
Dynamic Yield – Personalisation engine using AI to tailor content and offers in real-time.
Shopify Magic – Gen AI-powered features built into Shopify, including auto product descriptions.
🧠 For Internal Productivity:
Fireflies.ai / Otter.ai – Meeting transcription and summarisation tools.
Notion AI – Streamlines research, notes, and knowledge sharing across teams.
ChatGPT (with Advanced Data Analysis) – Great for summarising reports, brainstorming, and writing.
🎭 For Experimentation & Innovation:
Synthesia – Create lifelike AI avatars for internal comms or marketing.
Ablo by Space Runners – Enables customers to co-create fashion items with AI.
As Nicola Clement summed up: the tools are out there — what matters now is getting intentional. Start small, stay curious, and focus on solving real problems, not chasing hype.
Not Sure Where to Start with Generative AI?
If all this still feels a bit overwhelming, you’re not alone.
At RX Group, we’re helping retailers across the region identify where Gen AI can deliver real ROI — and more importantly, where to start. Whether you're driven by efficiency, innovation, or customer experience, we tailor the approach to fit your culture, structure, and risk profile.
Ready to move from curiosity to capability?
Reach out to Juanita@rxgroup.co.nz — let’s start your Gen AI journey with clarity and confidence.
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