Every marketing advantage dies by becoming abundant. AI is the fastest cycle yet. It leaves judgement and taste scarce.
AI did not make marketing mediocre. Marketing was already mediocre. AI made mediocre marketing almost free.
That distinction decides what you do next. If AI caused the problem, the fix is a policy about AI. If AI removed the cost of a problem the industry already had, the fix is a harder conversation about what your marketing was ever good at.
Start with the figures. Eighty-seven per cent of B2B marketers say AI has made them more productive. Fifty-eight per cent say their content quality has improved. Fewer than two in five say their content is performing better. All three come from the Content Marketing Institute and MarketingProfs, surveying 1,015 B2B marketers in mid 2025. Quality is up, output is up, productivity is up, and results are flat.
This is not an essay about whether AI is making marketing worse. It is an essay about what happens to advantage when competence becomes free, and what that leaves for a marketer to be paid for.
The flat results are not a paradox. They are what happens when everyone gets better at the same time. Improvement that is universally available stops being improvement and becomes the new floor.
Advantage is not a property of a capability. It is a property of that capability’s scarcity.Advantage is not a property of a capability. It is a property of that capability’s scarcity.
Every marketing advantage dies the same way
Think about what has delivered real advantage in marketing over the last thirty years, and when each one stopped.
Having a website. Understanding how search worked. Buying media efficiently. Owning a marketing automation stack. Publishing consistently. Running paid social before your category noticed it existed.
Every one of those was a real edge. Every one is now hygiene. And each died the same death, which had nothing to do with the capability getting worse. It got easy.
The industry keeps missing this because it keeps mistaking the capability for the advantage, then acting surprised when the returns evaporate. Media buying threw off outsized returns while it required specialist knowledge and relationships. Programmatic made competent buying available to anyone with a budget, and the advantage moved on. Search threw off outsized returns while most companies did not understand it, and then everyone hired for it. Marketing automation was a moat until the moat came in a subscription. Nothing that can be installed is a moat.
Notice too that the half-life keeps shortening. Websites took years to travel from advantage to table stakes. Marketing automation moved faster. Content marketing faster again. Generative AI has compressed the cycle to a couple of years.
By the time the Content Marketing Institute fielded its survey in mid 2025, 89 per cent of the B2B marketers using AI were already using it to generate or optimise copy. A capability at that level of adoption is not an advantage. It is a baseline. If your team is still writing the business case for AI adoption, the thing you are building the case for stopped differentiating anybody some time ago.
This is the honest answer to “how do we get an edge from AI”, and it is not the one people want. You don’t, not from adoption. Adoption is the cost of staying in the game. The edge has already moved to whatever AI left scarce, and the rest of this essay is about finding it.
Adoption is now the cost of entry. The advantage is in what AI cannot make abundant.
The mediocrity came first
Before blaming the tools, look at what marketing was producing when the tools were expensive.
System1, working with LinkedIn’s B2B Institute, tested 1,600 B2B ads with around six million people worldwide over four years. Seventy-five per cent scored one star or less on System1’s emotional response measure, the level that contributes effectively nothing to long-term market share growth. Not one of the 1,600 reached five stars.
That was 2021. That was work with budget behind it, agencies attached and senior sign-off. It still landed as noise.
System1’s later research with eatbigfish and Peter Field, The Extraordinary Cost of Dull, put a figure on the flatness. In the UK, 60 per cent of responses to B2B television advertising registered as emotionally neutral, against 52 per cent for advertising overall. B2B is the duller category by measurement, not just by reputation. The same research, looking at US advertising generally, found that at the same level of excess share of voice non-dull advertising delivered around 1.3 points of annualised share gain while extremely dull advertising delivered around 0.1. An order of magnitude of growth, decided by whether the work registered at all.
Peter Field’s work for the IPA traced the same decline across almost 600 case studies. Creatively awarded campaigns were roughly twelve times as efficient as non-awarded campaigns in the twelve years to 2008. By 2018 that advantage had fallen below four times. Over the same period the share of awarded campaigns that were short term rose from around 5 per cent to almost 40 per cent.
