The question I am asked most often about artificial intelligence is whether it will replace marketers. I think that is the wrong question.
Not because the worry behind it is misplaced. It isn’t. I have spent my career inside marketing teams, and I have never seen a technology land the way this one has. Not because it arrived suddenly, but because of where it arrived. Every previous wave of marketing technology gave us new places to do the work or new ways to measure it. This one does the work. That is a different kind of arrival, and it is why so many capable people are quietly looking at their own role and wondering how much of it a machine could now do.
The question is wrong because it asks for a yes or a no, and the truth is not a yes or a no. It is a movement. Here is the claim this essay will try to earn: the centre of gravity in marketing is shifting, away from executing the work and toward orchestrating the growth the work exists to produce. And here is why, in eight words:
Execution is becoming abundant. Judgement is becoming scarce.
Hold that claim against the strangest finding I have seen in this year’s research. Gartner’s 2026 CMO Spend Survey, which polled 401 marketing leaders at the start of this year, found that nearly two thirds of marketers believe AI will transform their roles. Only 32 per cent believe they need to update their skills.
Sit with that gap for a moment, because it is doing a lot of work. Two thirds of a profession can see the wave. Two thirds of the same profession has concluded it applies to someone else. I don’t think that is arrogance. I think it is a category error. Most marketers are asking “will I be replaced?”, getting the correct answer, no, and mistaking it for reassurance. Very few marketers will be replaced outright. A great many will find that the part of the job that used to define their value is quietly becoming the part a machine does best. The value does not disappear. It moves.
To see where it moves to, and what that means for your career and your team, you have to see this moment for what it is: not a new tool, but the fourth structural shift in how marketing creates value. The first three, digital, attribution and the technology stack, built the profession you work in today. AI is the fourth, and it behaves differently from all of them. It is worth walking through the sequence, because the pattern the first three reveal tells you almost everything about what happens next.
Four Shifts, One Direction, Until Now
For most of its history, marketing was a generalist craft. Before digital, one person, or one small team, understood the customer, shaped the message, chose where it ran, and judged whether it worked. The tools were few and stable: print, broadcast, direct mail, the trade show, the sales force. What made a marketer valuable was not mastery of any surface, because the surfaces barely changed. It was judgement about customers and messages, exercised with patchy data and a long feedback loop. You ran the campaign, you waited, you argued in a meeting about whether it worked. The craft was slow, but it was whole. One mind held the entire system.
The first shift broke that wholeness: digital multiplied the surfaces. Search, then social, then mobile, then video, each arriving with its own mechanics, its own auction dynamics, its own metrics, its own learning curve. No one mind could hold it all any more, and organisations responded rationally. They specialised. The generalist gave way to the paid search specialist, the SEO lead, the automation manager, the social team. This was not a mistake or a fashion. It was the correct response to genuine complexity. When running a single channel well takes real expertise, and doing it badly wastes real money, you hire someone who does only that.
The second shift was attribution, and it was subtler, because it changed not what marketers did but what they were believed for. When digital made activity trackable, the monthly argument about whether the campaign worked became a dashboard. That bought marketing credibility it had never had, and I would not give it back. But it came with a cost we are still paying: what could be measured became what mattered. The channels that could prove their contribution attracted the budget, the headcount and the ambitious people. The work that compounds slowly and resists measurement, brand, memory, reputation, was quietly starved. Attribution did not just measure the work. It reshaped the work in its own image.
The third shift was the technology stack itself. When Scott Brinker published his first marketing technology landscape in 2011, it held roughly 150 tools. By 2024 it counted more than 14,000. A hundredfold increase in barely more than a decade, and every one of those tools needed someone to own it, integrate it, and justify its licence fee. Operating the machinery became a career in itself.
