AI is making bad marketing easier than ever
Author: Simon Kingsnorth
01 September 2026
In this article, I want to give you something more useful than a list of AI tools to add to your stack — I want to make the case for why the brands winning with AI right now are winning on strategy, not on adoption.
The organisations flooding the market with AI-generated content are not gaining an edge. They are diluting one. And the fix is not to use less AI — it is to be far more deliberate about the thinking that has to come before any of it is switched on.
Why is AI adoption not the same as marketing advantage?
The adoption numbers tell a story that is worth understanding clearly before we discuss what to do about it.
McKinsey’s 2025 global survey found that 79% of organisations now use generative AI — up from 33% in 2023. The CMO Survey from Duke University and Deloitte found that AI and machine learning now power 24.2% of all marketing activities, nearly double the figure from the previous year. And 94% of marketers say they plan to use AI for content creation this year. In practical terms, the share of marketing teams not using AI has fallen from 65% to 5% in two years.
Those numbers represent a remarkable pace of adoption. They also mean that AI is now a baseline, not a differentiator. When virtually every marketing team in your sector is using the same tools, producing content through the same pipelines, and generating outputs from the same underlying models, the technology itself cannot be the source of competitive advantage. It has become table stakes — the minimum entry requirement, not the winning strategy.
The paradox that defines 2026 is this: near-universal AI adoption has coincided with rare, genuine business impact. McKinsey’s same research found that only 6% of organisations qualify as high performers actually extracting bottom-line value from AI. The gap between adoption and value is enormous, and the explanation for that gap is almost always the same: organisations treated AI as a production layer rather than a capability that requires a strategic foundation to be worth having.
HubSpot’s 2026 State of Marketing report puts this in direct terms: AI is the baseline, not the differentiator. It argues that brands now need stronger points of view, more trust, and more human-led creativity — precisely because AI-generated content is becoming increasingly average. The advantage is no longer in having AI. It is in using it effectively to say something worth hearing.
What is actually happening to content quality as AI scales?
The evidence on content quality under AI scale is uncomfortable reading if your organisation has been investing in volume-first content strategies.
In a survey of 132 marketers currently using AI in their workflows, the single biggest concern — cited by 87 respondents — was that AI-generated content sounds generic. Not factual accuracy. Not off-brand messaging. Not relevance. Generic. The outputs technically answer the prompt, but they often lack originality, depth and personality. They are competent without being distinctive, and in a crowded market, competence without distinctiveness is invisible.
Mentions of “AI slop” — the industry’s blunt shorthand for low-effort, repetitive, interchangeable AI content — grew ninefold between 2024 and 2025. Consumer preference for AI-generated creator content has collapsed from 60% in 2023 to 26% in 2025. And 57% of marketing agency leaders now cite AI content oversaturation as a top concern in their own industry.
There is a mechanism behind this decline that receives less attention than the consumer preference data: model collapse. When AI tools are trained on AI-generated content at scale, outputs become progressively more generic. The models learn from each other’s linguistic patterns, reinforce common structures, and lose the edge cases, opinions and lived specificity that make content distinctive. The content ecosystem begins feeding itself, and the outputs get flatter with every iteration. Brands that built deep AI content pipelines early and did not invest in editorial quality controls are already seeing this play out in their performance data.
The content market of 2025 and 2026 is not merely crowded. It is approaching what one analyst described as theoretical maximum density — where generative AI tools have effectively reduced the marginal cost of content creation to near zero, and the primary constraint has shifted from production capacity to differentiation and performance. More content has not made marketing better. It has made the average marketing weaker, and it has raised the premium on anything that is genuinely distinctive.
Why is differentiation getting harder, not easier, as AI matures?
This is the counterintuitive centre of the argument, and it is worth spending time on.
The instinct when faced with a production constraint is to invest in anything that removes it. AI removes the production constraint on content volume, which is why adoption has been so rapid and so enthusiastic. But in removing that constraint, AI has inadvertently created a different and more serious problem: it has commoditised the aesthetic of competence.
