Start / Insights / Article 07
JEDAM DIGITAL
November 2025
5 min read
I want to be direct about something I see constantly and find genuinely frustrating.
The dominant use of AI in marketing right now is generating more content faster. More blog posts. More social captions. More email sequences. More ad copy variants. The pitch is volume: AI lets your team produce ten times the content at a fraction of the cost. And technically, this is true.
It is also, in most cases, entirely the wrong strategy.
Content marketing has a fundamental unit economics problem that AI doesn’t fix: most content produces almost no return. The top 10% of articles drive the overwhelming majority of organic traffic. The top 10% of emails drive the overwhelming majority of opens and conversions. The top 10% of ads drive the overwhelming majority of qualified clicks.
If this was true before AI, it’s more true now — because your competitors are also using AI to produce more content faster, which means the total volume of content competing for attention in every channel is increasing rapidly. The channels are getting noisier. Standing out in a noisier channel by producing more noise is not a viable strategy.
The brands that are winning with AI marketing aren’t publishing more. They’re making every touchpoint more relevant to the specific person receiving it.
The genuinely transformative capability AI brings to marketing is not generation — it’s personalisation. The ability to take a single piece of core content or a single offer and adapt it dynamically based on who is receiving it: their industry, their past behaviour, their stage in the buying process, their specific pain points.
A B2B software company has one core message: our product saves operations teams time and reduces errors. But that message lands very differently with a VP of Operations at a 50-person company than with a Head of Digital Transformation at a 2,000-person enterprise. The pain points are different. The risk considerations are different. The language that resonates is different. The objections are different.
Before AI, truly personalised marketing at scale was only accessible to organisations with very large budgets and very sophisticated marketing teams. AI has democratised it — but only for organisations that invest in the infrastructure to do it properly. Generating more generic content faster is not that infrastructure.
The organisations with genuinely effective AI marketing systems have invested in a data layer that connects what they know about a prospect or customer — industry, company size, behavioural signals, content consumed, stage in the journey — to the content and messaging they serve that person. They’ve built the connective tissue between their CRM, their content assets, their email platform, their ad targeting and their web personalisation.
Once that infrastructure exists, AI generates value at every point where a message is served: the email that adapts its case study example based on the recipient’s industry, the landing page that shifts its hero message based on the referral source, the ad that adjusts its creative based on the engagement history of the segment being targeted.
Building this properly takes more time and investment than standing up an AI writing tool. It produces orders of magnitude more return.
If your current AI marketing strategy is “write more content faster”, you’re not building a competitive advantage — you’re joining a race to the bottom on content quality. The brands that will win the next phase of AI-enabled marketing are the ones investing now in personalisation infrastructure, predictive lead scoring and behavioural intelligence.
More content. Or more relevant content. Both are possible with AI. Only one of them builds durable marketing advantage.
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