Artificial intelligence is changing how the web works. Search engines, voice assistants, and generative platforms are altering how people find information and make decisions.
The internet is no longer built only for human visitors. Brands now operate in an environment where both people and intelligent systems interact with their content, reshaping how websites are designed, found, and measured.
Dual Audiences
The modern web now serves two audiences.
Websites are designed not only for people to read and navigate, but also for AI systems that interpret and act on information on behalf of users. This change is as significant as the move to mobile-first design.
Traditional search practices that focused on keyword visibility, human readability, and click-through rates are becoming less effective. AI-generated summaries in search results, along with tools like ChatGPT, Perplexity, and Gemini, surface information directly to users without them visiting a site. Website traffic and engagement data are becoming less reliable measures of success.
Brands need content that performs two functions. It must provide value and clarity for human visitors while also being structured in a way that can be understood and used by AI systems. This calls for new thinking around design, content structure, and data transparency.
Redefining Visibility
Visibility is no longer only about ranking highly on a search results page. It now depends on how often a brand’s information is cited or used by AI systems.
Brands with well-organized data, clear product details, and content that machines can interpret are more likely to appear in AI-driven environments. Websites should utilize modular, structured frameworks that separate content from design, allowing AI agents to easily process the information.
Modern SEO now extends beyond technical optimization and backlinks. It includes preparing data for language models and voice assistants, product feeds, and FAQ content to help make brand information accessible both to people and to machines.
Content strategies also need to evolve. Pages should be written to answer user questions directly, not just target keywords. AI systems prioritize clarity, authority, and logical structure. Brands that provide straightforward, useful information are more likely to appear in AI summaries and responses.
Personalization At Scale
AI is expanding how brands personalize content and recommendations. Machine learning and first-party data allow for tailored experiences at a scale that was not previously possible.
The challenge is maintaining a consistent brand identity while using automated personalization. Without strong frameworks, brand messaging can become inconsistent or lose tone.
To avoid this, organizations should build clear structures, tone-of-voice guidance, and defined data governance. Modular content systems make it possible to create personalized messages without losing consistency. Each variation should feel part of the same brand experience.
A strong data strategy is essential. Customer Data Platforms and analytics tools help brands understand context and behavior, enabling more relevant and timely communication. Human oversight remains important to ensure brand values and tone are respected across automated outputs.
Measuring Success In The AI Era
As AI reduces clicks and sessions, traditional marketing metrics are less meaningful. C-level leaders are focusing more on results than activity. The key question has become how effectively a brand’s content or product is being chosen or recommended by intelligent systems.
Brands can measure performance in three areas:
1. Agent Visibility And Selection
This reflects how often AI systems reference or prioritize a brand’s content. Tracking brand mentions and inclusion across AI platforms is becoming an important new visibility metric.
2. AI-Driven Traffic Referrals
Although click-throughs are fewer, visitors who arrive via AI recommendations often convert more quickly. Measuring how these users behave can reveal intent and content quality.
3. Brand Sentiment And Experience Quality
In personalized environments, success is not only about visibility but also how users feel. Measuring satisfaction, accuracy, and tone across AI interactions is key.
To do this effectively, brands need updated analytics. Tools that assess visibility in generative systems and track AI-driven referrals are beginning to emerge. Integrating these into broader measurement frameworks will be essential.
Preparing For The Open Agentic Web
The next phase of web development is the open agentic web, where AI systems can browse, interpret, and act across sites on behalf of users. These agents can make bookings, complete purchases, and retrieve information without direct user input.
New web standards are supporting this transition. Protocols such as NLWeb are helping make content easier for AI systems to access. This aims to create smoother interaction between users, brands, and intelligent systems.
Businesses should start adapting their digital infrastructure now. Content management systems, APIs, and data models should serve both human users and AI agents. Making information accessible in a structured, secure way will determine how effectively brands participate in this environment.
This shift also brings new decisions. Some brands may allow AI systems to use their content to improve visibility, while others may prefer to limit access. Each approach affects how visible and discoverable the brand becomes.
Leaders should see this as a major transition. Those who act early to build structured, machine-readable foundations will have an advantage. Those who delay risk losing visibility as AI systems become key gateways to information.
What C-Level Needs To Know
Executives should focus on three main areas as the open agentic web develops:
1. Build A Flexible Digital Infrastructure
Invest in structured, modular systems that can evolve with AI standards. APIs, data models, and schemas should be consistent and accessible.
2. Update Performance Metrics
Shift away from traffic and CTRs. Focus on agent selection, task completion, and performance outcomes that reflect both human and machine interactions.
3. Align Teams Around Data And Content
AI integration spans marketing, technology, and product functions. Shared frameworks are needed to ensure tone, data, and strategy stay consistent.
What Brand Teams Need To Do
Marketing teams should turn these strategies into practical action.
They need to create content that answers questions clearly, maintain clean data structures, and design experiences that both humans and machines can interpret. Testing structured formats such as conversational FAQs, knowledge hubs, and metadata-rich content will help future-proof visibility.
Measurement practices must also evolve. Teams should begin testing tools that monitor how often AI platforms reference their content and how structured data contributes to discoverability.
A New Web For Humans And Machines
The web is moving towards closer interaction between people and intelligent systems. Success will depend on how well brands design experiences that are both understandable and trustworthy for both parties.
For business leaders, the goal is to build digital systems that operate clearly and efficiently. For brands, it means creating content and structures that work with AI rather than against it.
The open agentic web will reward brands that connect visibility, personalization, and measurement into a single strategy. Those that act early will help shape how this new phase of the internet develops.
More Resources:
- Explaining Agentic SEO To The C-Level
- Agentic AI In SEO: AI Agents & The Future Of Content Strategy (Part 3)
- State Of SEO 2026
Featured Image: Anton Vierietin/Shutterstock
