The obsession with personalization at scale is misguided for brand building. Customer service interactions should be personal. However, a brand is built on a communal agreement of what it stands for. Hyper-personalized brand messages undermine this shared meaning.
Instead of forcing new offerings into existing frameworks, agencies should reverse-engineer their entire structure—talent and processes—from the new creative outputs the market demands. This requires anchoring in core principles while remaining flexible in practices.
Many CMOs have drifted into becoming system architects, obsessed with operational efficiency. However, their most crucial role is to maintain an empathetic 'theory of mind' about the customer and use expressive creativity to make the brand compelling.
The most valuable creative talent is often the most difficult to manage. Forcing everyone into a mold of the 'good corporate citizen' engineers mediocrity. A key leadership skill is managing peculiar, non-conformist individuals who drive disproportionate value.
Using AI effectively isn't about cognitive offloading, which leads to mediocrity. It's about amplifying human thought. Humans must provide the 'why' (ambition) and the 'what' (taste) to bookend the technology, which only solves for the 'how'.
Effective creative output, especially in digital products, blends system design (interface, usability) with storytelling (embedded narrative). Organizations must foster structural equality and mutual respect between these two types of thinkers—systematic and narrative—to achieve greatness.
The conflict between brand (feeling) and performance (acting) creates a dysfunctional 'hourglass' structure in marketing teams. The focus should be on the middle—helping customers *understand* the product's value. From that core, you can build both brand awareness and drive transactions.
Many marketing teams haven't adapted their organizational design for the internet era, which demands disciplines like experience design. They cling to the 'art and copy' model from the 1950s, making them unprepared for the systemic, synthetic challenges of the AI era.