Step back and look at what those three shifts have in common. Each one moved marketing’s centre of gravity closer to execution. Each one made the profession more specialised, more measurable, more operational. Headcount, budgets, career ladders and status were all progressively reorganised around the ability to produce and run the work. Being good at marketing came to mean being good at executing a discipline within it. And to be clear, that structure served us well. The people who built their careers this way were responding correctly to the world as it was.
The fourth shift is AI, and here is the thing I most want you to see: it runs in the opposite direction. For thirty years, every structural change pushed value toward execution. AI is the first that pushes it back. The trouble is that today’s marketing team was designed for yesterday’s constraints. It was built for a world where execution was expensive, slow and scarce. AI changes exactly those constraints, and a structure built on constraints that no longer exist does not stay standing out of respect for its history.
The Economics Underneath the Noise
Set aside, for a moment, the demos and the discourse. The strategically important point is simpler and more durable than any feature list: AI collapses the cost and time of a large share of marketing execution.
This is no longer a claim you have to take on faith. In a 2023 experiment published in Science, MIT researchers gave 453 professionals realistic business writing tasks; those with access to an AI assistant finished 40 per cent faster and produced work that blind assessors rated 18 per cent higher. Harvard researchers working with Boston Consulting Group ran a larger field experiment: 758 consultants, realistic strategy and marketing tasks, and the group using a frontier AI model completed more tasks, faster, at quality rated 40 per cent higher than the control group.
I believe those numbers, because I have watched them happen. Work I once scoped in fortnights comes back in afternoons. The first draft, the variant set, the campaign summary, the first cut of a segment: the honest response of anyone who has run a team through the last two years is not scepticism about the productivity data. It is recognition.
But the number in the Harvard study that should actually change how you plan your career is not the speed figure. It is this: the biggest gains went to the bottom half of performers, whose output improved by 43 per cent, far more than the top performers gained. Think about what that means. AI is not a tool that makes the best people better, mostly. It is a tool that makes average execution nearly indistinguishable from good execution. The gap that justified the premium for competent production, the gap between the adequate specialist and the strong one, is compressing in front of us.
And when average output rises everywhere at once, something counterintuitive happens: the market stops paying for it. This is where basic economics tells you what no feature demo will. When a resource becomes abundant, its relative value falls. Not because it stops mattering. Water matters more than diamonds, and costs less, because scarcity is what commands a premium. For thirty years, the scarce resource in marketing was the ability to produce and run the work. That is the resource AI just made abundant.
Execution is becoming abundant. Judgement is becoming scarce.
Follow that sentence one step further and you get the whole restructuring. When production was the bottleneck, you needed many people spending most of their time producing. As production stops being the bottleneck, the constraint moves to a different question entirely: not who can make the work, but who can decide what work is worth making. Organisations will need fewer hours spent producing, and more spent deciding. Everything else in this essay follows from that.
Where the Value Is Pooling
If the economics are right, we should already see the redistribution in the labour market. We do, and it is worth being honest about what it looks like, because “AI is not replacing marketers” should be a conclusion drawn from evidence, not a comfort blanket.
The most careful employment study so far comes from Stanford’s Digital Economy Lab, where Erik Brynjolfsson and colleagues analysed payroll records covering millions of American workers. Their finding is precise and uncomfortable: since generative AI became widespread, early-career workers aged 22 to 25 in the most AI-exposed occupations, marketing squarely among them, have seen a roughly 16 per cent relative decline in employment. But the same study found that employment for experienced workers in those same occupations held stable or grew.
Read those two findings together, because together they are a map of where the value is going. The damage is not spread evenly across the profession. It is concentrated exactly where the job consists of executing well-defined tasks, which is what entry-level roles were built around, and which is what the models now do cheaply. Where the job consists of judgement built on experience, employment is holding. The labour market is not saying “marketers are finished.” It is saying, with unusual clarity, “we will keep paying for judgement, and we will stop paying for undifferentiated execution.” Indeed’s Hiring Lab, analysing the skills listed in millions of job postings, reached the same destination from a different direction: nearly half the skills in a typical posting can be transformed or heavily assisted by generative AI, fewer than one per cent can be fully performed by it, and the roles that remain shift, in their words, from doing the work to directing it.
