How can brands influence their visibility in AI search?

AI now sits between your brand and your buyers, shaping how your company is described and which solutions get recommended before anyone reaches your website.

And AI-generated answers aren't always accurate. Research from Columbia University's Tow Center for Digital Journalism found that AI search engines provided incorrect answers to more than 60% of queries testing their ability to identify and cite news sources.

For marketers, the stakes extend to how AI engines interpret and represent their brands. Influence engineering provides a strategic approach to strengthening the signals AI systems use to understand your company and its offerings.

The Influence Engineering e-book outlines a practical, six-step strategy for improving how your brand is represented in AI search.

What to expect in this guide

The e-book explores how marketing teams can build a more intentional approach to AI search influence, including:

• Defining your brand's AI narrative: Establish how your company should be represented in AI-generated answers and identify gaps between your intended positioning and current visibility.
• Auditing AI search performance: Evaluate how ChatGPT, Gemini, Claude, and Perplexity describe your brand, including the accuracy of their responses and the sources they reference.
• Building authority beyond your website: Strengthen the signals AI engines encounter through original research, expert content, and relevant third-party coverage.
• Scaling and measuring your influence: Apply the e-book's six-step strategy through repeatable workflows, with ongoing measurement of brand accuracy and competitive share of voice.

AI search is creating new opportunities for brands to shape how buyers discover and evaluate their offerings. A deliberate influence engineering strategy helps marketing teams strengthen their presence across the sources and platforms shaping those decisions.

Download the Influence Engineering e-book to explore the six-step framework for building a more credible and consistent brand presence in AI search.

Frequently asked questions

What is influence engineering in AI search?

Influence engineering is the practice of deliberately strengthening the information and authority signals AI systems use to understand and represent a brand. It involves improving first-party content and building a consistent presence across relevant third-party sources to support more accurate AI-generated answers.

How does influence engineering differ from generative engine optimization (GEO)?

Influence engineering focuses on the signals shaping how AI engines perceive and describe a brand. Generative engine optimization (GEO) is the broader practice of improving visibility in AI-generated answers. Influence engineering can support GEO by strengthening brand representation and source credibility.

How does GEO differ from traditional SEO?

GEO focuses on improving brand visibility in AI-generated answers, including citations and recommendations. Traditional SEO focuses on organic search visibility and rankings. Both benefit from accessible, authoritative content, but GEO also examines how AI engines interpret and communicate information about a brand.

How can marketers measure their influence in AI search?

Marketing teams can measure AI search influence by tracking brand mentions and citations across relevant buyer prompts. Brand accuracy and sentiment analysis provide additional insight into how AI engines represent a company. Comparing these results over time helps identify where influence engineering efforts may be contributing to better visibility.

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