Marketing transformation towards AI-first - Nine concrete initiatives CMOs should consider

CMOs must shift their mindset from “SEO-first” to “AI-first.” The old model of optimizing for Google search rankings is losing effectiveness. Instead, marketers must focus on structured data, AI visibility, first-party engagement, and new digital channels to maintain competitive advantage.

By taking short-term defensive measures while investing in long-term AI-native strategies, marketing organisations can mitigate traffic losses and capitalise on AI-driven customer engagement.

Shifting from Traditional SEO to AI Indexability

Traditional SEO relies on optimizing web pages for search engines like Google by using keywords, backlinks, and structured data (e.g., schema markup). However, as LLMs become more dominant in answering queries, the focus will shift to optimizing content so that AI models can accurately retrieve, understand, and reference it.

LLMs don’t work like search engines—they don’t just index and rank pages. Instead, they analyse large corpora of text, detect semantic relationships, and generate responses based on training data or retrieval mechanisms. To ensure content remains authoritative and visible within AI-generated responses, businesses will need to optimise for AI comprehension rather than just search engine algorithms.

Technical Example: AI-Optimized Content in Action

Considering an example from a financial advisory website that provides investment insights.

Traditional SEO Approach:

A website might optimize for keyword-based SEO using:

• Title: “Best Investment Strategies for 2025”

• Meta description: “Discover top-performing investment strategies for high returns in 2025.”

• H1 Heading: “Investment Strategies for 2025”

• Content Optimization: Repeating phrases like “best investment strategies” to rank higher.

AI-Optimized SEO Approach:

To ensure LLMs accurately reference and cite the content, the website should:

1. Use Structured Data in AI-Friendly Formats:

   1. Implement schema.org metadata to explicitly define financial terms, data sources, and authorship.

   2. Example using JSON-LD for structured financial data:

<code>
{
	"@context": "https://schema.org",
	"@type": "Article",
	"headline": "Best Investment Strategies for 2025",
	"author": {
    	"@type": "Person",
    	"name": "John Doe"
  	},
	"publisher": {
		"@type": "Organization",
		"name": "Finance Insights",
		"url": "https://financeinsights.com"
	},
	"datePublished": "2025-01-10",
	"dateModified": "2025-02-05",
	"mainEntityOfPage": {
    	"@type": "WebPage",
		"@id": "https://financeinsights.com/investment-strategies-2025"
	}
}
</code>

2. Ensure AI Models Can Reference the Content Easily:

   1. Use clear citations, references, and sources that LLMs can recognize.

   2. Instead of vague statements, use structured claims:

      Bad Example: “Investing in AI stocks is a smart move.”

      Better Example: “According to a Goldman Sachs report (2024), AI-related stocks are expected to grow by 20% annually over the next five years.”

3. Use Embedding and AI-Compatible Summaries:

   1. Create an AI-friendly executive summary with bullet points.

   2. Use embeddings (e.g., OpenAI’s text-embedding-ada-002) to structure content for AI retrieval.

   3. Provide machine-readable summaries in XML or JSON formats.

4. AI-Compatible Citations & Linkable Content:

   1. Encourage linkability by structuring articles with direct citations.

   2. Example:

<code>
<p>According to a study by the IMF (2024), GDP growth is expected to reach 4.2% in emerging markets.</p>
<a href="https://www.imf.org/research2024">Read the full IMF study</a>
</code>

5. Optimize for AI Summarization:

   1. Structure content logically with clearly defined sections.

   2. Use bullet points and numbered lists to improve AI understanding.

   3. Example:

      1. Bad example: “Many experts suggest diversifying your investments.”

      2. Better example:

         “Financial experts recommend these three diversification strategies:

         1. Allocate 40% to equities.

         2. Invest 30% in fixed income.

         3. Maintain 30% in alternative assets.”

By structuring content in this way, AI models will:

• Retrieve and cite information more accurately.

• Reduce hallucination risks (misquoting or generating false attributions).

• Recognize authoritative sources (websites that consistently provide structured, AI-optimized data will be cited more often).

• Enhance brand visibility in AI-generated responses.

This marks a fundamental shift from traditional keyword stuffing to structured, AI-legible, fact-based content, ensuring that businesses remain referenced in AI-driven searches and chatbots.

SEO is shifting from keyword-based ranking strategies to AI-friendly content structuring

Below summary highlights how SEO is shifting from keyword-based ranking strategies to AI-friendly content structuring that prioritizes semantic clarity, factual accuracy, and structured metadata to ensure proper indexing and citation by AI models.