This is what I mean when I say the centre of gravity is shifting. For a decade, a marketer’s value sat close to the work itself: producing assets, running campaigns, optimising channels. That is the layer AI is absorbing fastest. As it does, value moves to the layer above: understanding customers, defining strategy, allocating resources, integrating across functions, and making the commercial calls that decide whether any of the activity was worth doing.
Be careful how you read this, though, because it is not an argument against expertise, and it is emphatically not “specialists out, generalists in.” A world where execution is cheap is a world with more execution in it, not less, which means more need for people who deeply understand how a discipline behaves, where it breaks, and what good looks like when a machine produces it at volume. The Harvard experiment carries a warning here that I think about often. The researchers included a task that looked like something AI would handle well but sat just beyond its real capability. Consultants without AI got it right 84 per cent of the time. Consultants with AI got it right 60 to 70 per cent of the time. The tool made them faster, more confident, and wrong more often, because they stopped interrogating the output. The researchers called it falling asleep at the wheel. Cheap execution without expert judgement does not merely fail to add value. It subtracts value, at scale, with total confidence.
So the rising value is not in generalism. It is in the connective tissue. When every channel can be run faster and cheaper, the hard part is no longer running any single one of them. The hard part is deciding how they fit together: how paid, owned and earned combine, how brand and performance trade off, how this quarter’s number is weighed against next year’s growth. The scarce skill is connecting expertise across disciplines into one coherent commercial system, rather than a stack of well-run silos pointing in slightly different directions.
That is a different job from the one most marketing ladders were built to reward. And it has a name.
The Growth Orchestrator
Here is the shift stated plainly. Marketing is the orchestration of growth. It does not create growth in isolation, and it never did. It orchestrates a system that creates growth.
I want to earn that sentence rather than assert it, so picture a team I suspect you will recognise, because I have sat inside versions of it more than once. The paid media is well run. The content calendar ships on time. The automation flows fire. The SEO trend line points the right way. Every specialist is hitting their number, every dashboard is green, and the company still misses its revenue target. Everyone has done their job, and somehow the sum of the jobs does not add up to growth. When the post-mortem comes, no one in the room can quite explain why, because the explanation does not live in any single channel. It lives between them. Demand was captured efficiently but never created. The story sales told was subtly different from the story the campaigns told. The budget followed last quarter’s attribution instead of next year’s buyers. No individual was wrong. The system was.
That gap, between well-executed parts and a working whole, is where marketing’s real job has always lived, even when our org charts pretended otherwise. The growth system has six stages, and it is worth naming them now, because the essays ahead in this series will take each one in turn. It begins with market and customer understanding: knowing who buys, why, and what they are really weighing when they decide. That feeds strategy and positioning: choosing where to compete and what you intend to be remembered for. Then comes demand creation, building memory and preference among the buyers who are not yet in the market, and demand capture, converting the few who are. Then conversion support, arming sales so the promise survives contact with the buying committee. Then retention and expansion, keeping and growing the customers you fought to win. And the loop closes with learning, feeding what the market just taught you back into the understanding you started with. Marketing’s job is not any single stage. It is the coherence of all of them.
For thirty years we could not staff that job properly, because execution consumed everything. The channels had to be fed. The stack had to be run. The reporting had to be built. Coordination was the thing you did in the gaps, in the weekly meeting, in the planning cycle, if there was time, and there was never time. The profession’s deepest work was perpetually crowded out by its busiest work.
This is the possibility that AI actually opens, and it is why I refuse to read this moment as a diminishment. AI does not sit outside the growth system. It sits inside it, and it can now support nearly every stage: sharpening the research, drafting the positioning, producing the demand-creation work, optimising the capture, arming the sales team, personalising the experience. What it cannot do, structurally cannot do, is decide where to compete, how to position, what to prioritise, and how to weigh this quarter against the long term. It runs inside the system. Someone still has to conduct.