Just a few years ago, a well-designed website immediately separated a business from most of its competitors. A polished brand identity, a professionally written landing page, a consistent visual identity — these things took time, money and skill to produce, and their presence was a meaningful quality signal. AI has collapsed that signal. Every brand can now generate a logo, a website, a brand guidelines document and a month’s worth of social content in the time it once took to brief a designer on a single asset. When everything looks equally polished, the visual quality of your output stops doing any differentiating work at all.
A 2025 Google and Kantar study found that differentiation accounted for 57% of retailers’ future growth and was the strongest long-term driver of commercial performance — stronger than awareness or salience. The study confirmed a principle that strategists have known for a long time: familiarity can get you into the consideration set, but it does not give people a reason to choose you once you are there. Differentiation does that. And differentiation is precisely what AI, used without strategic intent, tends to erode.
The most sophisticated organisations are aware of this. One of the most notable branding trends of 2026 is a deliberate move away from the recognisable “AI look” as more sophisticated teams realise what audiences have already understood: customers do not remember perfect. They remember distinctive.
What does a coherent USP look like in an AI-saturated market?
The concept of a unique selling proposition is not new. What is new is the environment in which it has to operate — and how the AI saturation problem has shifted the burden of proof.
In a market where every competitor can produce professional-looking content at negligible cost, a USP that exists only in your marketing language is no longer sufficient. Audiences have developed a finely tuned sensitivity to generic positioning, and they discount it accordingly. The USP has to be grounded in something real: a genuine capability, a proprietary approach, a specific audience insight, an original point of view on the problems your clients face.
For technology and financial services firms, this means moving beyond the descriptors that every firm in your sector claims — innovative, client-centred, trusted, results-driven — and into the territory of specific, demonstrable, verifiable differentiation. What do you do that a client cannot easily find elsewhere? What do you know from working with your clients that no AI model can replicate? What perspective do you hold that contradicts the conventional wisdom in your sector?
Those questions are not easy to answer. That is the point. The things that are easy to say are the things every competitor is already saying. The things that require genuine thought, genuine experience and genuine conviction are the things that AI cannot generate — and therefore the things that are becoming more valuable as AI adoption saturates the market.
A differentiator is the underlying quality that makes you distinct. A USP is how you express that quality to customers. The distinction matters because many organisations have a clear differentiator but have never translated it into language that a prospect can immediately grasp and remember. That translation — from strategic reality to compelling, memorable, specific articulation — is one of the highest-value activities a marketing leader can undertake in 2026.
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How do the best marketers use AI without losing their strategic edge?
The organisations extracting genuine value from AI share a common characteristic: they treat it as a capability that accelerates execution, not one that replaces strategy. The work that comes before AI is involved — understanding the audience, defining the position, determining what needs to be said and why — is still entirely human. What AI handles is the downstream production work that previously consumed the time and resource that should have been spent on that upstream thinking.
One marketing agency that went deep on AI content automation in 2024 found itself reversing course in the third quarter of 2025. It had used AI to handle research synthesis, outline generation and SEO optimisation — and had allowed human creativity and editorial judgement to atrophy in the process. The cost of reversal included retraining, updated processes and months of diminished competitive positioning while rebuilding credibility after the switch. The lesson was not that AI was the wrong tool. It was that the hybrid model — AI handling research synthesis, outlines and optimisation, while human writers focus on original analysis and brand voice — was always the right approach, and the shortcut of removing human editorial input was not actually a shortcut at all.
The frameworks that are working well in 2026 consistently follow the same logic. AI handles research synthesis, first-draft generation, optimisation, scheduling and performance analysis. Human expertise handles strategic direction, editorial quality, original insight, brand voice and the judgment calls that determine whether a piece of content is worth publishing at all. The 90/10 distinction — AI does 90% of the production work, human expertise governs the 10% that determines quality — appears repeatedly in the accounts of organisations getting measurable results from AI adoption.
The framing that is most useful: AI increases the speed of execution. It does not replace the strategic thinking that execution serves.
What role does creative quality play when everyone has access to the same tools?
This is where the attention economy discussed in our previous edition intersects directly with the AI differentiation problem — and where the stakes are highest for marketing leaders in technology and financial services.
