Companies spend enormous amounts trying to increase demand before asking ..
Does the market actually understand what we’re the answer to?
It’s a problem I’ve seen repeatedly in complex B2B. Pipeline slows, conversion falls or growth plateaus and the instinct is to increase activity.
More campaigns. More content. More outbound. More paid.
More volume.
But volume doesn’t fix a weak signal.
If your positioning is ambiguous, your category is poorly defined and sales, marketing and product describe the company differently, increasing distribution simply sends that ambiguity further.
And now there’s another audience interpreting those signals: AI.
When a buyer asks ChatGPT, Claude or Google AI about your category, those systems aren’t experiencing your brand. They’re reconstructing an understanding of it from the signals you’ve put into the market.
Your website. Your content. Third-party coverage. Customer language. Category associations. The way other people describe you.
AI hasn’t created the category signal problem. It’s exposed it.
What is a category signal?
A category signal is the collection of clues that tells the market what you are, what problem you solve and when you should be considered.
It isn’t a tagline. And it isn’t simply your positioning statement.
It’s the cumulative effect of what your website says, how your sales team describes you, what your customers say about you, the problems you’re associated with, the language others use when they mention you and the category you repeatedly show up in.
When those signals reinforce each other, something useful happens.
The market starts to know where to put you.
A buyer doesn’t have to work quite so hard to understand why you’re relevant. A salesperson doesn’t have to reinvent the story every time they enter a conversation. And an AI system has enough consistent evidence to connect your company with the problems and categories you want to be associated with.
When those signals conflict, the opposite happens.
You can be visible everywhere and still be difficult to understand.
That’s why I think we’ve spent too long treating visibility as the objective.
Being seen isn’t the same as being understood.
The demand problem might actually be upstream
Marketing sees insufficient pipeline.
Sales sees poor leads.
Product thinks marketing doesn’t understand the product.
Leadership asks for more demand.
Everyone diagnoses the problem from the part of the system they own.
So marketing launches another campaign. Sales changes the pitch. Product produces more feature messaging. Leadership increases the pipeline target.
But the problem can sit two layers underneath all of them.
The company hasn’t established a strong enough signal for the market to understand what it is the answer to.
That weakness travels downstream.
Weak category signal creates vague positioning. Vague positioning creates generic messaging. Generic messaging creates campaigns that struggle to cut through. Sales inherits conversations that require too much explanation. Buyers struggle to understand why this company, rather than one of the alternatives.
Eventually it appears on a dashboard as a demand problem.
But demand is where the problem became measurable. It isn’t necessarily where the problem started.
This is why throwing more activity at an underperforming pipeline can be so expensive.
You’re turning up the volume on a signal that wasn’t clear enough in the first place.
Before asking how we generate more demand, I think there’s a better question: are we clear enough to deserve more of it?
Category Signal → Positioning → Messaging → Demand → Pipeline → Revenue
AI gives us a new way to see the problem
This is where AI gets interesting to me.
Not because it gives marketing another channel to optimise. But because it gives us a new way to see whether the signal we’ve created is actually coherent.
Ask an AI system about your company without feeding it your website copy.
What category does it put you in?
What problem does it think you solve?
Who does it think you’re for?
Which companies does it consider your alternatives?
What does it think makes you different?
And perhaps most importantly, when someone asks about the problem you believe you solve, does your company appear at all?
The answers aren’t a perfect measure of your positioning. But they’re an extraordinarily useful mirror.
Because AI is reconstructing your company from the signals available to it.
If the picture it reconstructs bears little resemblance to the one sitting in your strategy deck, that’s worth paying attention to.
And if AI struggles to understand where to put you, there’s a reasonable chance your buyers are doing some of that work too.
That’s why I built a free to use AI brand visibility tool that scores category ownership.
https://angelakennedy.co.uk/category-ownership-test
The score isn’t really the interesting part.
The interesting part is the gap between what you think you’re saying and what the market is actually able to reconstruct.
Because that gap doesn’t stay in marketing.
It travels through positioning, messaging, demand, pipeline and ultimately revenue.
AI has simply given us another way to see it.
I think we’re entering a period where being discoverable won’t be enough. The companies that win won’t simply be the ones that appear. They’ll be the ones the market knows how to place.
Before you turn up the volume, strengthen the signal.