That someone is the growth orchestrator: the person who holds the whole system in view, decides where effort and money should go, and makes the trade-offs no single specialist and no model is positioned to make. Not the channel manager with a bigger title. A genuinely different role, closer to what a conductor does with an orchestra of virtuosos: not playing any instrument, but responsible for the only thing the audience actually hears, which is whether it all comes together.
And the timing is not incidental, because the buyer’s side of the system has become dramatically harder to serve with silos. Forrester’s 2026 buying research found the average B2B purchase now involves around 13 internal stakeholders and 9 external influencers, and that buyers increasingly begin their research inside AI tools, then validate what the machines tell them with sources they already trust: peers, communities, and the brands that had earned a place in their memory long before the buying process began. Your buyer is running an AI-assisted evaluation across a committee of twenty. A marketing function organised as independent channel silos cannot meet a buying process that behaves as a system. Only a system meets a system, and someone has to run yours.
Orchestration is also, now, a resource-allocation job in the most literal sense. Gartner reports AI already claims 15.3 per cent of the average marketing budget, while 56 per cent of CMOs say they lack the budget to execute their strategy at all. Scarce money plus abundant execution is precisely the condition under which allocation, not production, decides who grows. Someone has to choose what the AI spend displaces, which capabilities it builds, and how its contribution will be judged. Those are orchestration decisions, and in most organisations today, nobody fully owns them: the same Gartner survey found 70 per cent of CMOs naming AI leadership as a key goal while only 30 per cent say their organisation is ready to scale it. That gap is rarely a technology problem. It is a conducting vacancy.
Here is what I find energising rather than threatening about all of this. The orchestrator role is not a consolation prize for people whose real job got automated. It is the job the best marketers always wanted and were rarely allowed to do, the one buried under the execution load: to actually understand a market, actually shape where the business plays, actually decide. If you became a marketer because you wanted to grow something, rather than because you wanted to operate software, this shift is not taking your job away. It is finally offering it to you.
That system is the organising lens for the rest of this series, and it deserves more than a few paragraphs. We will give it an essay of its own and walk each stage in full. For now, hold the shape: a connected system that produces growth, that AI increasingly runs within, and that a human increasingly has to conduct.
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Judgement, and Taste
If evidence is more accessible than ever, analysis faster than ever, and execution cheaper than ever, then none of those is where your edge lives. What is left, and what does not automate cleanly, is the ability to decide between competing priorities when there is no objectively correct answer.
Take the most familiar trade-off in demand generation: how much to invest in creating future demand versus capturing the demand that exists today. The evidence here is about as good as marketing evidence gets. The Ehrenberg-Bass Institute’s work on mental availability shows why being remembered by buyers who are not yet in the market matters. John Dawes’s research with the LinkedIn B2B Institute, usually shortened to the 95:5 rule, gives the arithmetic: at any moment, roughly 95 per cent of business buyers are not in the market for your category, so most of your marketing works, if it works at all, by building memory for a purchase that has not started yet. Les Binet and Peter Field, analysing hundreds of campaigns in the IPA’s databank, showed the long-term cost of over-investing in short-term activation, and proposed a rough 60:40 balance of brand to activation, with their B2B work pointing nearer an even split.
Now notice what all of that evidence cannot do. It tells you what tends to work, on average, across categories. It cannot tell you how much to weight brand against activation in your market, for your category, against your targets, this year, and I can tell you from experience that the moment this question stops being academic is not comfortable. It arrives in a room, near the end of a hard quarter, when someone senior points at the brand line of the budget and asks what it did for pipeline this month. Every marketer who has held a budget knows that moment. The research is on your side, and the research is not enough, because the honest answer involves your cash position, your board’s patience, your competitor’s noise and your own conviction. An AI assistant can recite Binet and Field flawlessly. It cannot tell you whether your business can survive the eighteen months brand investment takes to pay back, because that answer is not in the training data. It is in you, or it is nowhere. Evidence informs. Judgement decides.
