Every generation of marketers is convinced it is living through the transformation of the profession. Ask a marketer in the 1950s and the answer would have been television. Ask in the sixties and it was the database, which turned mass broadcasting into targeting and direct response. Ask in the mid-nineties and it was the internet, which rewrote distribution and shifted power from brands to consumers. Since then it has been search, then social, then the smartphone that put all of it in our pockets.
Now it is artificial intelligence.
This is not another article about AI. It is an article about what becomes more important when the ability to create is no longer the constraint.
To be fair, the current wave deserves the attention it is getting. Generative AI is not simply another channel or software upgrade. It changes who can create, how quickly ideas can be developed and how much a small team, or one person, can accomplish.
A founder can describe an application in plain language and have a working prototype without first assembling a development team. A subject-matter expert can build a website, create a course, publish a newsletter and produce credible marketing materials with tools that were out of reach a few years ago. Marketers can lean on AI for research, analysis, writing, coding, design and the administrative grind.
The productivity evidence is real. In an experiment involving professional writing tasks, Shakked Noy and Whitney Zhang found that people using generative AI finished 40 per cent faster, and the quality of their output improved by 18 per cent. A separate field experiment with 758 Boston Consulting Group consultants found that AI users completed more tasks, worked faster and produced higher-quality work when the task sat within the technology’s capabilities.
These are not marginal gains. Capabilities once confined to specialists or large organisations are becoming available to almost anyone.
The barrier to building is falling
For most of modern business history, turning an idea into a product took serious resources. Software needed engineers. A credible brand needed designers and agencies. Manufacturing needed capital and suppliers. Reaching customers at scale needed distribution relationships or a large media budget.
Those barriers have not vanished. There is still a wide gap between a prototype and a secure, differentiated, commercially sustainable product. Expertise still matters. Quality still matters. Capital still matters.
What has changed is the starting point.
More people can now get from an idea to something tangible. The time and cost of testing a concept are falling, and work that once needed a team can increasingly be attempted by an individual. In one controlled GitHub experiment, developers using an AI coding assistant completed a programming task 55 per cent faster than those working without it. The technology did not remove the need for engineering skill, testing or judgement. It reduced the effort of turning an instruction into working code.
As the cost of creating something falls, more things get created. Markets will fill with applications, services, courses, newsletters, consultancies, products and content. Plenty will be competent. Some will be excellent. A good deal will be noise.
The supply of attention, however, does not increase simply because the supply of things competing for it has multiplied.
Lower barriers to production do not remove the commercial challenge. If anything they sharpen it. When building is easy, the hard part is knowing what to build, who to build it for and why it will matter to them. It is knowing how to make an unfamiliar product relevant, how to earn attention in a crowded market, and how to turn a first visitor into an audience, an audience into customers, and a promising product into a business that lasts.
None of those are technology questions. They are marketing questions.
The bottleneck hasn’t gone away. It has moved.
Most of the AI conversation ends at the moment of creation. Someone builds an app over a weekend, stands up a website in an afternoon or generates a month of content before lunch. The speed is impressive. The harder commercial questions are still sitting there.
A product can be technically possible without solving a problem that matters. A website can look credible without giving anyone a reason to visit. A business can publish constantly without building an audience. An automated email sequence can run flawlessly while pushing an offer nobody wants.
AI reduces the effort of bringing an idea into the world. What it cannot do is establish that enough people have the problem, are unhappy with the alternatives, are willing to change their behaviour and can be reached at a cost that makes sense. It cannot tell you whether the offer is distinctive enough, whether buyers will trust an unfamiliar provider or whether the economics will support growth.
Theodore Levitt warned about this decades ago in Marketing Myopia. Businesses become vulnerable, he argued, when they define themselves by the products they make rather than the customer needs they serve. The danger is greater still when a product can be built before the market has been understood.
The ability to build is not evidence of demand.
A founder may be able to launch globally on day one, but global availability is not global awareness. A product may be capable of serving a million customers, but scalability means nothing if the first hundred cannot be found. The fact that something exists does not mean anyone will notice it, understand it, remember it, trust it or choose it.
The bottleneck has moved from producing the product to creating the market conditions in which the product can succeed.
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Marketing was never just promotion
One reason this shift is misread is that marketing has slowly been reduced to a narrow set of visible activities.
In most organisations, marketing has become shorthand for advertising, social media, content, email, campaigns and lead generation. These are legitimate parts of marketing. They are not the whole discipline.
The American Marketing Association defines marketing as the activities and processes involved in “creating, communicating, delivering, and exchanging” offerings that have value. Communication is in that definition, but it sits alongside the creation and delivery of value. It is one of four verbs, not the headline.
