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Prepare Marketing Sites for the AI-First Web

A computer in a motion blur effect representing the readiness of website in AI future

Summarise this article with AI

ChatGPT
Claude
Perplexity
Gemini
DeepSeek

The digital landscape is experiencing a fundamental shift. Large language models aren't just changing how content is created but are reshaping how business decisions are made, how customers research solutions, and ultimately, how websites must be designed to remain competitive.

Consider the scale of this transformation: more than half of American adults (61%) have used AI in the past six months, with nearly one in five relying on it daily. Scaled globally, that translates to 1.7 to 1.8 billion people who have used AI tools, with 500 to 600 million engaging daily. Meanwhile, McKinsey research reveals that consumers' expectations for value and convenience have them making unexpected trade-offs across categories, increasingly turning to digital connectivity and at-home activities as permanent parts of their daily lives.

For growth-stage companies and their marketing teams, this represents both an opportunity and a strategic imperative. Your marketing site can no longer be optimized solely for human visitors but must also serve the AI systems that increasingly mediate the buyer's journey.

At Underscore, we've observed how forward-thinking marketers are already adapting. Here are three strategic enhancements we recommend to position your marketing site for the AI-first web.

1. Implement llms.txt: Strategic Content Curation for AI Systems

The emerging llms.txt standard represents more than a technical specification; it's a strategic communication tool. Placed at your site's root, this file provides AI crawlers with curated guidance on which content deserves priority attention.

The strategic value: Rather than leaving AI interpretation to chance, you maintain editorial control over how your brand narrative appears in AI-generated responses. With 91% of AI users reaching for their favorite general AI tool for nearly every job, establishing clear guidance for these systems becomes increasingly critical for competitive differentiation.

Implementation considerations: While universal adoption across AI platforms remains inconsistent, the directional trend is clear. Organizations that establish these standards early position themselves advantageously as the ecosystem matures.

Get your llms.txt for B2B Sites Get your llms.txt for B2C Sites

2. Add "Share for AI Analysis" Functionality: Seeding Personal AI Assistants with Your Expertise

Modern B2B buyers increasingly rely on their personal AI assistants for research and decision-making. A "Share for AI Analysis" feature allows users to easily transfer your content to their preferred AI tool (ChatGPT, Claude, Gemini, etc.), effectively seeding their AI's context with your expertise and positioning.

The strategic benefit: When users share your content to their personal AI assistants, your expertise becomes part of their working knowledge for that session. This means when they ask follow-up questions about your industry, solutions, or competitors, their AI responses will be informed by your content and perspective. It's a form of contextual influence that extends your brand's reach into private AI conversations.

Knowledge transfer implications: While most current LLMs don't retain information permanently across sessions, the immediate context transfer creates valuable touchpoints. Users conducting research or comparison shopping will have your positioning and expertise readily available in their AI toolkit, potentially influencing their decision-making process even in private conversations with their AI assistants.

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3. Enhance Schema Markup: Speaking AI's Native Language

Structured data markup has evolved beyond its original SEO function. Modern AI systems increasingly rely on schema.org markup to understand content hierarchy, context, and meaning.

Strategic significance: Well-implemented schema markup serves as a bridge between your content strategy and AI interpretation. It ensures that when AI systems parse your site whether for search results, recommendations, or decision support they accurately understand your service offerings, expertise areas, and brand positioning.

Beyond traditional SEO: While rich snippets remain valuable, the real opportunity lies in how structured data improves AI comprehension of your site's architecture and content relationships.

Get the Organization schema for B2B sites

The Consultative Approach: Strategy Before Implementation

These enhancements share a common thread: they require strategic thinking about how your brand wants to be understood, not just by human visitors, but by the AI systems that increasingly influence the buyer's journey.

This is precisely the kind of forward-thinking approach we bring to web development projects at Underscore. Rather than simply implementing features, we help growth-stage companies think through the implications: How do you want AI systems to interpret your brand? Which content deserves priority in AI-generated summaries? How can your site architecture better serve both human and artificial intelligence?

The consumer AI market has reached $12 billion in just 2.5 years, yet only 3% of users pay for premium services, indicating a massive monetization gap. The companies that thrive in the AI-first web won't be those that simply add AI features but those that think strategically about how AI integration serves their broader growth objectives.

Moving Forward

These three enhancements represent tactical implementations of a larger strategic shift. For marketing teams at growth-stage companies, the question isn't whether AI will impact how your website performs but whether you'll proactively shape that impact or simply react to it.

As McKinsey research indicates, consumers' tolerance for friction and inconvenience continues to decrease while their expectations for service and speed increase. This behavioral shift extends to how potential customers discover and evaluate your services through AI-mediated channels.

The most successful implementations we've seen combine technical execution with strategic thinking about brand positioning, user experience, and long-term competitive advantage. They're the result of consultative partnerships that go beyond pixel-pushing to address fundamental questions about how modern websites create value.

Ready to explore how strategic web development can position your marketing site for the AI-first web? At Underscore, we help growth-stage companies think deeper about their digital presence and execute smarter solutions that drive sustainable growth.

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• Frequently Asked Questions

How do I know if my marketing site needs AI optimization?

If your target audience includes decision-makers who research solutions online, your site likely needs AI optimization. Growth-stage companies particularly benefit because their buyers increasingly rely on AI tools for research and comparison. Key indicators include: competitive markets where AI-generated recommendations influence purchases, complex service offerings that benefit from summarization, and technical content that needs better structure for AI interpretation. The question isn't whether to optimize, but how strategically to approach it.

What's the difference between adding AI features and strategic AI integration?

Adding AI features typically means implementing tools like chatbots or summary buttons without considering broader implications. Strategic AI integration involves thinking through how AI systems should interpret your brand, which content deserves priority in AI responses, and how these enhancements serve your growth objectives. It's the difference between tactical implementation and consultative strategy. The most successful approaches combine technical execution with deep thinking about brand positioning and competitive advantage.

How much does implementing these AI enhancements typically cost?

Investment varies significantly based on strategic approach and implementation complexity. Basic technical implementation might range from a few thousand dollars, while comprehensive AI readiness involving content strategy, architecture optimization, and ongoing refinement represents a larger investment. The key consideration isn't cost but strategic value. Companies that view these enhancements as part of broader digital transformation typically see better returns than those treating them as isolated features.

Will these changes impact my current SEO performance?

When implemented strategically, AI enhancements typically improve SEO performance rather than harm it. Schema markup has long been an SEO best practice, while structured content and clear information architecture benefit both search engines and AI systems. However, changes should be implemented thoughtfully with proper testing and monitoring. The most successful implementations we've seen actually strengthen SEO while preparing sites for AI-mediated discovery.

How do I measure the success of AI optimization efforts?

Success metrics depend on your specific goals but typically include improved engagement on key pages, better performance in AI-generated search results, and enhanced user experience metrics. More sophisticated measurement involves tracking how your content appears in AI responses and monitoring changes in qualified traffic patterns. The key is establishing baseline measurements before implementation and focusing on metrics that align with your broader growth objectives rather than vanity metrics.