There is a second capability beside judgement, and we are about to need much more of it. Call it taste. Judgement determines what should be done. Taste determines how well it must be expressed, and whether this particular expression clears the bar. In a world where anyone can generate a competent asset in seconds, competent stops being a differentiator; the research says the floor is rising everywhere, for everyone, including your competitors. When the floor rises, the only advantage left is the distance between the floor and your ceiling. I have reviewed plenty of machine-drafted work this year that was fluent, on-brief, and entirely forgettable, fine, but not us, and the discipline of saying “not yet” to work that is merely fine is becoming one of the last durable advantages a marketer has. Taste is also the guard against falling asleep at the wheel: someone in the system has to be the person who looks at confident, plausible output and catches the one that is confidently wrong. In an AI-enabled team, that person is not a luxury. That person is the quality system.
What to Build If You Want to Stay Valuable
The obvious response to all this is to go and learn the tools. It is also the wrong first move, or at least an incomplete one. Tools change every few months, and fluency with this quarter’s features is the fastest-depreciating asset in your stack. It is telling that only 32 per cent of marketers believe they need to update their skills: the temptation for the minority who do act will be to close the gap with prompt tricks and tool tours, because those are easy to buy and easy to tick off. But the durable investment is to deepen the capabilities that make your judgement worth having in the first place. Five stand out.
First, customer and market understanding deeper than a model can infer from a prompt. AI can summarise the research you give it. It cannot sit in a sales call and hear the objection under the objection. Marketers who keep doing primary work, talking to customers, listening to calls, reading the market first-hand, hold an advantage over everyone whose understanding comes from prompting the same models as their competitors.
Second, commercial and financial literacy. Understand the business you sit inside: its economics, its margins, what a customer is worth and costs to acquire. The marketers who allocate resources and defend budgets are the ones who speak in commercial terms, and that conversation is getting harder as boards start asking what the AI spend is returning.
Third, the evidence base of marketing itself. Ehrenberg-Bass on how brands grow. Binet and Field on effectiveness. Behavioural science on how buyers actually decide, as opposed to how our funnel diagrams say they should. This knowledge compounds, because it is the raw material judgement is made from, and it is what lets you evaluate machine output rather than merely accept it.
Fourth, orchestration itself: the unglamorous, deeply human work of getting sales, product, finance and marketing pointed at the same growth thesis. Thirteen insiders and nine outside voices in the average buying group is also a fair picture of your internal reality. Systems are run by coalitions, and building coalitions is a skill no model has.
Fifth, and only now, AI fluency. It belongs on the list; working knowledge of what the tools do well and where the frontier currently runs is becoming table stakes. The point is not to skip it. The point is to sequence it. AI fluency multiplies the value of judgement, market knowledge and commercial skill, and multiplies nothing when they are absent.
This is also the honest part, so let me be honest. This shift is not painless, and it is not automatic. Marketers whose identity is built on executional craft are the most exposed, and the employment data says the squeeze is already real at the entry level, which means leaders have a second obligation the essay above implies but I want to make explicit: the old bottom rung of the ladder is gone, and we will have to build our juniors a new one, deliberately, or the next generation of judgement never gets formed. Moving up into orchestration is a real change in skill, not a title bump. Not everyone will make the move, and pretending otherwise helps no one. But the direction of travel is clear enough to act on now, while it is still a choice rather than a scramble.
The Work, Revealed
So, will AI replace marketers? It was the wrong question all along, and by now you can see why. AI will keep removing tasks, and some of those tasks have defined entire jobs. That is real, and it deserves respect rather than a wave of the hand.