Optimization Tactics	Traditional SEO Best Practice	AI Indexing Best Practice
Keyword Optimization	Use primary and secondary keywords strategically in titles, headers, and body text.	Focus on semantic relevance; ensure content is structured with clear topic hierarchies.
Metadata & Structured Data	Implement schema.org markup (e.g., Article, BreadcrumbList, FAQPage).	Use structured JSON-LD metadata to define authorship, credibility, and factual claims.
Content Formatting	Optimize readability with H1-H6 headings, short paragraphs, and bullet points.	Use AI-friendly summaries, numbered lists, and explicit statements to improve model comprehension.
Backlinks & Citations	Build backlinks from high-authority domains to improve credibility.	Provide direct citations with machine-readable sources (e.g., DOI, permalinks, reference tags).
Authority & Credibility	Showcase expertise with author bios, domain authority, and verifiable sources.	Use structured references and avoid ambiguous claims; ensure content is fact-based and source-linked.
Content-Length & Depth	Prioritize long-form content (1,500+ words) to rank for in-depth topics.	Focus on structured insights with modular content (clear subtopics and AI-parsable sections).
Internal Linking & Navigation	Improve internal linking for site discoverability and reduce bounce rates.	Use structured topic relationships to help AI understand content contextually.
User Engagement & Signals	Optimize for low bounce rates, high time-on-page, and interactive elements.	Ensure AI-relevant engagement signals, such as structured Q&A sections for conversational models.
Image & Multimedia SEO	Use alt text, captions, and compressed images for better indexing.	Provide text-based descriptions of key data points to ensure AI can interpret image-based content.
Page Speed & Mobile Optimization	Optimize for fast load times and responsive design for better rankings.	Ensure AI-accessible versions of content (e.g., plain-text summaries for AI scrapers).
AI-Compatible Summarization	Use meta descriptions and snippet optimization for featured search results.	Provide clear AI-parsable summaries in XML/JSON for LLMs to extract factual information.

Impact on Marketing Organizations & CMO Actions (Short & Long Term)

The decline in organic traffic due to AI-generated search results and the increased reliance on structured data for AI indexing will significantly impact marketing organizations in several ways:

1. Reduced Organic Traffic & SEO ROI

   1. Traditional SEO strategies focused on keyword ranking will deliver diminishing returns.

   2. Websites relying on organic traffic (blogs, news sites, e-commerce) will see lower conversion rates.

   3. AI-powered search results may provide answers without directing users to company websites, reducing engagement.

2. Shift from Search to AI-Optimized Content

   1. AI-driven search engines prioritize structured, factual, and well-cited content over keyword-optimized articles.

   2. Organizations need to ensure their content is designed to be cited directly by AI models.

3. Increased Dependence on Paid & Alternative Channels

   1. As organic traffic declines, businesses will rely more on paid advertising (Google Ads, social media ads) to maintain visibility.

   2. Companies must diversify their digital marketing beyond Google Search, including social media, influencer collaborations, and community-driven marketing.

4. Rise of Conversational & AI-Driven Marketing

   1. AI chatbots and voice search will play a bigger role in customer interactions.

   2. Marketers must optimize content for AI chat systems (ChatGPT, Google Bard, Bing Chat) by providing structured data and AI-parsable content.

CMO Action Plan

CMOs must shift their mindset from “SEO-first” to “AI-first.” The old model of optimizing for Google search rankings is losing effectiveness. Instead, marketers must focus on structured data, AI visibility, first-party engagement, and new digital channels to maintain competitive advantage.

CMOs must rethink their digital marketing strategy with both short-term adjustments and long-term transformations.

Short-Term (0-12 Months)

1. Optimize for AI Search & Structured Data

   1. Implement structured data (schema.org, JSON-LD, RDFa) for products, services, and FAQs.

   2. Ensure content includes well-cited, AI-readable factual information to increase the chances of AI referencing it.

2. Monitor Traffic Declines & Adjust Budget Allocation

   1. Track organic search traffic using Google Search Console & analytics tools to understand the impact of AI-generated results.

   2. Shift budgets towards alternative acquisition channels like social media, influencer partnerships, and email marketing.

3. Invest in AI-Generated Content & Automation

   1. Leverage AI tools to generate high-quality, structured content that aligns with how AI systems retrieve information.

   2. Automate SEO updates, meta descriptions, and structured data enhancements.

4. Increase Focus on Branding & Owned Channels

   1. Build strong brand authority so that AI models recognize and prioritize your company in search.

   2. Strengthen owned marketing assets (email lists, podcasts, YouTube, LinkedIn thought leadership).

Long-Term (12+ Months)

1. Develop AI-Native Marketing Strategies

   1. AI-first content strategies: Shift from keyword-based SEO to knowledge-based, structured, and AI-retrievable content.

   2. Conversational search optimization: Optimize for AI chatbots and voice search (e.g., answering queries in a structured Q&A format).

2. Integrate AI into Customer Engagement

   1. Deploy AI-driven chatbots on websites & social media to handle customer queries.

   2. Invest in personalized AI-powered recommendations & product discovery tools.

3. Shift Towards Community & First-Party Data

   1. As search traffic declines, build direct customer relationships through email, loyalty programs, and private communities.

   2. Collect and use first-party data to maintain engagement without reliance on external search platforms.

4. Leverage AI for Hyper-Personalized Content & Ads

   1. AI-driven hyper-personalized ads (predictive customer behavior models, automated creative generation).

   2. AI-assisted content marketing that generates topic-cluster strategies rather than keyword-driven content.

5. Explore New Search Alternatives & AI Partnerships

   1. Optimize for alternative search engines (e.g., Bing, DuckDuckGo, Brave).

   2. Partner with AI ecosystem providers to ensure your content is referenced in AI-driven search engines and assistants.

By taking short-term defensive measures while investing in long-term AI-native strategies, marketing organizations can mitigate traffic losses and capitalize on AI-driven customer engagement.