Marketing has always had a noise problem. AI didn’t create it, but it has supercharged it.
Scroll through LinkedIn, skim the first page of Google results, or open your inbox, and you’ll see the same patterns repeating: identical advice posts, vague “thought leadership,” SEO pages that technically answer a question while saying absolutely nothing. It’s content designed to exist, not to matter.
Generative AI has made this easier than ever. With a few prompts, marketing teams can now produce endless volumes of passable copy at speed. The result is what many marketers are increasingly referring to as slop. It’s content with no meaningful point of view, no originality, and no human perspective.
That’s only half the story.
Used well, AI is also freeing teams from busywork, accelerating research, and creating space for deeper thinking and better ideas. The difference between those two outcomes has very little to do with the tools themselves, and everything to do with how marketing teams think.
AI isn’t killing marketing creativity. Uncritical use of AI is.
The industrialisation of bad marketing
Bad content isn’t new. What’s new is how efficiently it can now be produced.
Large language models work by identifying patterns in existing material and predicting what comes next. That makes them excellent at summarising, rephrasing, and standardising. It also makes them very good at reinforcing sameness. When marketers ask AI to “write a thought leadership article about AI in marketing,” the output reflects the average of everything that already exists, not something genuinely new.
The problem emerges when this output is published without challenge.
AI-generated content often looks competent on the surface. It uses the right terminology, follows familiar structures, and ticks SEO boxes. But it lacks lived experience, friction, and judgement. There’s no “so what?” No tension. No moment where a reader feels seen or challenged.
At scale, this creates three serious issues for marketing.
First, originality collapses. When everyone uses the same tools in the same way, brands start to sound indistinguishable. Tone of voice documents become meaningless if content is generated before thinking has taken place.
Second, metrics become the enemy of meaning. AI makes it easy to chase output: more blogs, more posts, more landing pages. But volume is not a proxy for effectiveness. Marketing that exists to satisfy algorithms rather than audiences quickly erodes trust.
Third, human perspective disappears. AI has no lived experience. It doesn’t know what it’s like to defend a budget, launch a campaign under pressure, or navigate internal politics. When marketing content loses those realities, it becomes generic advice for a hypothetical audience that doesn’t exist.
AI didn’t invent these problems. It simply made it possible to ignore them faster.
Why this isn’t a reason to reject AI
The instinctive “AI is ruining marketing” backlash misses something important.
The most effective marketing teams aren’t using AI to replace thinking. They’re using it to protect it.
When applied with intent, AI removes friction from the parts of marketing that drain time without adding much strategic value. Research collation, performance summaries, ideation, campaign repurposing, these are areas where speed matters more than originality.
By accelerating the mechanical work, AI gives teams something most marketers have been starved of for years: time.
Time to think properly about an audience. Time to challenge assumptions. Time to develop a point of view instead of rushing to publish.
In that sense, AI isn’t a creativity engine. It’s a creativity enabler. It shifts the role of the marketer away from production and back towards judgement, taste, and decision making.
But that only works if teams are willing to take responsibility for what they publish.
Where AI genuinely adds value in modern marketing
There are clear, practical use cases where AI is already improving marketing effectiveness, and they have very little to do with “one-click content creation.”
Research and synthesis
AI excels at processing large volumes of information quickly. Marketers are using it to summarise customer interviews, analyse survey response data, review competitor positioning, and pull insights from long-form reports. The value isn’t in replacing insight. It’s in getting to the insights faster.
Drafting as a starting point, not an end point
A blank page is a blocker. AI helps teams get to “something” quickly, which can then be shaped, challenged, and improved by humans. The mistake is publishing the AI-generated first draft instead of treating it as raw material.
Operational efficiency
Campaign reporting, content repurposing, metadata creations, internal documentation, these are necessary but rarely strategic. Automating them allows marketers to spend more time on work that actually moves the needle.
In these cases, AI works best when it sits behind the thinking, not in front of it.
Acceleration versus substitution
One way to avoid the slop trap is to be explicit about where AI is allowed to lead and where it isn’t.
AI is excellent at acceleration:
- Speeding up analysis
- Generating options
- Removing repetition
- Scaling existing ideas
Humans are essential for substitution-resistant work:
- Defining strategy
- Making trade-offs
- Creating narrative
- Exercising taste and judgement
- Taking responsibility for decisions
When AI is used to substitute these human roles, marketing quality collapses. When it’s used to accelerate everything around them, marketing improves.
This distinction matters far more than prompt engineering tips or tool comparisons.
What this means at different levels of a marketing team
AI’s impact isn’t uniform. It changes how people work depending on their role, and ignoring that creates tension and confusion.
For marketing assistants and early-career marketers, AI is both an opportunity and a risk. It can accelerate learning, introduce structure, and reduce repetitive tasks. If it becomes a shortcut for thinking, it slows long-term development. The most valuable skill at this level isn’t “using AI,” but learning how to evaluate, edit, and improve what AI produces.
For managers and heads of marketing, the challenge is quality control. AI increases output whether standards exist or not. Without clear editorial judgement, brand guardrails, and accountability, teams will publish more (and achieve less). This level is where “just because we can” needs to be replaced with “should we?”
For CMOs, the implications are strategic. Brand differentiation, trust, and long-term equity are at risk if AI-generated sameness goes unchecked. The competitive advantage won’t come from having access to tools; everyone has that. It will come from how rigorously organisations protect originality, voice, and clarity of thinking.
AI exposes the maturity of a marketing organisation. It doesn’t hide it.
Standards are the real dividing line
The future of marketing isn’t a battle between humans and machines. It’s a divide between teams with standards and teams without them.
AI will continue to improve. Content will continue to scale. The internet will get louder. In that environment, the marketers who will achieve the most will be the ones willing to slow down at the right moment, to edit harder, think deeper, and publish less but better.
The question every marketing team now must answer isn’t “How do we use AI?”
It’s:
- What do we believe?
- What do we want to be known for?
- What are we willing not to publish?
AI doesn’t answer those questions, but it makes avoiding them impossible.
