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Generic AI creates content without context. In contrast, 'Brand-Aware AI' functions like a strategic coach that understands your brand's rules and learns from performance data. It shifts from just generating content to actively recommending improvements based on what resonates.
The true power of AI in marketing is not generating more content, but improving its quality and effectiveness. Marketers should focus on using AI—trained on their own historical performance data—to create content that better persuades consumers and builds the brand, rather than simply adding to the noise.
Canva positions its data science team as a partner that empowers marketers with information, rather than a gatekeeper that stifles creativity. This allows the marketing team to remain focused on their core function and take big, creative swings that can't be fully measured upfront.
To get high-quality, on-brand output from AI, teams must invest more time in the initial strategic phase. This means creating highly precise creative briefs with clear insights and target audience definitions. AI scales execution, but human strategy must guide it to avoid generic, off-brand results.
AI doesn't replace copywriters; it transforms their role. By automating the menial task of generating countless variations, it frees them to focus on high-level strategy: defining brand voice, guiding the AI, and acting as the expert who orchestrates the machine rather than being the machine.
Most people use AI to perform tasks like writing copy. A more powerful application is using it as a strategic brainstorming partner. Ask it high-level questions about cultural trends and consumer behavior (e.g., 'Why did this artist pop?') to generate novel insights for your strategy.
Nick Pattison's firm creates generative tools for clients, enabling them to produce on-brand assets like geometric patterns themselves. This innovative handoff empowers clients to scale their brand system instantly and playfully, moving beyond static guidelines.
Generative AI models like ChatGPT predict the next logical word based on vast, generic datasets. A more advanced approach uses predictive models trained on a brand's specific performance data—opens, clicks, conversions—to forecast which content variants will actually drive business outcomes, not just sound plausible.
Traditional brand guidelines in static PDFs fail to scale with AI. A "brand system of record" acts as a dynamic, living brain, capturing tone, style, and visuals that AI can use in real-time to ensure all generated content is consistent and on-brand.
The optimal human-AI workflow involves feeding AI both unstructured data (TikTok videos, comments) and structured data (brand bibles, target audiences). AI excels at synthesizing these disparate sources into a near-complete output, like a creative brief, leaving the final 20% of strategic refinement to human experts.
Generic AI app generation is a commodity. To create valuable, production-ready apps, AI models need deep context. This "Brand OS" combines a company's design system (visual identity) and CMS content (brand voice). Providing this unique context is the key to generating applications that are instantly on-brand.