By 2030, it is predicted that 80% of web traffic will be non-human, fundamentally reshaping marketing from capturing human attention to also influencing AI agents. This transformation requires brands to evolve from emotion and promotion-driven human engagement to hybrid strategies that optimise for both human connection and AI agent decision-making-processes. Early movers who start implementing agent-optimised marketing strategies will gain significant competitive advantages in visibility, recommendations, and agent-led purchasing decisions, and given the speed of change, this work should start now.
Understanding Web 4.0’s Impact on Brand Discovery
The evidence of this transformation is already being seen. Traditional search is declining, while AI-mediated search is exploding, especially amongst under-35s, with ChatGPT’s c.800 million weekly users regularly turning to AI for information discovery. The Mail Online’s experience provides even more direct evidence of the importance of agent-optimised marketing strategies – Carly Steven, their SEO and editorial and ecommerce director, recently shared that their click-through rates declined by over 50% when Google’s AI Overviews disintermediate search results.
There are also platform-specific citation patterns emerging. Recent analysis by Profound of 30 million citations revealed that ChatGPT heavily favours Wikipedia, which accounts for 47.9% of its top citations, while Perplexity favours Reddit with 46.7% of its top sources. Analysis of Google AI Overviews shows a more balanced distribution of sources across YouTube, LinkedIn, and professional platforms. These patterns reflect how different AI systems have been trained and how they weight authority and relevance.
We’re now in a period of mass adoption and the scale of change is accelerating rapidly. Generative AI traffic to retail sites has increased 3,500% from July 2024 to May 2025, according to Adobe Analytics. Meanwhile, Bain & Company research shows 80% of consumers now rely on AI-written summaries for at least 40% of their searches. While AI agents aren’t yet in consumers’ hands for autonomous decision-making, this adoption is likely to begin in late 2025.
This transition is the start of what I call Web 4.0 – a big shift from human browsing to agent-mediation, moving from clicks and visits to agent interactions and recommendations. We’re witnessing the evolution from traditional SEO to optimising for how AI systems discover, evaluate, and recommend brands.
The marketing implications of this transition will be huge. Brand and product visibility will increasingly depend on AI model recommendations rather than human search behaviour. This will require platform-specific strategies: Wikipedia optimisation for ChatGPT visibility, authentic Reddit engagement for Perplexity reach, and professional content development for Google AI Overviews. Traditional human-centric engagement metrics will become less relevant, demanding new measurement frameworks focused on agent interactions and the transition to developing strategies to influence agent decision-making processes. rethinking marketing for web 4.0
The transformation to agent-mediated marketing will be about fundamentally shifting how we think about digital presence and brand representation. It will require embracing uncertainty while experimenting with new approaches that acknowledge a simple reality: AI systems interpret and represent brands differently than humans do.
The most successful marketers won’t be the ones with perfect strategies—they’ll be the ones asking better questions. They will be testing how different AI platforms actually discover and represent their brands. For example, querying ChatGPT, Perplexity, and Google’s AI Overviews with industry-related questions to see where and how their brands appear.
Think Distribution, Not Destination
Traditional marketing focuses on driving traffic to owned properties – websites, apps, social media profiles. Agent-mediated marketing will require thinking about distribution across AI systems as destinations themselves. A brand’s “presence” will increasingly exist within AI responses, not just on a website.
This shift will require new content strategies. Instead of creating content primarily for human consumption, marketers will need to consider how that same information appears when an AI system summarises it. Will a product description translate well when an AI explains the offering to a user? Can AI systems easily extract and represent key differentiators when making comparisons?
Smart marketers will begin creating specific “AI-native” content – information structured for machine comprehension that could exist separately to human-facing content. The priority will be to ensure a brand’s story translates accurately across both human and AI agent interactions.
Experiment with Platform-Specific Approaches
The citation pattern differences across AI platforms suggest that optimal strategies will vary significantly. ChatGPT’s heavy reliance on Wikipedia indicates that maintaining accurate, comprehensive Wikipedia entries will matter for visibility in its responses. Perplexity’s Reddit focus suggests that authentic community engagement could drive brand mentions in its recommendations.
Rather than trying to optimise for all platforms simultaneously, marketers will need to run targeted experiments. They might enhance their Wikipedia presence and measure ChatGPT citation changes. They could engage meaningfully in relevant Reddit communities and track Perplexity mentions. Or they might develop authoritative LinkedIn content and monitor Google AI Overviews inclusion.
These experiments won’t require massive infrastructure changes – they’ll require marketers to pay more attention to how a brand appears across the information sources that AI systems prefer.
Measure What Matters, Ignore What Doesn’t
Traditional metrics won’t disappear overnight, but new approaches will be required as agent-mediated interactions grow. Forward-thinking marketers will begin tracking new indicators: How often does their brand appear in AI responses? What sentiment accompanies those mentions? How do AI systems position them relative to competitors?
These measurements won’t require sophisticated tracking systems initially. Many will be assessed through manual observation and simple documentation. As important as this, will be developing an intuition about how a brand performs in agent-mediated environments.
Prepare for Acceleration
While AI agents aren’t yet making autonomous purchase decisions for consumers, this capability is quickly approaching. The marketers that understand how AI systems currently interpret and represent their brands will be best positioned when those systems begin making recommendations that directly influence buying decisions.
This preparation will involve building relationships with the information sources that AI systems trust, ensuring accurate representation across platforms where AI systems source information, and developing content that translates well when AI systems summarise or recommend it.
The transition to Web 4.0 will represent a fundamental shift in how brands build digital presence. Success won’t come from rigid frameworks but from curiosity, experimentation, and adaptability. The brands that thrive will be those that learn to influence how AI systems understand and represent them through strategic clarity and authentic digital presence.
What marketers can start doing now
Test and Learn – Begin by understanding your current AI visibility. Spend time querying ChatGPT, Perplexity, and Google’s AI Overviews with questions your customers might ask. Document where and how your brand appears, what context surrounds those mentions, and how you’re positioned relative to competitors. This research will build intuition about AI representation that no analytics dashboard can provide yet.
Audit Your Information Sources – Since AI systems source information from platforms like Wikipedia, Reddit, and LinkedIn, audit your presence across these channels. Is your Wikipedia entry accurate and comprehensive? Are you authentically engaged in relevant Reddit communities? Do you contribute meaningfully to professional discussions on LinkedIn? These platforms increasingly serve as your brand’s “database” for AI systems.
Create AI-Readable Content – While maintaining content for human audiences, begin experimenting with structured information that AI systems can easily interpret. This might mean adding clear product specifications, straightforward company descriptions, or simple Q&A sections to your website. The goal isn’t to replace human-focused content but to ensure key brand information translates well when AI systems summarise it.
Technologies to Watch
llms.txt – Similar to how robots.txt guides search engines, llms.txt files are emerging to help AI systems understand website content hierarchy and brand messaging priorities. While not yet standardised, early adoption could provide advantages as AI systems begin recognising these signals.
NLWeb (Natural Language Web) – Microsoft’s initiative aims to bring conversational interfaces directly to websites, allowing users to interact with web content through natural language queries. This technology could fundamentally change how users discover and interact with brand information, making conversational optimisation as important as traditional web design.
Model Context Protocol (MCP) – This emerging standard promises to enable AI agents to access real-time information from business systems. As MCP develops, it could become crucial for brands wanting to provide agents with current pricing, inventory, or product information.
Structured Data Evolution – Traditional schema.org markup is evolving to better serve AI systems. Staying current with these developments will help ensure your content remains discoverable and interpretable by AI platforms.
Looking Ahead
The transition to Web 4.0 represents one of the most significant shifts in marketing since the Internet emerged. Unlike previous transformations that happened over years, this change is happening over months. The marketers who begin experimenting now, not with massive infrastructure investments, but with curiosity and strategic attention, will be best positioned when AI agents start being adopted by consumers.
Success in this new environment won’t come from rigid frameworks or complex technical implementations. It will come from understanding how AI systems interpret brands, building authentic presence across trusted information sources, and developing content strategies that serve both human and agent audiences effectively.
The future of marketing is being written now, in the responses that AI systems generate today. Marketers need to start learning how Web 4.0 will work while there’s still time to influence its development.
