AI use cases are many. But AI is eroding marketing trust- what’s wrong? The way marketing teams are using it. Here’s a better way.
“Our first point of contact with most information is rarely the information itself but some lossily compressed derivative that’s already been processed and strained through a dozen layers of reinterpretation.” – The Theory of Dumb by Lane Brown, New York Magazine (2025)
In an article about AI use cases for marketers, information must take front stage. As the quote says, and everyone has come to understand, the quality of information available has been degrading.
But why does this matter to us and the use cases for marketing? Because marketing, as a function, must reclaim and stand against the en-shittification of the internet. The reason behind this is simple: if the internet becomes desolate and untrustworthy, marketing as a function will be affected.
And do you need numbers to prove this? Marketing is equated with noise. It’s called marketing noise for a reason. And AI magnifies this noise and transforms content into slop. And slop it is.
Who are marketing teams producing this content for?
- It’s Google’s ranking systems to bring in more buyers.
- AI LLMs that can be hacked to bring in more buyers.
This buyer-attracting focus has made marketing teams less buyer-centric and more spammy. The industry must not fall prey to this. But you may think that this is not true- marketing is still appreciated, right?
This is partially true. Great marketing is still reaching people, but observe and see that many marketing campaigns are appreciated by fellow marketers.
Marketing is threatened by its own echo chamber. And AI will make it worse- unless everyone decides to stop and take action. The good news is that AI itself will help the industry get there, while positioning itself as the function of the AI era.
The potential of marketing has never been this infinite, nor this polar. Depending on the usage, AI will change the industry for the better or worse. The goal of this article is to persuade you to a better way.
What is AI’s role in marketing?
AI has affected marketing and developers the most. After all, intelligence replaces intelligence. That’s why organizations laid off many marketing and development teams and tried to replace them with machines, only to call them back.
But as it stands, AI is still a threat to knowledge work as it becomes better and better. There’s a growing debate that the thinking machines will hit a wall. And as with all things tech, that wall will eventually be overcome by a new innovation.
So AI might be a mainstay for at least the next few decades, conservatively.
We know two things:
- AI slop is real, and people can identify AI after they see the patterns emerge. The patterns become repetitive after a point.
- Automation has made things easier, and with intelligence in the mix, it is no longer necessary to physically create messages. The AI does it based on the segment.
These two realities have eroded trust in marketing. Organizations are just vying for similar buyers. If you are a manager or above, reading this, you must get calls daily.
Buy this and buy that. Please hop on our 15-minute call to show you our solution.
And now AI telemarketers and SDR agents have increased the number of calls an organization makes.
The use of AI in marketing today has been that of the factory, churning out messaging to the buyers at light speed.
Yes, there’s also market research, report creation, and strategy (god forbid), but teams are using AI tools as a volume farm. Usually, because marketing teams know their product needs volume, either because it doesn’t solve a core problem or the market is highly competitive.
But as all contradictions go, this one will be solved by itself. AI can fix AI slop.
The question is how?
AI Use Cases for Marketers
Okay, let’s put marketers into two camps:
- Storytellers
- Strategists and Analysts
All marketers belong to one of two camps, depending on the campaign. So the strategies and use cases can be interchanged depending on which hat you’re using. You must choose the distinction you’re going for.
The difference exists because there are two different things.
You have to be a storyteller to battle slop and a strategist and analyst to understand what the customer wants.
AI use case 1: Storytelling or Content Creation (call it what you may)
Okay, content creation with AI sucks. The pattern repeats, and there are ample logical mistakes it makes. But why is that? Well beyond someone not editing the articles, the second part is not knowing what you’re doing.
For example, the article has an AI-written paragraph; if you can guess which, you win.
The difference between the paragraph and slop is simple: the intent behind it. A lot of articles, videos, and scripts- if you open Instagram or YouTube, you know what this means- sound exactly the same.
They don’t have any intent beyond self-promotion. And that’s where they falter.
Plug in your perspective in the LLMs or content creation tools, and you will see a difference in quality. Then feed it your own original messaging and positioning, and see it improve.
