AI in Retail: Stop Slapping an "AI Sticker" On It

Rethinking how we approach and actually use AI to make meaningful changes.

The retail world is abuzz with AI. From flashy product recommendation engines to voice-powered search, it's easy to assume AI is seamlessly revolutionising every facet of the industry. But let’s be honest—most businesses are barely scratching the surface. Instead of jumping straight to “AI-integrated solutions,” we need to rethink how we approach and actually use AI to make meaningful changes.

Here’s the catch: AI isn’t a magic wand. And no, slapping an “AI sticker” on your product doesn’t mean you’re leveraging AI effectively. What matters is understanding the right use cases and the right contexts — and knowing when to let humans stay in the driver’s seat.

The Hype vs The Reality

Retailers love the idea of AI automating everything, from generating meta descriptions to crafting product recommendations. And sure, AI-powered tools sound great in sales pitches. But are they always the right fit?

Take product descriptions, for example. Many brands dream of AI auto-generating descriptions that perfectly capture their tone of voice. The reality? It’s a mess. AI tools can churn out descriptions that sound bland, robotic, or—worse—indistinguishable from competitors using the same tech. Great copy is an extension of your brand identity. Why risk diluting it?

On the flip side, tasks like generating meta descriptions or creating schema diagrams for backend architecture? That’s where AI shines. These are jobs where nuance takes a backseat to speed, accuracy, and sheer volume—making them ideal for automation.

Practical Use Cases That Actually Work

Let’s move beyond the hype and focus on where AI genuinely delivers value in retail:

1. Documentation Done Right

Writing documentation—whether it’s for ERP systems, BI tools, or enterprise architecture—is the necessary evil of every tech team. It’s time-consuming, unglamorous, and prone to being deprioritised. Enter AI tools like Claude and ChatGPT.

Feed them your code, database schema, or even raw notes, and they can generate detailed documentation in minutes. For instance:

• ERP onboarding materials for new hires.

• BI reporting field definitions.

• Notion-ready markdowns with field-level explanations.

This isn’t just a time-saver—it’s a lifesaver. We’ve seen weeks’ worth of effort condensed into hours.

2. Schema Design and Diagrams

Creating ER diagrams or schema layouts used to involve hours of manual effort. With tools like Claude, you can input raw data or even meeting transcripts, and it’ll output ready-to-use Mermaid code. These outputs can be directly visualised in platforms like Notion, enabling faster collaboration and iteration.

And it doesn’t stop there. By treating AI-generated diagrams as inputs, they can evolve into even more refined outputs, such as SQL queries tailored to your database structure. It’s a cyclical workflow that streamlines complexity while improving accuracy.

3. Meta Descriptions with Context

If you’re crafting SEO meta descriptions, AI can be a handy helper. By pulling from existing product descriptions, it can generate concise, optimised text that’s perfect for search engines. This is a low-stakes area where AI can shine without compromising your brand voice.

Why AI Isn’t the Silver Bullet for Everything

The problem with most AI implementations in retail? Companies try to jump straight into automation without proving the concept. You wouldn’t automate a broken workflow—so why would you integrate AI into a process that hasn’t been refined manually first?

It’s not just about functionality. Consider:

• Brand Identity: Does your AI-generated copy align with your brand’s tone of voice, or could it end up sounding like everyone else’s?

• Google Penalties: Will your AI-driven meta descriptions or content trigger red flags with search engines? (Spoiler: it depends.)

• Customer Trust: Is your messaging becoming too robotic or impersonal?

The truth is, some things—like compelling product copy—are better left to humans.

Using AI Without “Integrating” It

Let’s clear up a misconception: you don’t need to fully “integrate” AI to make it useful. In fact, some of the best use cases involve keeping AI out of your core systems and instead leveraging it as a tool to supercharge specific tasks.

Our approach often involves testing AI’s capabilities through back-and-forth conversations with tools like ChatGPT. Only once it’s clear the tool can deliver the right results do we formalise the workflow. This ensures AI is adding value without overcomplicating processes.

The Future of AI in Retail

AI has already begun to revolutionise the way we work—but not in the way you might expect. Instead of replacing humans, it’s freeing them up to focus on higher-value tasks. And that’s the point: AI should complement human expertise, not replace it.

But the retail industry needs to be careful. Blindly integrating AI or chasing the next shiny tool won’t deliver results. Thoughtful implementation, clear workflows, and a strong focus on brand identity are what will separate the winners from the rest.

So, the next time someone pitches you an “AI-powered everything” solution, take a step back. AI isn’t about doing everything—it’s about doing the right things faster, smarter, and better.