Write in a direct, human voice for [audience] and follow these rules

• Contractions: It’s OK (or NOT OK) to use contractions 
• Prefer active voice
• Do not start any sentences with This 
• Vary sentence structure and length
• Do not repeat the same key words or phrases in back-to-back sentences unless it is clearly intentional
• Vary word choice
• Avoid like not X, but Y or not just X, Y. State the main point directly
• Do not use phrases like at the intersection of
• Do not fragment stack
• Limit phrases like it might be argued, it could be suggested, in many ways, somewhat, quite used on repeat
• Limit unlock, quietly
• Quotation marks: Only use quotation marks when quoting a real person, source, or product name
• Lists and formatting: Do not turn everything into a list unless I explicitly ask for a list.

Act As Prompts

AI is the best and worst thing to happen to design.

It gives scrappy teams a way out of the blank page. A solo founder with no design or marketing background can spin up a decent-looking landing page in an afternoon. You can generate logos, layouts, color palettes, illustrations, and copy faster than most agencies can schedule a kickoff. AI can level the playing field.

Bad Design is Still Bad (Even if AI Made It)

A lot of AI assisted design is wrecking brands: pages are crowded and repetitive, emojis are scattered everywhere, color choices clash, and layouts look like they were pulled from the same template as a hundred other companies. It’s great that something is live, but that doesn’t mean it works for your brand.

None of this is a reason to stop using AI; just apply it with more intention.

What Good Looks Like

Designers are taught that everything on the page has a job. A few basic principles make a huge difference:

Start With Your Message

Write your message before you start design layout: who you are for, what you do, and why they should buy, in one or two sentences. Build everything else around that. Define your brand, tone, and message, then let AI help you express it. Design for your customers, what convinces them to buy. Define your brand, tone, and message first, then let AI help you express it.

Prompts to Steer AI in a Better Direction

• Create a design with one main headline, one subhead, one primary call to action, and at most three supporting sections. Explain the order and purpose of each section.
• Suggest a simple color palette with one primary color, one accent color, and one background. Include hex codes and how each color should be used.
• Recommend a clean sans serif font for headings and one for body text, and describe how to size and weight them to create a clear hierarchy.
• Act as a prospective customer and critique this landing page. Will it convert? Identify issues with the copy and design that might stop you from signing up or buying.
• Review this page description and identify where hierarchy, contrast, or consistency are weak. Suggest three concrete changes to simplify and clarify it.

Add a Professional Human Judgment

If you can, an hour with someone who understands brand, design and user experience can help you cut what is confusing, tighten what converts, and keep what actually feels like your brand.

AI can help you explore more options and move faster but human judgment makes sure those options serve your story and your growth. The opportunity and the responsibility is to let AI amplify what is distinct about you, instead of speeding you into a generic, forgettable mess.

Integrated campaigns that run a consistent story across multiple channels are about 31% more effective than disparate tactics, and every additional coordinated channel can improve ROI and effectiveness by up to 35% (1).

Multi‑channel programs also show several‑hundred‑percent higher purchase rates than single‑channel blasts, because buyers see and recognize the message instead of getting one lonely touch and moving on (1, 3).

McKinsey found that companies with integrated, insight‑driven commercial systems significantly outperform their peers in growth and resilience, because they treat marketing as part of a connected growth engine, not a set of isolated tasks (3).

On the product side, failure analyses keep finding the same thing: a big chunk of new products fail not because of marketing, but because there was weak customer understanding and poor product–market fit (2). When fit is weak, even polished campaigns struggle because you are pushing something the market doesn’t really want (2).

What AI Readability Entails

AI systems interact with the web through large-scale crawls, structured datasets, and retrieval pipelines designed to normalize information across sources. Interpretability depends on factors marketers already understand, but rarely govern holistically: semantic hierarchy that preserves intent, consistent entity definitions that resolve ambiguity, internal linking that reinforces topical relationships, and clear separation between claims, evidence, and opinion.
Content that cannot be reliably contextualized is less likely to be summarized or cited, regardless of how well it performs in traditional search.
Visibility in AI-driven environments is therefore determined less by whether a site functions as a coherent knowledge system under programmatic scrutiny.