None of that required a language model. The industry manufactured its own mediocrity through short-termism, measurement pressure and the steady substitution of activation for brand, which is the argument the previous essay in this series makes in full.
What generative AI changed is the price. Producing forgettable work used to cost a budget, a team and a quarter. Now it costs a prompt.
AI lowered the cost of being forgettable, and volume responded accordingly.
Average is now infinitely scalable
There is a mechanism behind the convergence, and it is better documented than most of the commentary suggests.
Anil Doshi at UCL and Oliver Hauser at the University of Exeter randomised 293 writers across three conditions: no AI, one AI-generated idea, and five. Six hundred independent evaluators assessed the results, published in Science Advances in July 2024. Individual work got better. Novelty rose 5.4 per cent with one AI idea and 8.1 per cent with five; usefulness rose 3.7 and 9.0 per cent. Collective similarity between the stories rose too, by 10.7 per cent of the measured range with a single AI idea.
Every writer improved. The body of work became more alike. For one marketer that reads as a win, because your draft beats the one you would have written alone. For a category it reads as convergence, because every competitor’s draft improved in the same direction at the same time.
There is a complication worth knowing. A University of Michigan study with more than 800 participants across 40 countries found the opposite effect: high AI exposure increased collective idea diversity. The designs differ in one respect. Doshi and Hauser gave people AI output to work from. Michigan used AI to surface ideas participants would not otherwise have reached. Substitution converges. Expansion diverges. Which one you get is a decision about process, not a property of the technology, and most teams have never made that decision consciously.
The volume side is now measurable. Graphite, sampling 55,400 URLs from Common Crawl, estimates that roughly half of newly published web articles are primarily AI-generated, and has been reporting a figure near that mark for five consecutive quarters. Its detectors have published error rates, so treat the exact percentage cautiously. The important finding is not whether AI has technically crossed 50 per cent. It is that human and AI-generated articles are now being produced in roughly equal volume.
What happens to that content is the more interesting finding. Across 31,493 keywords, 86 per cent of the articles ranking in Google results were human-written. Only 7 per cent of top-position results were AI-generated, half the baseline rate. Citations inside AI answer engines split roughly 82 per cent human to 18 per cent AI. Production share and visibility share have come apart.
Meanwhile the surface being competed for is shrinking. Pew Research Center tracked 68,879 Google searches from a panel of 900 US adults. When an AI summary appeared, 8 per cent of visits produced a click on a traditional result, against 15 per cent when no summary appeared. One per cent clicked a link inside the summary itself. Ahrefs, comparing 150,000 keywords with AI Overviews against 150,000 without, reported in February 2026 a 58 per cent average drop in click-through rate for top-ranking pages, up from 34.5 per cent in its study the previous April. SparkToro, using Similarweb clickstream data, put the share of US Google searches ending without any click at 68 per cent for the first four months of 2026.
More competent content, produced faster, competing for a materially smaller surface.
And the audience is not neutral about it. The Nuremberg Institute for Market Decisions ran controlled experiments alongside a 3,000-person survey across the US, UK and Germany. Identical advertising rated lower on emotional impact when labelled AI-generated than when labelled human-made, and only a quarter of respondents thought they could recognise AI content in the first place. Which makes disclosure the wrong thing to be anxious about.
Undifferentiated work fails on its own terms, long before anyone forms a view about how it was made.
Differentiation was never the mechanism
Here the industry’s own framing gets in the way, and it is worth clearing out.
Almost every article about B2B sameness quotes a statistic along the lines of “86 per cent of B2B buyers see no real difference between suppliers”, usually credited to CEB and Google. We went looking for the primary source. No study reports that figure. The nearest origin is a 2013 CEB and Google paper which found that only 14 per cent of business decision makers saw a real difference between suppliers and valued it enough to pay for it. Somewhere along the way the second half of the question was dropped and the remainder inverted, and the result has circulated through agency blogs and conference decks citing each other for a decade.
Here is what is documented. Gartner surveyed more than 1,100 B2B customers in late 2020 and found 64 per cent could not distinguish one brand’s digital experience from a competitor’s. Gartner also names perceptions of difference between supplier offerings as one of three drivers of buyer confidence, and its earlier work found that buyers overwhelmed by high-quality information were 153 per cent more likely to settle for a smaller, less ambitious purchase than they originally planned. The information was good. Volume alone was enough to shrink the deal.
Now the deeper point, which cuts against how most B2B teams think about this entirely. In a 2007 paper in the Australasian Marketing Journal, Jenni Romaniuk, Byron Sharp and Andrew Ehrenberg showed a low level of perceived differentiation between competing brands across many categories and two countries. Buyers largely did not see meaningful differences. They bought anyway. The authors’ conclusion was to stop putting perceived differentiation at the centre of brand strategy and put distinctiveness there instead: unique associations that make a brand easily identifiable.
If distinctiveness rather than differentiation is the engine, then “everything sounds the same” describes a memory failure, not a positioning one. The question is identifiability. Does anything about your work make it retrievable later?
Retrievable when is the part that gets skipped. John Dawes’s work at Ehrenberg-Bass with LinkedIn’s B2B Institute is the useful frame here, shorthanded as the 95:5 rule: up to 95 per cent of business buyers are not in market at any given moment. Dawes presents that as a heuristic rather than a measured constant, so use it as a way of thinking and not as a number in a board deck.
The measured version comes from 6sense’s B2B Buyer Experience Report for 2025, drawing on nearly 4,000 responses. Ninety-five per cent of winning vendors were already on the buyer’s day-one shortlist. Around four in five deals went to the vendor preferred before any seller contact. Buyers reached first contact roughly 61 per cent of the way through their process, earlier than the 69 per cent recorded the year before. Companion research put 85 per cent of buyers as having prior experience with the vendor they selected. That is a statement about winners rather than about your odds, so do not turn it into a conversion rate. What it establishes is enough: by the time you reach a competitive evaluation, most of the outcome is already sitting in the buyer’s memory of you.
Which is where Romaniuk’s category entry points come in. Buyers retrieve brands by situation: the problem, the moment, the trigger. Attach yourself to a handful of the situations that matter and you get recalled when one occurs. Publish competent, unmemorable content attached to nothing in particular and you do not.
The real cost of sameness lands before the comparison. You were never in the set to be compared.
What abundance leaves scarce
Back to the question the first section left open. Every capability that becomes abundant stops conferring advantage. So what has survived every one of these cycles without becoming abundant?
Two things, and they are not the same thing, though the industry uses them interchangeably.
Judgement determines what should be done. Taste determines how it should be expressed.
Judgement is the commercial call: which market, which problem, which trade-off, what to stop doing. The expressive call comes later and answers a different question, about what makes the work land, what to cut, and what deserves to exist at all. Neither has ever been available in a subscription. You cannot install taste, and no prompt can make the final judgement for you.
The evidence for the judgement premium is strong and getting stronger. Fabrizio Dell’Acqua, Ethan Mollick, Karim Lakhani and colleagues ran an experiment with 758 consultants at Boston Consulting Group, since published in Organization Science. On tasks inside AI’s capability frontier, consultants with AI access completed 12.2 per cent more tasks, 25.1 per cent faster, at higher quality. On tasks outside that frontier, consultants with AI access were 19 percentage points less likely to produce a correct solution than those working without it.
Same tool, same people. The variable was whether the task sat inside the model’s competence or outside it, and nobody hands you a map of where that line falls. Finding it is a judgement call, and the study prices what getting it wrong costs. AI does not reduce the need for judgement. It raises the price of not having any, because a confident wrong answer produced in four seconds travels a great deal further than a slow one.
The labour market is already repricing this. Stanford’s Digital Economy Lab, using ADP payroll data covering millions of US workers, finds employment among 22 to 25 year olds in the most AI-exposed occupations now sits 19 per cent below where it would be had it kept pace with less exposed peers. Experienced workers show no comparable gap. When this series covered that research in the second essay, the figure was 16 per cent. In the August 2026 version it is 19, and the researchers note the divergence has widened steadily since they first documented it. The finding underneath the headline is the one to carry: declines concentrate where AI substitutes for human tasks. Where it complements, employment is flat or rising, particularly for experienced workers.