The Four Ps make the same point. Product, Price, Place and Promotion describe a commercial remit far broader than communications. If you are newer to the profession, this is worth committing to memory: promotion is one component of the marketing mix. It is not the definition of marketing.
Philip Kotler spent a career establishing marketing as the process of understanding markets, identifying customer needs, creating value and choosing which customers an organisation is best equipped to serve. In that conception, marketing begins long before a campaign is briefed. At its fullest, it shapes the product, the market, the proposition, the route to market and the commercial model.
That places marketing upstream in the business. It should help decide which problems are worth solving, how a market should be understood, which customers represent the best opportunity and why the organisation has a credible right to win. It should have a voice in the offer, the price customers will accept, the way the product is distributed and the position the business can occupy in the customer’s mind.
Promotion comes after those decisions. It cannot compensate for their absence.
Yet that is rarely how it plays out. Marketers are often brought in only after the product has been built, the commercial target set and the launch date agreed. Product builds, finance prices, sales picks the audience, and marketing is asked to generate demand. In many B2B companies it is worse than that: the marketing department is treated as a support function for Sales.
When marketing is confined to the final stage, its job is no longer to help the organisation understand and shape the market. Its job is to communicate decisions that have already been made.
AI did not cause this narrowing. It started much earlier.
I’d argue that digital marketing did a great deal to accelerate it.
How digital marketing changed what we valued
Let’s be clear: digital marketing brought enormous benefits. It expanded access to customers, made experimentation faster and allowed small businesses to reach markets that were previously out of range. It created new forms of customer experience and gave marketers a level of visibility into behaviour and performance that earlier generations could only guess at.
It also made marketing accountable. For organisations that had struggled to connect marketing spend to commercial outcomes, that was a real advance.
But every new capability changes what people pay attention to. Digital platforms delivered an immediate, constant stream of measurable activity: impressions, clicks, open rates, conversions, leads, cost per acquisition, return on ad spend. Because those numbers were visible, fast and easy to put on a slide, they became the easiest things to report, optimise and defend.
The problem was never measurement. The problem was letting what could be measured immediately define the value of marketing.
Les Binet and Peter Field identified this tension in their analysis of the IPA Effectiveness Databank. Their research distinguished short-term activation, which converts demand that already exists, from long-term brand building, which creates future demand and produces broader commercial effects over time. They also warned that growing reliance on very short-term online measures would undermine long-term effectiveness. Most of the evidence since suggests they were right.
Digital did not make strategy unnecessary. It made execution unusually absorbing. There was always another campaign to launch, another platform to learn, another audience to optimise, another dashboard to update, another conversion point to improve. Marketing teams organised themselves around channels and systems, and professional expertise became closely tied to the ability to operate them.
Over time, channel choices crowded out market insight and customer choices. Optimisation displaced diagnosis. A media plan was presented as a marketing strategy, and a collection of platform tactics was mistaken for a coherent approach to growth.
Not because digital marketing failed. Because it offered so many useful, measurable things to do.
The urgent gradually displaced the important.
AI could repeat the same mistake
If that sounds familiar, it should. The early AI conversation is following the same script. Marketers are being urged to find the best tools, write better prompts, automate more workflows, produce more content and deploy agents on ever more complex tasks. Do more. Produce more. Automate more. Drive efficiency at scale.
AI has unlocked a great deal that is useful. It reduces repetitive work, shortens production cycles and makes expertise more accessible. But these are execution capabilities.
AI is very good at helping answer the question, “How can we do this?”
It is far less inclined to answer, “Should we do this at all?”
AI can generate hundreds of content ideas without knowing whether the market needs more content. It can produce message variations without deciding which idea the brand should consistently own. It can summarise customer research without determining which customers the business should prioritise, or which it is willing to walk away from. It can build a campaign without taking any responsibility for the commercial reasoning behind it.
The research on the “jagged technological frontier” shows why this distinction matters. In the same Boston Consulting Group experiment, AI produced substantial gains on tasks inside its capabilities. On a task deliberately designed to sit outside that frontier, consultants using AI were 19 percentage points less likely to reach the correct answer than those working without it.
The lesson is not that AI should be avoided. It is that the technology’s value depends on the quality of the task, the context in which it is used and the judgement of the person directing and evaluating the work.
AI can make valuable work faster. It can also make poorly chosen work faster.
If marketing responds to AI by producing more activity without improving the decisions behind it, the profession will repeat the mistake of the digital era, only at far greater speed and scale.
A product does not create its own demand
The belief that a good product will find its own market is remarkably persistent, especially among founders and product-led organisations. Occasionally it happens. It is not a growth strategy.
Customers do not evaluate every available product and select the objectively best one. They operate with limited time, incomplete information and varying levels of interest. They rely on memory, familiarity, trust, recommendations and whatever is easiest to find or justify.
