How AI is transforming data and analytics in the industrial sector
Industrial data used to be a record. AI has made it your most valuable asset
Transactions logged. Inventory movements tracked. Customer interactions recorded. Demand patterns captured.
Year after year, your industrial software has been doing what it was designed to do: keeping a precise, comprehensive record of how your business operates. For most of that time, the record was the whole point. When you needed to know what happened, the system told you. Reports were produced, decisions were made and the data did its job.
Yet 92% of supply chain execs are still forced to make gut decisions at times because their reports don't provide predictive guidance. The data exists, it just doesn’t tell them what to do next.
92% of supply chain execs are forced to make gut decisions because their reports don't provide predictive guidance.
The data you already have
Most industrial businesses are sitting on a goldmine and treating it like admin. Every product you’ve ever stocked. Every supplier ever traded with. Every order, pricing decision and inventory movement going back years. It's all in there. But when was the last time anyone did anything with it beyond running the same reports on the same cycle?
Here's what putting it to work actually looks like. A manufacturer was sitting on a warehouse full of products that had stopped moving – deprecated lines, written off the balance sheet, taking up space. Standard response: accept the loss, move on.
Instead, using AI built on top of the data already in their ERP, every obsolete SKU was cross-referenced against live demand signals. Product descriptions were matched to regional search trends. Campaigns were generated automatically – one per product, targeted to where each was most likely to sell.
It took a day and a half. The stock that had been written off ended up generating €130,000 of revenue.
The data that made this possible was already in the system. AI didn't find new information. It just acted on existing information quickly enough to find value that would otherwise have stayed invisible.
Garbage in, garbage out
There's an old rule in data management: garbage in, garbage out. It's always been true, but it used to be a relatively contained problem. Maybe a bad report misled someone in a meeting, or a flawed forecast sent a buyer in the wrong direction. The damage was localised to one person or one decision.
AI changes the scale of that problem entirely, especially when it’s embedded across your operations. Suddenly it isn't just producing one report for one person. It's running continuously, feeding into automated workflows and influencing decisions throughout your business without anyone stopping to check the logic. A flawed input can easily get baked into the logic of the system and run from there.
Businesses relying on legacy systems that haven’t been updated in ten or fifteen years will have usually accumulated years of entries that were never properly maintained. Duplicate records. Obsolete product lines that were never removed. Nobody noticed because the reports still ran and the business still operated.
The moment you point AI at that data, you notice. The good news is that the moment most businesses confront their data quality isn't a crisis – it's a migration. Moving to a new system forces the kind of housekeeping that day-to-day operations never quite demand.
It’s uncomfortable. But businesses that get it right see real returns. Businesses using AI-enabled industrial systems report a 30% improvement in forecasting accuracy and a 15% reduction in operational costs.
The demand you’re currently ignoring
All industrial businesses have a version of this problem.
Enquiries come in across email, phone, WhatsApp or your website. Some are substantial, but many are small. A quote for a handful of units, a one-off order or a customer testing the water on a new product line. Each one requires someone to pull product data, check stock, apply pricing and write a response.
For the small ones, that process often costs more than the order is worth. So it sits in an inbox and gets deprioritised. Eventually it disappears and the customer moves on.
Nobody made a bad decision. The economics just didn't stack up.
This is the long tail of demand: the volume of small, fragmented enquiries that exist but never get converted because the effort of responding outweighs the value of the sale. For most, it's a significant and permanently ignored revenue stream.
AI changes the economics of that entirely. The same response that took 20 minutes of manual effort to pull live stock, check prices and draft a reply can now be handled automatically in seconds. The £50 order that wasn't worth pursuing becomes viable. Multiply that across hundreds of enquiries a month and you’ve created entire revenue streams that simply didn't exist before.
The data needed to do this is already in your system. It’s in your stock positions, your pricing logic and product catalogues. That's what makes automated responses viable rather than risky. The AI isn't guessing; it's working from the same data a person would use, just faster.
Your data is the starting point
Data is the one asset that no competitor can replicate and no AI vendor can supply. It exists because your business exists. And the longer you've been operating, the more of it you have. What's changed is what you can do with it. AI only works when it’s given data that’s clean, structured and connected enough to act on.
That's where Forterro starts: the data conversation. It’s how we help industrial businesses to understand what they have, where it needs attention and what becomes possible once it's working properly. And we can do the same for you.