What should guide our AI marketing decisions?

Audience understanding, purposeful communication and a clear brand give AI marketing decisions a steadier basis than keeping pace with every new capability. The Frameworks advises marketers to use AI to deliver on brand purpose and create more meaningful, relevant audience experiences, rather than treating faster production as the goal. For a marketing team, the starting question is what an audience needs and how AI could strengthen the brand's connection with that audience.

A 2025 survey by Content Marketing Institute and MarketingProfs found that 95% of B2B marketers, mostly in North America, said their organisations use AI-powered applications, while 68% described their AI approach as exploratory or developing. Widespread adoption need not displace brand direction. The Frameworks argues that AI works best on a foundation of clear purpose, consistent behaviour and genuine audience understanding. Capabilities become cheaper and easier for competitors to match; brand loyalty takes longer to establish and is harder to replicate.

Give an AI experiment a narrow, worthwhile purpose by identifying one or two things the brand can do particularly well and building an experience around them. The Frameworks recommends that focus as a practical way to explore generative AI without becoming overwhelmed by its possibilities. The experience should align with brand values and deepen understanding of customers' wants and needs.

The Frameworks established a working group to experiment with generative AI tools and review their potential. Its approach to small-scale AI projects centres on digital brand and marketing tools that a team can prompt, design for and measure. A marketing team can use the same discipline: keep the application understandable and achievable, combine hands-on experimentation with professional expertise, and learn by doing. Access to a tool is not a substitute for creative skill. The Frameworks treats AI as a collaborator, with experts remaining responsible for shaping quality output.