When AI makes it possible to produce a hundred pieces of content at the cost previously required to produce ten, the temptation is to produce a hundred. But the question that rarely gets asked before that decision is made is: what does the audience do with a hundred pieces of content from your brand, most of which are competent but none of which is remarkable?
The evidence from attention research is consistent: audiences are not rewarding volume. They are rewarding distinctiveness. The variation in brand recall between good creative and average creative can be as much as 17 percentage points. Optimised creative drives 49% higher attention than unoptimised versions of the same ad. And the production quality threshold that once separated good brands from average brands has largely been eliminated by AI — which means that creative distinctiveness, not creative polish, is now the variable that determines whether any individual piece of content does useful work.
There is one thing AI cannot generate: your experience. Your client stories, your proprietary observations, the patterns you have noticed from working in your sector, the things you believe that most people in your industry would push back on. That is the raw material that elevates content from accurate and generic to worth trusting. And it is available exclusively to organisations that have built something real and know how to talk about it.
Nearly two-thirds of marketing leaders in HubSpot’s 2026 State of Marketing research said they need more distinctive, human-centred content to stand out in an increasingly automated landscape. The irony is that the path to that distinctiveness runs directly through the strategic work that AI is most often used to bypass.
How should you think about targeting when AI is doing the heavy lifting?
Precise targeting is one of the areas where AI genuinely delivers on its promise — and where the combination of strong strategic positioning and AI-powered targeting creates a meaningful multiplier effect.
The businesses winning with AI in paid media right now are not necessarily the ones with the best creative or the biggest budgets. They are the ones who have done the upstream strategic work — defining their audience with genuine precision, understanding the specific problems those people are trying to solve, and building creative around a clearly articulated point of view — and then used AI to accelerate and refine the distribution of that thinking to the right people at the right time.
Lookalike targeting built from high-quality first-party data, AI-powered bid optimisation, creative personalisation at scale across well-defined audience segments — these tools are genuinely powerful. But they are powerful in proportion to the quality of the strategic inputs they are working with. An AI system can optimise the distribution of a message with remarkable efficiency. It cannot improve the message itself. If the creative is generic, the targeting will simply put generic content in front of more people, more efficiently, at lower cost. That is not an advantage. It is a faster route to a worse outcome.
The implication for marketing leaders is practical: the return on investment from AI-powered targeting is directly proportional to the quality of your brand strategy, your audience understanding and your creative positioning. Investing in AI tools without investing in the strategic foundations they depend on is one of the most common and most expensive mistakes in marketing right now.
What does “safe positioning” cost you in a crowded market?
Safe positioning — the tendency to describe your brand in terms that are broadly true, broadly acceptable and broadly indistinguishable from every competitor in your sector — is a rational response to the fear of saying something wrong. It is also one of the most damaging strategic decisions a brand can make in a market saturated with AI-generated content.
When every competitor is saying the same things in the same register, a brand that chooses to be precise, direct and genuinely opinionated stands out almost by default. The contrast effect is real and measurable. Audiences notice when a brand has a point of view. They notice when a brand is willing to say something specific rather than retreating to a position that no one could disagree with.
The market position counts that have been tracked across the technology sector provide a stark illustration of this. Chiefmartec counted 15,505 martech products in 2026 — a market where undifferentiated positioning is commercially catastrophic because buyers have too many alternatives and too little time to investigate each one. In that environment, a brand that positions itself generically is not playing it safe. It is opting out of the consideration set entirely.
The brands in technology and financial services that are building genuine market positions are doing so through specificity: specific audiences, specific problems, specific expertise, specific points of view on how the world works and what that means for the clients they serve. None of that specificity is generic. All of it takes real thinking to develop. And none of it can be produced by an AI tool that has not been given a genuinely distinctive brief to work from.
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How does AI content saturation affect your AEO and organic visibility?
The AI content saturation problem has a specific and underappreciated implication for brands trying to build visibility in AI-generated search answers — and it runs counter to the instincts of many content teams.
AI answer engines — ChatGPT, Gemini, Claude, Perplexity — are not passive repositories of content. They actively evaluate the credibility and distinctiveness of what they cite. Content that is structurally indistinguishable from other content on the same topic, that adds no original data or perspective, and that offers no proprietary insight, is consistently disadvantaged in AI citation environments relative to content that is specific, well-evidenced and genuinely useful.
The practical implication: the same features of content that make it distinctive and valuable to human audiences also make it more likely to be cited by AI models. Clear authorship, original data, proper citations, and a specific rather than generic treatment of any given subject are credibility markers for human readers and quality signals for AI engines simultaneously. The content strategy that wins with human audiences in 2026 and the content strategy that wins in generative engine discovery are, increasingly, the same strategy.
For technology and financial services firms that have invested in high-volume AI content pipelines without editorial quality controls, the AEO implications are worth taking seriously. The models that determine brand visibility in AI search are actively penalising the generic outputs that AI makes it easy to produce, and actively rewarding the distinctive, authoritative content that requires genuine strategic investment to create. The shortcut that seems to reduce cost actually reduces visibility.
What is the right balance between AI speed and human strategic thinking?
The most useful way to think about this is not as a balance at all, but as a sequence. Strategic thinking comes first and determines everything. AI execution comes second and accelerates everything. When that sequence is reversed — when AI generates the content and strategic thinking is applied retrospectively to edit it — the result is almost always weaker than when the sequence is right.
The strategic work that belongs exclusively to humans is specific. It includes: defining what makes your organisation genuinely different from the alternatives your prospects are considering; understanding your audience’s actual problems rather than the problems AI models assume they have based on what has been written about them elsewhere; developing a point of view on your sector that is worth holding, not merely worth saying; and making the editorial judgement calls that determine which content ideas are worth pursuing and which are a distraction from the things that matter.
The execution work that AI handles well are equally specific. It includes research synthesis and competitive analysis; first-draft generation from well-defined strategic briefs; SEO and structural optimisation; scheduling and distribution; performance analysis and reporting; and the rapid iteration of creative variants from strong creative platforms.
The organisations that have got this right describe a compound effect over time. As AI handles more of the execution work, the human strategic time that is freed up can be reinvested in the upstream thinking that determines what gets executed. The strategy gets sharper. The briefs get better. The AI outputs get more distinctive. And the cycle compounds in a direction that generic AI adoption — without strategic investment — cannot replicate.
Where do you start if your marketing has drifted towards generic?
The honest answer: start with strategy, not tools.
Before revisiting your AI workflow, your content calendar or your distribution channels, spend time on the questions that AI cannot answer for you. What does your organisation know that your competitors do not? What do you believe about your sector that most of your competitors would push back on? What specific problem do you solve better than anyone else, for which specific type of client, in which specific circumstances?
Those answers are the foundation of everything that follows. They determine the brief that goes into the AI tool. They determine the editorial bar that AI output has to clear before it is published. They determine the creative direction that makes your paid media distinctive rather than interchangeable. And they determine the content strategy that builds genuine AEO visibility rather than producing more generic content that AI search engines will quietly deprioritise.
From that strategic foundation, the practical build looks like this. Define your differentiated position — not in the language of your internal strategy documents, but in the language of a specific prospect encountering your brand for the first time. Build your AI content workflow around that position, with clear editorial standards that generic output cannot pass. Invest in the creative quality that makes your distinctive position visible — visually, in your copy, in your hooks, in everything a prospect sees before they decide whether to stop or scroll past. And target precisely, using the first-party data and AI-powered tools that extend the reach of that well-defined message rather than amplifying a generic one.
At SK, we work with technology and financial services clients to develop the strategic foundations that make AI-powered marketing genuinely effective — from positioning and brand strategy through to content marketing, paid media, lead generation and AEO. If your marketing output has started to look like everyone else’s, we would be glad to help you work out why — and what to do about it.
👉🏻 Ready to build AI-powered marketing that actually stands out? Get in touch with SK today.
Summary
- AI adoption is now a baseline, not a differentiator. McKinsey’s 2025 survey found 79% of organisations use generative AI, up from 33% in 2023. With near-universal adoption, the technology itself cannot be the source of competitive advantage — only 6% of organisations are extracting genuine bottom-line value from it.
- The AI content flood is a real and measurable problem. The biggest concern among marketers using AI is that content sounds generic, cited by two-thirds of surveyed practitioners. Mentions of “AI slop” grew ninefold in 2025, and consumer preference for AI-generated creator content collapsed from 60% to 26% in two years.
- Differentiation accounts for 57% of future growth, according to a 2025 Google and Kantar study — and it is precisely what volume-first AI content strategies tend to erode. Customers do not remember perfect. They remember distinctive.
- The right sequence is strategy first, AI second. Organisations treating AI as a production layer rather than an execution accelerator for clear strategy consistently produce more output with less impact. The strategic work — positioning, audience understanding, editorial direction — remains entirely human.
- A coherent USP must be grounded in something real. Generic descriptors — innovative, client-centred, trusted — are the claims every competitor makes, and AI makes it easier than ever to produce them at scale. Genuine differentiation requires specific, demonstrable, proprietary claims that require real thinking to develop.
- Creative quality has never mattered more. When production polish is no longer a differentiator, creative distinctiveness becomes the variable that determines whether any individual piece of content does useful work. The variation in brand recall between good and average creative can exceed 17 percentage points.
- Safe positioning has a measurable commercial cost. In a market with 15,505 martech products and AI-saturated content across every channel, brands that position generically are not playing it safe — they are opting out of the consideration set.
- AI content saturation directly affects AEO visibility. AI answer engines actively disadvantage generic, indistinguishable content and favour specific, well-evidenced, authoritative material. The content strategy that wins with human audiences and the content strategy that wins in AI search are increasingly the same strategy.
- Targeting multiplies whatever strategy it is working with. AI-powered targeting is genuinely powerful, but its return is proportional to the quality of the strategic inputs — audience definition, creative positioning, message precision — that determine what gets distributed. Efficient distribution of a generic message is not an advantage.
- The fix is not to stop using AI. It is to invest more deliberately in the strategic foundations — distinctive positioning, genuine audience understanding, editorial quality standards — that make AI output worth having and worth distributing.
- For technology and financial services firms specifically, the buyers making consequential decisions over long sales cycles will not be persuaded by content that reads like every other firm in the market. AI can help you communicate genuine expertise at scale. It cannot create that expertise, or the strategic clarity to articulate it.
At SK, we help technology and financial services organisations build and execute marketing strategies that deliver measurable growth. From brand strategy and positioning to content marketing, paid media, SEO and AEO services, lead generation and AI marketing strategy, we work with ambitious firms who want to lead in their markets.
Talk to us about building a distinctively positioned AI marketing strategy →
How should technology and financial services firms approach AI-powered marketing in 2026?
For technology and financial services organisations, the AI marketing question is not whether to use AI — that decision has already been made at an industry level — but how to use it in a way that builds rather than erodes competitive position.
The starting point is an honest audit of your current AI marketing output. If you set aside the content you have produced with AI assistance over the past six months and asked a senior person in your sector to read it without knowing who produced it, would they recognise a distinctive voice, a consistent point of view, a specific understanding of the problems your clients face? Or would it read as competent but interchangeable — the kind of content that could plausibly have come from any firm in your sector?
That audit is uncomfortable. For many organisations, the answer reveals that AI adoption has accelerated output but diluted distinctiveness. The fix is not to stop using AI. It is to invest more deliberately in the strategic foundations that make AI output worth having: a clear articulation of your differentiated position, a genuine understanding of your specific audience, and an editorial quality bar that generic AI content cannot pass.
The technology and financial services sectors share a characteristic that makes this investment particularly high return: both are sectors where buyers are making decisions of genuine consequence, with significant due diligence, over long sales cycles. Those buyers are not going to be persuaded by content that reads like every other firm in the market. They are going to be persuaded by evidence of genuine expertise, clear thinking about the problems they face, and a demonstrable point of view on what the right approach looks like. AI can help you communicate that expertise at scale. It cannot create it.