This matters enormously in B2B, where purchases are infrequent, involve several stakeholders and carry professional and organisational risk. At any given moment, most potential buyers are simply not looking.
Professor John Dawes’ 95:5 rule is a useful way to think about this. The exact ratio varies by category and should be treated as a rule of thumb rather than a constant. The principle holds: for many B2B purchases, the large majority of potential buyers are out of market at any given time. Advertising and other forms of brand building therefore work largely by creating and refreshing memories that can be retrieved when a buyer eventually enters the category.
For a new business, the implications are significant. Most of its future customers are not searching for it. They may already have a supplier, lack budget, be locked into a contract or have no immediate need. They are not being difficult. They are not in the market yet.
Demand-capture activity can help a brand compete for the minority who are in market now. Search advertising, comparison content, review platforms and sales outreach all matter at that late stage. But none of them explains how the brand entered the buyer’s mind before the search began.
Someone has to create the memory. The customer must encounter the brand, understand what it stands for and connect it to a relevant need or buying situation. Familiarity and trust have to be in place before the organisation appears on a shortlist.
The Ehrenberg-Bass Institute describes this as mental and physical availability. Brands grow by becoming easier for more category buyers to think of and easier to buy. That requires reach well beyond the small group shopping today, and consistent links between the brand and the situations that bring people into the market.
This is the work that happens long before a sale shows up in a dashboard. It is harder to measure through an attribution model, which is arguably the wrong instrument at this stage of the buying journey anyway. And it is the work that many emerging businesses underestimate, because its effects are less immediate than launching a product or watching a click.
From the first visitor to a scalable business
AI can help a business build its first website. It cannot deliver the first visitor. And once that visitor arrives, the real work starts: understanding what brought them there, whether the offer is relevant and what would persuade them to come back.
An audience is not accumulated traffic. It is a group of people who recognise value in what a business consistently provides. Building one requires a clear understanding of who the organisation wants to serve, the problems it can credibly help solve and the ideas it can contribute better than the alternatives.
Turning an audience into customers takes more than visibility. The offer must be clear, sensibly priced and easy to evaluate. The customer must believe the value outweighs the cost and the risk of changing. In B2B, the proposition may also need to survive procurement, finance, IT, legal and executive scrutiny.
Then comes scale, and a fresh set of questions. Which acquisition channels are repeatable? Do the customer economics hold? Can the brand grow beyond the founder’s personal reputation? Will the proposition stay distinctive once competitors respond?
None of this is separate from product development. It is building the business.
A technically scalable product with no repeatable route to market is not yet a scalable company. A large potential audience that cannot be reached economically is not yet a viable market. High engagement that never becomes customer value or revenue is not yet growth.
Marketing, done properly, is what connects the idea to the economic reality around it.
When execution becomes abundant, judgement becomes scarce
The falling cost of execution does not make marketing judgement less important. It makes it the differentiator.
Execution turns an instruction into an output. Judgement determines whether it was the right instruction in the first place.
Judgement is what separates an interesting idea from a commercially valuable one. It is what lets a marketer interpret incomplete or contradictory evidence, weigh short-term revenue against long-term growth, and choose between several plausible courses of action.
It is also required because marketing decisions are never made in a laboratory. Customers change, competitors respond, budgets tighten, and every organisation brings its own history, capabilities and politics to the table. The same evidence can lead to different choices depending on the market, the business model and the appetite for risk.
AI can contribute to that process. It can retrieve information, challenge assumptions, spot patterns, develop scenarios and speed up how a marketer explores a problem. It may even recommend a course of action.
But the marketer remains responsible for deciding whether the evidence is credible, whether the recommendation fits the context and which consequences the organisation is prepared to accept.
This is why more powerful technology demands stronger foundations, not weaker ones. The sharper the tool, the more it matters to know what good marketing actually looks like.
Evidence informs. Judgement decides.
AI should give marketers time to become marketers again
For years, marketers have said they do not have enough time for strategic work. Their days are consumed by campaign administration, reporting, decks, data cleaning, asset production, platform management, stakeholder requests and coordination.
AI can lift some of that. The evidence from writing, coding and professional knowledge work suggests that well-designed AI assistance can hand real time back to people while improving performance on suitable tasks.
The more interesting question is what organisations and marketers do with that time.
The path of least resistance is to demand more output. If a marketer can produce five pieces of content in the time it used to take to produce one, the organisation will ask for five. If reporting can be automated, it will ask for more dashboards. If campaigns can launch faster, it will launch more campaigns.
That captures the productivity gain. It does nothing to improve the marketing.
The bigger opportunity is to reinvest the time in the work execution has been crowding out: talking to customers, spending time with sales, studying the category, understanding competitors, improving the offer, sharpening positioning and finding the real barriers to growth. Marketers could sit with product teams on what should be built, with finance on how value should be priced and with sales on how the market actually buys.