Now taste, which the industry treats as a soft word for something with a very hard price tag.
Go back to the Cost of Dull research. In its UK B2B sample, of roughly £103 million in advertising spend, more than £53 million sat in the extremely dull quartile. More than half of the money went into the worst quarter of the work. That is not a budget problem. Every one of those campaigns was funded, approved and produced to a professional standard. Somebody signed it off. What was missing was the discernment to look at competent work and say it is not good enough to be remembered, and the standing to say it before the money went out the door.
Taste is expensive to lack and impossible to buy. Anu Atluru put it plainly in a 2024 essay: “In a world of scarcity, we treasure tools. In a world of abundance, we treasure taste.” Marketing is now firmly in the second world, and most of its processes were built for the first.
There is a complication the profession has barely started to face. Judgement and taste are built by doing the work: running the campaign, defending the budget, watching a decision fail and understanding why. David Duncan argued in Harvard Business Review in February 2026 that AI is absorbing the repetitive early-career tasks which historically produced professional discernment. Put that next to the Stanford data and the shape is uncomfortable. Fewer entry-level roles, and less formative work inside the roles that remain, squeezes the pipeline that produces the one capability the market is now paying a premium for.
The scarce asset is getting scarcer. That is good news for anyone who already has it, and a serious problem for the profession.
Five decisions worth making now
Diagnosis without a decision is commentary. Here is what changes if you accept the argument:
Stop looking for advantage in adoption. A capability your competitors can buy this quarter is not, by itself, a strategy. Ask of anything you are about to invest in: how long before this is available to everyone in my category, and what happens to my position then? If the answer is under two years, you are buying hygiene, and should budget for it as such rather than pitching it as an edge.
Measure retrieval, not output. Volume metrics reward the one thing AI made free. If your reporting is built on pieces published, keywords ranked and sessions delivered, you have instrumented the part of the system that no longer differentiates you, in an environment where the click surface is contracting. Add a memory-side measure: whether your brand is retrieved for the buying situations that matter, tracked over time. It moves slowly and is harder to defend in a monthly review than a traffic chart, so make the case before you need it, not during a budget cut.
The same test applies to B2B thought leadership: judge it by whether buyers remember and trust you, not by how much you publish.
Decide your category entry points before you brief anything. Identify the buying situations you want to be retrieved for and keep the list short enough to be memorable. Filter each candidate on the three tests Romaniuk sets out: is the association credible for you, how much competition is there for it, and is it common enough among buyers to be worth owning. Everything you publish attaches to one of them or it does not get made. If your content calendar is currently a list of topics, converting it to a list of situations is the highest-leverage change available to you this quarter.
Move AI upstream of the draft. The homogenisation pattern comes from taking AI output as a starting point and polishing it. The diversity pattern comes from using AI to reach options you would not have reached alone. Same tools, different placement in the process. Use it to widen the option set, pressure-test a position, argue against your own case and find the evidence that contradicts you. Then reject the first plausible output as a matter of routine. This is slower per asset, and it only works if someone in the room has the standing to say no. Both of those are the point.
Protect the apprenticeship in your own team. If the Stanford pattern reaches marketing and entry-level work keeps thinning, an individual team develops a judgement gap long before the profession does. Hand juniors decisions rather than tasks. Let them own a call, defend it, and be wrong occasionally with support. The cost is short-term output. Weigh it against a team that can operate the tools and cannot tell when the tools are wrong.
If you cannot say which of these your team has already decided, adoption is still doing your strategy for you. If you can, you are competing on the two things AI has not made free.
What this comes down to
Every marketing advantage of the last thirty years died by becoming abundant. Websites, search, media buying, automation, content. AI is running the same cycle faster than any of them, and adoption crossed into hygiene while most teams were still building the business case.
