Generative AI can predict how customers will emotionally react to policy changes or marketing messages. Running communications through AI first can prevent the kind of backlash Carnival Cruises experienced by identifying tone-deaf language.

The traditional "test and learn" mantra is flawed because teams often start with a weak set of creative variants. By using predictive AI to generate a diverse but pre-vetted, high-performance set of options, marketers can ensure their tests are more meaningful and aren't just optimizing a bad strategy.

Marketers should use AI-driven insights at the beginning of the creative process to inform campaign strategy, rather than solely at the end for performance analysis. This approach combines human creativity with data to create more resonant campaigns and avoid generic AI-generated content.

Existing AI tools like Societies can test marketing content by creating hundreds of AI agents based on a user's actual audience (e.g., from LinkedIn). The platform predicts how viral a post will be and suggests improvements before it's published, offering a data-driven approach to content strategy.

Create a business that runs ad tournaments for D2C brands. Use an AI to ingest a brand's actual customer reviews, build detailed customer personas from that language, and then have those personas "judge" dozens of ad concepts overnight. This offers rapid, data-driven feedback at a fraction of traditional costs.

The future marketer's job evolves from creating and testing individual ads to designing and A/B testing entire systems. This means experimenting with different models for research, data analysis, and concept generation, operating at a higher strategic level rather than a tactical one.

Instead of asking an AI tool for creative ideas, instruct it to predict how 100,000 people would respond to your copy. This shifts the AI from a creative to a statistical mode, leveraging deeper analysis and resulting in marketing assets (like subject lines and CTAs) that perform significantly better in A/B tests.

To prevent audience pushback against AI-generated ads, frame them as over-the-top, comedy-first productions similar to Super Bowl commercials. When people are laughing at the absurdity, they are less likely to criticize the technology or worry about its impact on creative jobs.

Moving beyond using AI for simple content generation, SAS applies it to enhance marketing quality. They built an AI agent that scores creative briefs against effectiveness criteria. This forces teams to create better inputs, leading to better creative outputs and reframing AI's role from cost-saver to quality-enhancer.