Even free tiers of ChatGPT and Claude start to sound original. But it requires you to have knowledge about the thing you’re writing about. Without it, you are creating slop of the highest kind.
It has no thought. It has no perspective. It is a thing about a thing. Not the thing itself. And you’re adding to the erosion of trust.
AI use case 2: Product Experience and Tool Creation
No. This does not mean the abuse of your Lovable credits, though it does mean thinking of AI not just as an assistant but as a co-creator.
Many AI tools are either the controller or the assistant. But one perspective that isn’t often heard or accepted is that it can be a co-creator of experiences. Think about this, you have a blog or website- but someone on Instagram is outselling you, whether that’s services or products. Why? They have realized how to use Instagram’s superior product experience and algorithm to their advantage.
AI can and will decentralize this- how?
- This is a prediction; feel free to disregard this point, but AI will soon start giving algorithmic recommendations- this will become the context or the illusion of it. So that means people who integrate AI will be able to personalize their product and website experience based on the segment or individual.
- Your current AI systems are not glorified chatbots and information management systems; if used right, they improve your experiences. You can create tools that your buyers want based on their journey. Think of content, right? What are they but experiences of other people? AI can collate this experience into a framework that can be played with.
- For example, a simple tool that, if you put the details of your ICPs, can give probabilistic answers of what messaging might work for them. And if you add an RAG function, based on your own user data, imagine the power of this tool. It would give answers based on data that you thought were unconnected.
The trick is always to use the tool in ways that are simple yet uncommon. You already have a lot of power (data) as a marketer. What can you do with it? You can create things with AI that aren’t content but tangible, free products that deliver experiences.
Instead of a blog, you can give them an actual tool to play with. It’s democratizing the HubSpot approach and dialing it to eleven.
AI use case 3: Channel Management
This use case is quite common. Every marketer knows what the omnichannel experience is, yet it eludes most.
The main challenge of the omnichannel experience is data silos and organizational context. Yes, this is a simplified explanation. The developers will be better suited to explain what you ought to do. But there is a solution here: AI helps unify data because it is currently the best information management system.
Of course, there are biases; that’s why there should be a context layer- these could be your MCPs or other custom methods that you use to unify all your data points.
One answer that Satya Nadella gives is the semantic embedding of data into one layer that the AI systems can use as context. One of the most powerful applications of this idea is channel management. It can help marketers: –
- Orchestrate the omnichannel experience.
- Divert resources to channels that show high growth potential.
- Predict user behavior and direct their actions in subtle ways.
While these ideas are not novel, they are not being represented in conversations about the positive change of AI in business. However, this does raise a huge ethical dilemma: the control of your users’ minds. With AI in the mix, as it becomes smarter, the sophisticated recommendations and experiences will affect the users.
The power of this implication is vast, and you, as a marketing leader, need to be aware of this.
AI Use Case 4: Audits
A quick question: when you or your team can’t solve a problem because you’re too invested in it, who are you turning to?
For many teams, it is AI. There’s a good chance that if you don’t understand something, you can ask ChatGPT (or whichever LLM you prefer). This gives you data or something akin to an answer that you already knew but didn’t want to vocalize. Now, you have a third person, “unbiased” view of your problem.
But LLMs are not quite there yet, creatively. They can give you past answers, but never what can be created, which makes them perfect for audits.
The most powerful AI use case is its ability to audit systems and data, and provide predictions based on its probabilities. This does require RAG integration and should not be overlooked.
For example, a recent audit you can run is this: how does your website stack against your competitors based on your audience’s behavior?
The AI will give you probabilistic scenarios based on their behavior and patterns.
Marketers need to think about AI as a creative enhancer.
The use cases presented to you aren’t the only ones. They are the result of asking a simple question: what if?
Marketers need AI to do one thing, in an ideal scenario: break out of conventional thinking. Aren’t there many ways to solve a problem? But revenue waits for no one. If a function doesn’t bring in money and prove its tangible impact, why does it even exist?
That is the consensus of many.
Marketing must not devolve into noise by producing more volume, more doing. Instead, it needs machines that help teams focus- and prove the impact. This is what AI can do.
But only when used effectively. To break constraints. To make sense of the data. And prove it.