Competent execution is no longer where sustainable advantage lives. It cannot be, because competent execution is getting cheaper and more widely available by the day.
What is left is the judgement to decide what deserves to exist, and the taste to make it worth remembering. Neither can be bought, installed, subscribed to or prompted. Both are built slowly, by people doing the work, which is why the supply is tightening at the exact moment the price is going up.
We believe marketing is a commercial discipline, not a promotional service. Its job is to be retrieved when a buyer’s situation arises, and no volume of competent content does that on its own.
We believe every capability that can be bought will be bought by everyone, and that advantage lives in what remains scarce afterwards: the judgement to choose and the taste to make it land.
Above all, we believe those two capabilities are built by doing the work, and that a profession which automates away its own apprenticeship will pay for it later.
Marketing was already mediocre. AI just made mediocre free. That is an uncomfortable place for the profession, and an extraordinary opportunity for anyone willing to build the thing that does not scale.
Back2Marketing exists to help marketers build that thing.
Evidence informs. Judgement decides.
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And if you want to build the judgement, not just the output, Back2Marketing’s B2B Marketing Fundamentals course teaches marketing from first principles.
Sources
Content Marketing Institute and MarketingProfs, B2B Content and Marketing Trends: Insights for 2026, October 2025. 1,015 B2B marketers surveyed June to August 2025. 87 per cent report improved productivity; 58 per cent improved content quality; 39 per cent improved content performance; among marketers using AI, 89 per cent use it to generate or optimise written copy. The productivity and performance figures are separate items and are not presented as a single computed gap.
System1 and LinkedIn’s B2B Institute, research on B2B creative effectiveness, reported in Marketing Week, January 2021. 1,600 B2B ads tested with approximately six million people worldwide over four years; 75 per cent scored one star or less on System1’s measure; none reached five stars.
System1, eatbigfish and Peter Field, The Extraordinary Cost of Dull, July 2024. 60 per cent of responses to UK B2B television advertising neutral against 52 per cent for UK television advertising overall; of roughly £103 million in UK B2B advertising spend analysed, more than £53 million sat in the extremely dull quartile; for US advertising generally, annualised share gain of around 1.3 points for non-dull advertising against around 0.1 for extremely dull at equivalent excess share of voice.
Peter Field, The Crisis in Creative Effectiveness, IPA, June 2019. Almost 600 IPA case studies, 1996 to 2018. Creatively awarded campaigns around twelve times as efficient as non-awarded in the period to 2008, falling below four times by 2018; share of awarded campaigns that were short term rose from around 5 per cent to almost 40 per cent.
Anil R. Doshi and Oliver P. Hauser, Generative AI enhances individual creativity but reduces the collective diversity of novel content, Science Advances, July 2024. 293 writers randomised across three conditions, 600 evaluators. Novelty up 5.4 per cent with one AI idea and 8.1 per cent with five; usefulness up 3.7 and 9.0 per cent; collective similarity up 10.7 per cent of the measured range with one AI idea.
Joshua Ashkinaze, Julia Mendelsohn, Li Qiwei, Ceren Budak and Eric Gilbert (University of Michigan), How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas, ACM Collective Intelligence Conference, 2025. More than 800 participants across 40 countries; high AI exposure increased collective idea diversity without affecting individual creativity.
Graphite, AI Now Writes as Many Online Articles as Humans Do, May 2026. 55,400 URLs randomly sampled from Common Crawl, classified by three detectors with published false-positive rates of 1.4 to 1.8 per cent and false-negative rates under 2 per cent. Primarily AI-generated articles at 50.9 per cent in Q4 2025 and 49.9 per cent in Q1 2026, near 50 per cent for five consecutive quarters. The essay reports comparable volumes rather than treating 50 per cent as a milestone.
Graphite, AI Content in Search and LLMs, October 2025. 31,493 keywords; 86 per cent of ranking articles human-written; 7 per cent of position-one results AI-generated; approximately 82 per cent of AI answer-engine citations human-written. Correlation only.