How Structured Is the Web Today?

Publicly available crawl data and technology-usage analyses indicate that machine-interpretable structure remains limited across the web, particularly beyond baseline implementations.

Why Most Web Sites Are Not There Yet

Most sites evolved incrementally. Campaigns, product launches, regional expansions, and stakeholder requests layered on top of one another over years. Content volume increased while semantic governance remained static. Templates multiplied. Taxonomies drifted. Internal linking followed navigation history rather than meaning. Structured data adoption reflects this pattern. Many teams technically have schema, often injected by CMS defaults or plugins, but lack a consistent entity model. Markup identifies page types without defining relationships. Organizations appear under multiple identifiers. Products are disconnected from use cases, proof points, and expertise

The Opportunity Marketing Teams Rarely Price Correctly

AI systems must choose which sources to surface, summarize, and cite. They cannot include everything, and they do not resolve contradictions gracefully. Selection favors sites that reduce interpretive overhead and present internally consistent representations of expertise, offerings, and claims.

Early inclusion compounds. Once a source is repeatedly selected as usable, it becomes a reference point. Reference points are harder to displace than high-ranking pages because they influence how a category is explained.
Marketing teams that treat AI readability as infrastructure rather than optimization are influencing how their market is described.

What AI-First Web Architecture Involves

AI-first architecture is about modeling meaning explicitly, including semantic hierarchy that reflects real conceptual relationships. It requires consistent entity resolution for organizations, products, people, and evidence across the site. It depends on explicit relationship modeling so systems do not have to guess how offerings connect to problems, data, or outcomes. Internal linking must reinforce knowledge flow rather than historical page sprawl. Content must support accurate quotation, with scope and context intact.

Schema markup supports this work, but it does not replace it. Without architectural discipline, schema becomes fragmented metadata layered onto incoherent structure. Without schema, meaning remains trapped in prose. Effective AI visibility requires both.

Why Timing Matters

AI retrieval systems are already forming preferences based on what they can interpret reliably. Once those preferences stabilize, late adopters face a credibility gap rather than a discoverability gap. They are not competing for attention; they are competing against established defaults.

Progress does not require rebuilding everything. It requires prioritizing what already matters most: core product pages, foundational explanations, authoritative content, and the pages that influence buying decisions today.

What AI Leadership Looks Like

Teams moving ahead share a common posture. AI visibility is treated as a growth input, not an experiment. Semantic modeling is governed alongside brand and compliance. Marketing, web, and technical stakeholders align on entity definitions. Drift is prevented as content scales. Success is evaluated through inclusion, citation, and influence, not traffic alone.

THE EVIDENCE

Integrated campaigns that run a consistent story across multiple channels are about 31% more effective than disparate tactics, and every additional coordinated channel can improve ROI and effectiveness by up to 35% (1).

Multi‑channel programs also show several‑hundred‑percent higher purchase rates than single‑channel blasts, because buyers see and recognize the message instead of getting one lonely touch and moving on (1, 3).

McKinsey found that companies with integrated, insight‑driven commercial systems significantly outperform their peers in growth and resilience, because they treat marketing as part of a connected growth engine, not a set of isolated tasks (3).

On the product side, failure analyses keep finding the same thing: a big chunk of new products fail not because of marketing, but because there was weak customer understanding and poor product–market fit (2). When fit is weak, even polished campaigns struggle because you are pushing something the market doesn’t really want (2).

FRIENDLY REMINDER

Marketing is not a vending machine for quick hits. When leaders have a coherent system and invest in integrated campaigns, marketing is capable of great things . The following framework may seem basic, but it is amazing how many times they are skipped.