<p>At a marketing conference in Singapore last year, the audience sat through a panel where four CMOs, described their AI strategies. Each spoke of content velocity, personalisation at scale and lower production costs. The data they provided was impressive and their tone assured.. By the time the third speaker had finished, however, something had become impossible to ignore – every strategy sounded the same. That, in a sentence, is the paradox now confronting marketing functions everywhere.</p><p>The productivity case for AI is no longer contested. Organisations implementing AI in their marketing functions report an average 41% increase in revenue and a 32% reduction in customer acquisition costs. Marketing teams using AI report 44% higher productivity, with the average professional saving eleven hours a week. Content that once took a day can now be produced in an hour. A/B testing, a way of comparing two versions of something to see which performs better, that once required weeks of iteration can now run overnight. Personalisation that previously demanded an entire data science team is now within reach of a three-person brand unit. McKinsey, a firm of consultants, has estimated that generative AI could increase the productivity of the marketing function by an amount equivalent to five to fifteen percent of total marketing spend. For a function that has watched its budget as a share of company revenue decline from 11% in 2020 to 7.7% in 2024, according to recent CMO surveys, this represents not merely an efficiency gain but something closer to a lifeline.</p><p>And yet the audience has noticed. Around 52% of consumers report reduced engagement with content they believe is AI-generated, while roughly 62% say they are less likely to engage with or trust content on social media if they know it was produced by an algorithm. Insights published in the Journal of Business Research identified what scholars have begun calling an “AI-authorship effect” – when consumers believe emotional marketing communications are written by AI rather than by humans, they judge them as less authentic and show weaker engagement and purchase intentions, even when the content itself is otherwise identical. Coca-Cola’s AI-generated holiday advertisement in 2023 was widely derided as soulless. The content met every technical brief and still the audience rejected it. A study by the Nuremberg Institute for Market Decisions found that labelling an advertisement as AI-generated led consumers to see it as less natural and less useful, with attitudes towards the advertisement becoming more negative and willingness to purchase declining, even when the content itself had not changed.</p><p>The two facts sit in direct conflict. The more AI is used, the more efficient the function becomes. On the other hand, the more visibly AI is used, the less effective the output becomes. Consequently, the central strategic shift for CMOs is not whether to adopt AI but where to place it. AI increasingly belongs in the process rather than in the message. It should sit beneath the surface, helping campaigns move faster and further, without becoming the thing the audience notices first.</p><p>This distinction has important consequences for how marketing budgets are allocated and how marketing talent is valued. Three shifts are already visible among the more thoughtful firms. The first is a shift from creation to curation. Generative AI has made content creation almost effortless. However, the harder bit is now knowing what is actually worth publishing. Eighty-eight percent of marketers now use AI every day, but only 26% have figured out how to generate tangible value from it. The second shift is from execution to taste. When any reasonably equipped team can produce a campaign in forty-eight hours, the differentiator is no longer production capacity alone but editorial judgement. This has to do with the ability to recognise what feels human and what should never be published at all. The third shift lies in where the budget flows. Spending is moving away from production and towards editors and strategists who determine which AI outputs are actually worthy of an audience. Consequently, these are no longer small adjustments at the margin but a broader recalibration of where skill truly sits.</p><p>Some firms will respond to the visibility problem by retreating from AI altogether. That would be a mistake. The issue is not AI itself but its undiscriminating use. A competitor running AI-assisted personalisation at scale, with strong human editorial oversight at the front end, will consistently outpace a firm producing expensive, handcrafted content at half the volume. The real risk to manage is not adoption but indiscriminate adoption – publishing whatever the algorithm generates with no filter.</p><p>The lesson is to recognise where AI belongs. As a system that accelerates work, it is unmatched. As a substitute for judgement, it remains inadequate. The firms that understand this distinction will spend more intelligently and avoid sounding exactly like everyone else.</p>
<p>At a marketing conference in Singapore last year, the audience sat through a panel where four CMOs, described their AI strategies. Each spoke of content velocity, personalisation at scale and lower production costs. The data they provided was impressive and their tone assured.. By the time the third speaker had finished, however, something had become impossible to ignore – every strategy sounded the same. That, in a sentence, is the paradox now confronting marketing functions everywhere.</p><p>The productivity case for AI is no longer contested. Organisations implementing AI in their marketing functions report an average 41% increase in revenue and a 32% reduction in customer acquisition costs. Marketing teams using AI report 44% higher productivity, with the average professional saving eleven hours a week. Content that once took a day can now be produced in an hour. A/B testing, a way of comparing two versions of something to see which performs better, that once required weeks of iteration can now run overnight. Personalisation that previously demanded an entire data science team is now within reach of a three-person brand unit. McKinsey, a firm of consultants, has estimated that generative AI could increase the productivity of the marketing function by an amount equivalent to five to fifteen percent of total marketing spend. For a function that has watched its budget as a share of company revenue decline from 11% in 2020 to 7.7% in 2024, according to recent CMO surveys, this represents not merely an efficiency gain but something closer to a lifeline.</p><p>And yet the audience has noticed. Around 52% of consumers report reduced engagement with content they believe is AI-generated, while roughly 62% say they are less likely to engage with or trust content on social media if they know it was produced by an algorithm. Insights published in the Journal of Business Research identified what scholars have begun calling an “AI-authorship effect” – when consumers believe emotional marketing communications are written by AI rather than by humans, they judge them as less authentic and show weaker engagement and purchase intentions, even when the content itself is otherwise identical. Coca-Cola’s AI-generated holiday advertisement in 2023 was widely derided as soulless. The content met every technical brief and still the audience rejected it. A study by the Nuremberg Institute for Market Decisions found that labelling an advertisement as AI-generated led consumers to see it as less natural and less useful, with attitudes towards the advertisement becoming more negative and willingness to purchase declining, even when the content itself had not changed.</p><p>The two facts sit in direct conflict. The more AI is used, the more efficient the function becomes. On the other hand, the more visibly AI is used, the less effective the output becomes. Consequently, the central strategic shift for CMOs is not whether to adopt AI but where to place it. AI increasingly belongs in the process rather than in the message. It should sit beneath the surface, helping campaigns move faster and further, without becoming the thing the audience notices first.</p><p>This distinction has important consequences for how marketing budgets are allocated and how marketing talent is valued. Three shifts are already visible among the more thoughtful firms. The first is a shift from creation to curation. Generative AI has made content creation almost effortless. However, the harder bit is now knowing what is actually worth publishing. Eighty-eight percent of marketers now use AI every day, but only 26% have figured out how to generate tangible value from it. The second shift is from execution to taste. When any reasonably equipped team can produce a campaign in forty-eight hours, the differentiator is no longer production capacity alone but editorial judgement. This has to do with the ability to recognise what feels human and what should never be published at all. The third shift lies in where the budget flows. Spending is moving away from production and towards editors and strategists who determine which AI outputs are actually worthy of an audience. Consequently, these are no longer small adjustments at the margin but a broader recalibration of where skill truly sits.</p><p>Some firms will respond to the visibility problem by retreating from AI altogether. That would be a mistake. The issue is not AI itself but its undiscriminating use. A competitor running AI-assisted personalisation at scale, with strong human editorial oversight at the front end, will consistently outpace a firm producing expensive, handcrafted content at half the volume. The real risk to manage is not adoption but indiscriminate adoption – publishing whatever the algorithm generates with no filter.</p><p>The lesson is to recognise where AI belongs. As a system that accelerates work, it is unmatched. As a substitute for judgement, it remains inadequate. The firms that understand this distinction will spend more intelligently and avoid sounding exactly like everyone else.</p>