Ahrefs, AI Overviews Reduce Clicks by 58%, February 2026. 150,000 keywords with an AI Overview against 150,000 informational keywords without; 58 per cent lower average click-through rate for top-ranking pages as of December 2025, up from 34.5 per cent in the April 2025 study. Vendor research on its own data.
Pew Research Center, Google users are less likely to click on links when an AI summary appears in the results, July 2025. 68,879 searches tracked across a panel of 900 US adults, March 2025. 8 per cent click rate on traditional results with an AI summary present against 15 per cent without; 1 per cent on links inside the summary. General consumer search rather than B2B specific.
SparkToro with Similarweb clickstream data, In 2026, Less Than One-Third of Google Searches Still Send a Click, June 2026. 68 per cent of US Google searches ended without a click, January to April 2026. General consumer search rather than B2B specific.
Fabian Buder and Matthias Unfried, Nuremberg Institute for Market Decisions, Transparency Without Trust, NIM Insights, 2024. 3,000 respondents across the US, UK and Germany plus two controlled experiments. Identical advertising rated lower, especially on emotional aspects, when presented as AI-made; 25 per cent believed they could recognise AI-generated content, a confidence measure rather than a test of ability.
Gartner, press release, May 2021. Survey of more than 1,100 B2B customers, December 2020. 64 per cent cannot tell the difference between one B2B brand’s digital experience and another’s; perceptions of difference between supplier offerings named as one of three statistically significant drivers of customer confidence.
Gartner, press release, July 2019. Survey of more than 1,000 B2B customers. Customers facing conflicting high-quality information are 153 per cent more likely to settle for a course of action smaller and less disruptive than originally planned.
CEB Marketing Leadership Council with Google and Motista, From Promotion to Emotion, 2013. Approximately 3,000 B2B purchasers, 36 brands. Only 14 per cent of business decision makers saw a real difference between suppliers and valued it enough to pay for it; the origin of the widely misquoted “86 per cent” statistic.
Jenni Romaniuk, Byron Sharp and Andrew Ehrenberg, Evidence concerning the importance of perceived brand differentiation, Australasian Marketing Journal, 2007. Low perceived differentiation across competing brands with purchase continuing regardless; distinctiveness proposed in place of differentiation. Not conducted in B2B categories.
Jenni Romaniuk, Ehrenberg-Bass Institute with LinkedIn’s B2B Institute, Category Entry Points in a B2B World, 2022. Buyers retrieve brands by buying situation; candidate entry points are screened on credibility, competition and commonness.
John Dawes, Ehrenberg-Bass Institute with LinkedIn’s B2B Institute, The 95:5 Rule, 2021. Presented by the author as a heuristic rather than a measured constant.
6sense, B2B Buyer Experience Report 2025, nearly 4,000 responses, and In It (Multiple Times) To Win It, November 2025. 95 per cent of winning vendors on the day-one shortlist; the vendor ranked first wins about 80 per cent of the time; first contact at 61 per cent of the journey, down from 69 per cent; 85 per cent of buyers had prior experience with the vendor selected. These describe winners rather than conversion odds.
Fabrizio Dell’Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani, Navigating the Jagged Technological Frontier, Harvard Business School working paper 2023, published in Organization Science, 2026. 758 BCG consultants, AI access randomised. Inside the frontier, 12.2 per cent more tasks completed, 25.1 per cent faster; outside it, 19 percentage points less likely to produce a correct solution.
Erik Brynjolfsson, Bharat Chandar and Ruyu Chen, Stanford Digital Economy Lab, Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence, August 2026 version. ADP payroll data. Employment of 22 to 25 year olds in the most AI-exposed occupations 19 per cent below the counterfactual, up from 16 per cent in the November 2025 version; no comparable gap for experienced workers; declines concentrated where AI substitutes rather than complements. Covers AI-exposed occupations across the economy rather than marketing specifically.
David S. Duncan, How Do Workers Develop Good Judgment in the AI Era?, Harvard Business Review, February 2026. Argument rather than original research.
Anu Atluru, Taste is Eating Silicon Valley, Working Theorys, September 2024. Argument rather than research; quotation as given.