They could write better briefs. Ask harder questions. Stop activity that does not serve the strategy. Think more carefully about how brands are built in memory, how future demand is created and how the organisation becomes easier to choose.
AI should not simply make marketers faster at the version of the job they inherited. It should give them the capacity to reclaim the version of marketing the business actually needs.
AI, used well, can help marketers get back to marketing.
Returning to first principles
Returning to first principles is not a case for resisting technology or retreating to some earlier era. Digital platforms, analytics, automation and AI have all expanded what the profession can do. The task is to reconnect those capabilities to the purpose they were meant to serve.
First principles provide stable footing when the tools are changing by the month. They ask marketers to begin with the market rather than the channel and with the customer rather than the campaign. They ask whether a real need exists, whether the organisation can create meaningful value, whether the offer is distinctive, and whether customers can easily think of it, find it and buy it.
They also give marketers a better yardstick for evaluating new technology. The question is not simply whether a tool can generate content, automate a workflow or cut cost. It is whether it helps the organisation understand customers more clearly, make a better decision, create more value, build stronger memories or make it easier to buy.
Technology should strengthen marketing capability rather than substitute for marketing thought.
Without first principles, a business can execute efficiently in the wrong direction. With them, AI becomes more useful, because it is being applied to better-defined problems, with stronger evidence and clearer standards for judging the outcome.
Let’s get Back To marketing.
Five questions to ask before you build
If the ability to create is no longer the constraint, the discipline lies in what you ask before you create. Before the next product, course, newsletter or campaign, answer these five honestly:
Do enough people experience this problem to sustain a business, and is it important enough that they would change their behaviour to solve it?
Is your audience dissatisfied with the current alternatives, or merely aware that alternatives exist?
Can you reach your target audience economically, through channels that are repeatable?
Why would your target audience trust an unfamiliar provider, and what will earn that trust before the first sale?
What will make your target audience think of you when they finally enter the market, given that most of them are not buying today?
If you cannot answer these, the product is not ready, however quickly it can now be built. If you can, your marketing strategy is starting from a good place.
The return of marketing
The AI era will create more builders than any before it. Individuals and small teams will develop products that once required substantial resources and organisations. The distance between an idea and its execution will shrink, and experimentation will get cheaper.
AI is a meaningful democratisation of capability. It will not democratise attention in the same way.
Customers will still have limited time and more choices than they can evaluate. Businesses will still need to decide which markets to enter and which problems are worth solving. New products will still need to become known, remembered, trusted and easy to buy. Organisations will still need to balance the demand they can capture today with the demand they must create for tomorrow.
None of that can be solved by production alone.
It requires marketers who understand the commercial role of the discipline, who can tell activity from progress, and who know how to use evidence without surrendering judgement. It requires marketing teams that contribute before the product is finished, not only after the launch date is set.
Back2Marketing exists to help restore that first-principles version of marketing.
We believe marketing is a commercial discipline, not a promotional service. It begins with understanding markets and customers, creating value and making choices about where and how a business can grow. Communication matters, but it cannot repair a weak proposition or create lasting demand for something the market does not value.
We believe digital and AI should increase the contribution marketers make to a business. They should reduce lower-value work, improve access to evidence and give marketers more time to think. They should not trap the profession in an ever-accelerating cycle of more content, more campaigns and more optimisation without a strategic foundation underneath.
Above all, we believe first principles become more valuable as the world becomes more complex. Tools will change. Channels will change. The economics of production will change. The need to understand people, create value, build demand and make sound commercial decisions will not.
Everyone can now build. Few know how to market.
That is the gap Back2Marketing exists to close.
Evidence informs. Judgement decides.
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And if you want to build the judgement, not just the output, Back2Marketing B2B Marketing Fundamentals course teaches marketing from first principles.
Sources
American Marketing Association, Definitions of Marketing, on marketing as the creation, communication, delivery and exchange of value.
Philip Kotler, The Past, Present and Future of Marketing, on the development of marketing and the centrality of customer value.
Shakked Noy and Whitney Zhang, Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, Science, 2023.
Fabrizio Dell’Acqua et al., Navigating the Jagged Technological Frontier, Harvard Business School and Boston Consulting Group field experiment.
GitHub, Quantifying GitHub Copilot’s Impact on Developer Productivity, controlled experiment examining task-completion speed.
Les Binet and Peter Field, The Long and the Short of It, IPA analysis of short-term activation and long-term brand building.
Ehrenberg-Bass Institute for Marketing Science, Advertising Effectiveness and the 95:5 Rule, on out-of-market buyers and the role of advertising in building memory.
Ehrenberg-Bass Institute for Marketing Science, research on how brands grow through mental and physical availability.