🚪 The Last Portal You'll Ever Build
I had a conversation with a customer this week that crystallised something I've been thinking about for months.
We were discussing their website rebuild — new WordPress site, WooCommerce integration, the usual. They wanted to know about connecting their PIM to the new platform. Standard stuff. But then the conversation turned to AI, and within twenty minutes we'd accidentally designed the future of how their customers would interact with their product data.
The customer asked: could their end customers — the people buying their products — talk to an AI that knows the entire catalogue? Not a chatbot with canned responses. An actual intelligent assistant that understands every product specification, every technical detail, every image, every relationship between products.
Yes, I said. And it could create custom exports on the fly. A customer says "I need profiles for an outdoor canopy project" and the AI finds the right products, asks clarifying questions, and generates a formatted datasheet with full specifications and images. No portal. No filter screens. No export configuration. Just a conversation that ends with a download.
There was a pause. Then: 💡 "So we don't need to build a customer back-end?"
That's the moment I want to talk about.
🖱️ Thirty years of wrapping databases in buttons
Think about what business software has been for the last three decades. It's been user interfaces — elaborate, expensive user interfaces designed to help humans navigate data. Every portal, every dashboard, every configuration screen, every export wizard exists for one reason: humans need structured pathways to find and manipulate information.
Your customer portal exists because a human can't hold your entire product catalogue in their head. Your export configuration screen exists because a human needs to specify exactly which fields they want. Your search filters exist because a human can't look at ten thousand products simultaneously and pick the five that matter.
Every one of these interfaces is a concession to human cognitive limitations. And every one of them cost somebody time and money to build, test, maintain, and support.
Now consider what happens when you put an AI in front of the same data.
The AI doesn't need a portal. It can hold the entire catalogue in its context. It doesn't need an export wizard. You just tell it what you want. It doesn't need search filters. It can reason about which products match your requirements, ask you questions to narrow things down, and explain why it's recommending what it's recommending.
The interface isn't a screen full of buttons. It's a conversation.
👋 What disappears
I don't think this happens overnight. But I do think we're watching the beginning of a significant shift.
Customer portals become conversations. Instead of logging in, navigating menus, setting filters, and configuring exports, your customer talks to an AI that knows your products as well as your best salesperson does. Better, actually, because it has perfect recall of every specification, every technical detail, every product relationship.
Export configurators become requests. "Send me a PDF with these five products, full specs, images, and dimensions" replaces a twelve-step wizard where you select attributes, choose a template, and wait for a download link.
Search interfaces become dialogues. Instead of typing keywords and scrolling through results, you have a back-and-forth. "I need something for outdoor use." "What climate conditions?" "Coastal, so salt exposure." "In that case, here are three profiles rated for marine environments." That's not a search. That's consultation.
Report builders become questions. "Which products haven't been updated in six months?" "Show me everything we've added since January that's missing images." The AI doesn't need a reporting interface. It just needs access to the data and an understanding of what you're asking.
🧠 What arrives
Here's what most people miss: the portal disappearing doesn't mean the technology gets simpler. It means the technology underneath gets more important.
When a human navigates a portal, the data can be a bit messy. The human compensates — they know that "aluminium" and "aluminum" are the same thing, they recognise that a product image is wrong, they mentally skip over incomplete descriptions. The portal works despite imperfect data because humans are remarkably good at working around problems.
An AI reasoning over your product data needs that data to be clean, structured, rich, and contextualised. It needs proper relationships between products. It needs consistent attribute naming. It needs accurate technical specifications. It needs comprehensive descriptions that give it enough context to make intelligent recommendations.
In other words, the AI makes your product data layer enormously more valuable.
This is why PIM doesn't just survive the AI revolution — it becomes the foundation everything else is built on. The single source of truth isn't just a nice architectural principle any more. It's the knowledge base that powers every AI interaction, every intelligent export, every conversational query.
And the companies that have been investing in their product data — cleaning it, enriching it, structuring it properly — are the ones whose AI will actually work. If your data is scattered across spreadsheets, trapped in an ERP, and duplicated inconsistently across three systems, no amount of AI will save you. Garbage in, eloquent garbage out.
🏗️ The knowledge layer
What's really emerging is something I'd call the knowledge layer. It sits between the raw data and the AI, and it's where the real value accumulates.
It's not just the product data itself. It's the context around it. Which industry are we in? What regulations apply? What terminology do our customers use versus what our engineers use? Which products are commonly bought together? What are the typical failure modes? What questions do customers actually ask?
This knowledge layer is what makes a generic AI into your AI. It's why we're asking each of our customers to name their own AI inside the PIM. Because it genuinely is their own — customised to their industry, their products, their customers, their terminology.
The prompt templates, the Agent Task library, the per-customer context configurations — these aren't just software features. They're accumulated institutional knowledge made accessible to an artificial intelligence. Every customer interaction that helps us refine a prompt, every edge case that teaches us something about an industry, every translation that gets tweaked because the AI used the wrong technical term — all of that compounds into a knowledge layer that gets more valuable over time.
🧭 Building for the transition
If you're a business that relies on product data — and if you're reading this, you probably are — here's what I'd suggest thinking about.
First, your PIM is about to become the most important system in your stack. Not your CRM, not your ERP, not your website. Your PIM. Because it's the system that holds the product knowledge that powers everything else. Invest in it accordingly.
Second, start thinking about your data quality not as a housekeeping task but as an AI readiness exercise. Every inconsistency, every missing attribute, every poorly structured product relationship is a point where the AI will give a wrong or incomplete answer. The companies that take data quality seriously now will have a significant head start.
Third, don't build another portal. If you're about to invest six months and significant budget into a customer-facing product portal, pause and ask whether a conversational AI interface over clean PIM data would serve your customers better. It almost certainly will, and it'll cost less to build and maintain.
Fourth, think about what knowledge your team has that isn't captured anywhere. The things your best salesperson knows about which products to recommend. The rules of thumb your technical team uses to check for errors. The industry context that helps someone decide whether a specification makes sense. That knowledge needs to be captured, because it's what turns a generic AI into something genuinely useful.
The portal era isn't ending tomorrow. But it is ending. What's replacing it is better for everyone — simpler for customers, more powerful for businesses, and built on a foundation of structured product knowledge rather than structured user interfaces.
The last portal you build might genuinely be the last one you need.
Dr Pat Violaris is the Managing Director of OneTimePIM, a Product Information Management system built by people who've been wrestling with product data since 1992. He has a PhD in Expert Systems, which is what they called AI before it was cool.