But a task is not the work. Marketing has always been about creating value for customers and growth for the organisations they buy from. Every structural shift before this one, digital, attribution, the stack, buried that purpose a little deeper under the mechanics of doing. AI is the first shift that digs it back out. Strip away the drafting, the assembling, the reporting, the operating, and what remains is what was underneath the whole time: understanding markets, making sound decisions under uncertainty, and bringing people, ideas and capabilities together in pursuit of growth that lasts. The machine has not automated the work. It has revealed it.
So if you are carrying the worry, and most of us are, let me offer you something sturdier than reassurance. The worry comes from looking down at your tasks and watching some of them walk away. The hope comes from looking up at the work, and remembering that the tasks were never why you chose this. You did not become a marketer to operate software. You became one to understand people, to make calls that matter, to grow something. For thirty years the profession asked you to do that in the gaps between the busywork. The busywork is leaving. What remains is the job you actually wanted.
Organisations rarely remember who executed the work. They remember who changed the trajectory of the business.
Execution is becoming abundant. Judgement is becoming scarce. The centre of gravity is moving toward the people who can decide. Make sure you are standing where it lands.
This is the second piece in a series on making better marketing decisions in an AI-enabled world. If it was useful, follow along: the essays ahead go deeper on the growth system underneath all of this, and on how to build the judgement the next decade will pay for.
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Sources
Gartner, 2026 CMO Spend Survey, published May 2026. 401 CMOs and marketing leaders surveyed January to March 2026; AI at 15.3 per cent of marketing budgets; 70 per cent name AI leadership a key goal; 30 per cent ready to scale AI capabilities; 56 per cent report insufficient budget; roughly two thirds of marketers expect AI to transform their roles while 32 per cent believe they need to update their skills.
Shakked Noy and Whitney Zhang, “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence”, Science, 2023. 453 professionals; 40 per cent faster completion, 18 per cent higher rated quality on professional writing tasks.
Fabrizio Dell’Acqua et al., “Navigating the Jagged Technological Frontier”, Harvard Business School working paper with Boston Consulting Group, 2023 (published in Organization Science, 2025). 758 consultants; faster completion and quality rated 40 per cent higher inside the frontier; bottom-half performers improved 43 per cent; accuracy fell from 84 per cent to 60-70 per cent on the outside-frontier task.
Erik Brynjolfsson, Bharat Chandar and Ruyu Chen, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence”, Stanford Digital Economy Lab, 2025. ADP payroll data; ~16 per cent relative employment decline for workers aged 22 to 25 in the most AI-exposed occupations; stable or growing employment for experienced workers in the same occupations.
Indeed Hiring Lab, “AI at Work Report 2025: How GenAI is Rewiring the DNA of Jobs”, September 2025. ~46 per cent of skills in a typical US job posting subject to hybrid or full transformation; fewer than 1 per cent fully performable by GenAI today; workers shifting from doing the work to directing the work.
Forrester, “The State of Business Buying, 2026”, January 2026. Average buying group of ~13 internal stakeholders and ~9 external influencers; AI tools increasingly the starting point of buyer research; buyers validating AI-generated answers with trusted human sources.
John Dawes, Ehrenberg-Bass Institute for Marketing Science with the LinkedIn B2B Institute, “The 95:5 Rule”, 2021. At any given time, up to 95 per cent of business buyers are not in the market for a given category.
Les Binet and Peter Field, “The Long and the Short of It”, IPA, 2013 (IPA effectiveness databank); and Binet and Field with the LinkedIn B2B Institute, “The 5 Principles of Growth in B2B Marketing”, 2019. ~60:40 brand-to-activation balance overall; closer to an even split indicated for B2B, labelled tentative by the authors.
Scott Brinker, chiefmartec, Marketing Technology Landscape Supergraphics, 2011-2025. ~150 tools in 2011; 14,106 in 2024; 15,000+ in 2025.