In an AI answer engine era, brands need to master marketing to both humans and machines

POV’s
February 4, 2025
Viktor Nilsson, Monika Løkken, Christian Berthelsen & Mika Mustiala

AI is rewriting the rules of search, so get ready to unlock the potential of answer engines and position your brand for future success, as the initial impact of GenAI on consumer search behavior is less disruptive than many predicted. While new AI-powered tools like Perplexity and SearchGPT offer enticing alternatives by providing direct answers, users currently seem to be integrating these alongside existing search habits rather than replacing them entirely.

This suggests that GenAI is currently complementing, and not revolutionizing, how people search. However, history suggests we often overestimate short-term impacts and underestimate long-term ones. Therefore, brands must prepare for a future where AI engines play a more significant role and carefully balancing their marketing strategies between engaging human users and marketing for machines.

So how do you market towards AI engines?

The key difference between AI engines and classic search engines is that Search Engines search and present results based on an index of the web. AI answer engines generate content based on training data using a predictive model.

AI language models predict the next word in a sentence based on the probability of it appearing after the preceding words. This probability is learned from large datasets of text and code. The more often a word appears next to another in the training data, the higher the probability the AI will use that combination.
Training data is key, and that makes the foundation of the AI’s knowledge. It’s a vast collection of text and code from various sources, including websites, books, podcasts, videos. The AI learns patterns and relationships between words from this data.

AI models are either static (like Claude and free ChatGPT) or search-augmented (like Perplexity and SearchGPT). Static models use only their training data; search-augmented models also use real-time web searches for more current answers.

How to scale your brand’s presence in AI answers

In essence, the strategy boils down to increasing the frequency and relevance of your brand’s mentions across the web, particularly on platforms likely to be used in AI training data. It’s a long-term strategy that requires consistent effort and a deep understanding of how AI language models work. Therefore, here is listed some points that you should have in mind:

Audience-Led Content Planning: Don’t just throw mud at a wall; architect your content ecosystem with precision to minimize production and distribution waste. Deeply understand your target audience and the questions they’re asking. This ensures your content resonates with both humans and the AI, which learns from human interactions.

Scale Brand-Topic Associations: The goal is to systematically increase the frequency and relevance of your brand’s association with key topics across the web. This requires a multi-pronged approach:

Cross-Platform Brand Content: Create and distribute high-quality content across multiple platforms (your website, blog, social media, etc.) that consistently links your brand to relevant topics. Whilst it is key to think beyond keywords and focus on creating comprehensive, informative content that answers audience questions, you still need to remember that AI engines are predictive and therefore align your brand with relevant terminology across the web.
Influencer & Advocacy Content: Partner with influencers and brand advocates to create content that mentions your brand in the context of relevant topics. Their reach and credibility can significantly amplify your brand-topic associations.
PR & Publisher Content: Secure media coverage and guest posting opportunities on reputable publications. These mentions carry significant weight with both audiences and AI models, strengthening your brand’s authority and relevance.

Optimize for Large Language Model (LLM) consumption

Remember, LLMs crawl the open web. Make it easy for them to understand your content and connect it to relevant topics:

JavaScript Rendering: Most AI crawlers currently do not render JavaScript. Ensure key content on your site isn’t solely reliant on client-side rendering.
Clear and Concise Language: Avoid jargon and write in a way that is easy for both humans and machines to understand.
AI-positive platforms: Ensure that the platforms and publications you market your brand content on allow for AI Engines to train models on.
Structured Data: Schema markup primarily benefits search engines by providing context to your content. While it doesn’t directly impact LLMs right now, it could indirectly impact search-augmented LLMs.

Monitor, Analyze, and Iterate

Continuously track your brand mentions, analyze the results, and refine your strategy. Practice social listening across platforms, monitor conversations incl. external fora such as Reddit, and identify new opportunities.

Furthermore, these platforms are likely to incorporate ads in the future, hence continuous alignment with paid activation teams will become increasingly important.

By combining a deep understanding of both audience & machine, with a strategic approach to content creation and distribution, you can effectively scale your brand’s presence in AI answer engines. And that would be an advantage, as AI is changing the search landscape. But as it can be a challenge to embark on a new landscape, we offer a comprehensive cross-platform search strategy that covers:

Demand analysis: Understanding user preferences across all platforms (AI Engines, social, retail, etc.)

Behavioral insights: Deep dives into user topics, intent, and preferred content formats

Content mapping: Identifying content gaps and benchmarking against competitors
Actionable recommendations: Prioritized strategies, targets and budget allocation for maximum impact

So are you curious to future-proof your brand’s search strategy? Reach out to your local specialist: