As AI automates content creation, the critical role for marketing leaders shifts. Instead of producing volume, their primary function becomes instilling a sense of "taste" and sound judgment across their teams to ensure AI-generated output is high-quality and on-brand.
Enterprise buying isn't purely rational. Marketers should open with emotion, inspiration, and vision to capture attention and build aspiration. Only after earning that attention should they follow up with the logic, security, and assurance needed to de-risk the decision for IT and procurement.
As AI makes customer acquisition a table-stakes capability, elite marketers will shift focus to protecting their customer base. Driving loyalty, fostering advocacy, and amplifying customer stories will become the key differentiator, as these are human-centric activities that generate more sustainable revenue.
Developing a team's creative taste isn't abstract. It's a trainable skill built by establishing a ritual of reviewing great, average, and poor creative examples side-by-side. This process of comparison and discussion calibrates the entire team on what quality looks like.
Time saved from AI-driven efficiencies must be consciously reallocated to strategic tasks that AI can't do, like deeper customer research or improving sales enablement. This compounds the value of the initial time saving, but only if that time is actively protected and reinvested.
The old view that demand generation funds brand is backward. A strong brand is a prerequisite for long-term, sustainable demand. Investing in brand equity makes all performance marketing and sales channels more effective, creating a compounding effect on growth over time. Brand is an investment in long-term demand.
Neuroscience research from Canva shows a quantifiable reason to avoid generic, AI-generated content. The human brain processes and encodes visually engaging content 74% faster than "dull" content. This speed directly impacts brand recall and message clarity, making visual storytelling a competitive advantage.
Early AI adoption focuses on productivity (e.g., writing copy faster). The next stage of maturity is using AI to directly impact revenue. For example, Canva uses AI to create and test 20% more ad variations, leading to more engaging, higher-converting campaigns that drive business results.