Brexit export paperwork is still adding cost, delay and complexity for UK manufacturers selling into the EU. For many businesses, the problem is no longer understanding the rules but continuing to manage them through manual processes, spreadsheets and overstretched teams. That is why the real opportunity now is not political debate, but finding smarter ways to automate rules-based compliance work and protect export margins.
You’re scrolling through LinkedIn. An “AI-powered” toothbrush ad. Then a webinar on “AI transformation”. Then a vendor pitching their “AI-first” CRM. By the time you’ve finished your coffee, the two most-hyped letters in business have lost all meaning, and you’ve probably tuned them out completely.
That’s the white noise problem. And it’s quietly costing companies real opportunity.
How we got here
A few years ago, “AI” meant something fairly specific. Now it’s slapped on anything with an if-statement and a marketing budget. Some products genuinely use large language models. Others wrap a basic algorithm in a chatbot interface. A surprising number just rebrand the recommendation engines they’ve had since 2015.
The result: when everything is AI, nothing is. The term has been stretched so thin that customers, employees, and even executives can’t tell what’s underneath. And when you can’t tell, you do one of two things: you assume it’s all magic, or you assume it’s all hype. Neither helps you make good decisions.
What gets lost
Here’s the irony. While “AI” loses its meaning at the surface level, the technology underneath is doing genuinely extraordinary things. Models that can summarise three hours of meeting transcripts in two minutes. Agents that can chase down a refund through six service portals. Pipelines that spot anomalies in claims data before a human would even open the file.
But these specific, valuable things get lumped into the same vague “AI” bucket as a Shopify plugin that suggests product titles. So when someone asks “should we be doing AI?” the question is almost meaningless. It’s like asking “should we be doing software?” in 1998.
The companies getting the most out of this technology have stopped asking that question. They’ve started asking better ones:
- Where in our workflow do people spend hours on something a model could do in seconds?
- What knowledge inside our company is locked in PDFs, Slack threads, or someone’s head?
- Which decisions do we make repeatedly, with similar inputs, that could be assisted (not replaced)?
Notice that none of those questions contain the letters A or I. That’s the point.
The fatigue is rational
There’s a reason people roll their eyes when “AI” comes up in meetings. They’ve been promised transformation, sold middling products, and watched leadership chase trends without strategy. Healthy scepticism is a feature, not a bug.
But scepticism becomes a problem when it tips into dismissal. If you’ve decided “AI” is just hype, you’ve stopped paying attention to a category of tools that is genuinely changing how knowledge work gets done. You’re not protecting yourself from snake oil; you’re just making sure your competitors get there first.
A better way to talk about it
If you’re a leader trying to cut through the noise, here’s a small but useful rule: drop the term “AI” and describe what the thing actually does.
Instead of “we want an AI strategy”, try “we want to reduce the time our team spends on admin”. Instead of “is this product AI?” ask “what does it do, on what data, and how does it fail?” Instead of “AI will change everything”, try “this tool will change this specific process”.
It’s less impressive in a board deck. But it leads to better decisions, better budgets, and, eventually, better outcomes.
The signal under the noise
The frustrating thing about white noise is that it isn’t silence. There’s information in it. The reason “AI” became overused is that something genuinely significant did happen: the underlying capabilities really did take a leap. The hype is a lagging indicator of real change, not a substitute for it.
So, the goal isn’t to ignore the noise, and it certainly isn’t to join in with louder noise of your own. It’s to listen past it. Find the specific problems in your business that this technology is well-suited to solve and ignore the rest. Treat “AI” the way you’d treat any other tool: interesting only insofar as it solves something real.
The companies that get this right won’t be the ones with the most “AI-powered” features. They’ll be the ones where you can’t quite tell what’s AI and what isn’t, because everything just works a little better than it used to.
That’s what value looks like when the noise dies down.
Author: Deborah Holmwood, Client Change & Transformation Partner.
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