Conductor co-founder Seth Besmertnik argues that brands must transition to an AI-first search strategy to capture recommendation share, a shift he has overseen during Conductor’s 20-year history. The primary tension lies in moving beyond a “no budget” practitioner loop to secure a seat at the executive table by aligning search with a holistic brand narrative. This strategy suits enterprise brands seeking AI visibility, while those focused solely on legacy link building may find less value here.
Who should act on the AI search optimization strategy and who can skip it
Readers who should act on the AI search optimization strategy
- In-house SEO managers should act on this to move beyond tactical tasks and align AI efforts with brand narrative and recommendation share.
- Marketing Directors should act on this to understand how AI changes the ROI of SEO and how to measure it through brand representation.
- SEO Agency owners should act on this to offer high-value consulting in the AEO space and ensure technical compliance for non-Google crawlers.
- Technical SEO specialists should act on this to ensure site crawlability and rendering are optimized for OpenAI and Claude specifically.
Readers who should skip the AI search optimization strategy
- PPC and Social Media Managers should skip this and look at Search Engine Land’s paid ads or social media content instead.
- Users seeking a tool demo should skip this and look at a product demo or feature launch guide for Conductor’s software.
What to do next about the AI search optimization strategy
- Audit existing content for persona-specific personalization by identifying at least 2 distinct personas like senior citizens vs. high schoolers.
- Verify technical web standards for non-Google crawlers by checking rendering and crawlability for OpenAI and Claude.
- Establish a “recommendation share” reporting framework by defining how AI engines describe and recommend the business in their outputs.
Conductor’s transition from link building to AI Engine Optimization (AEO)
Conductor’s origins began as Link Experts, or “Linkerts,” a company founded by Seth Besmertnik at age 23 to solve the problem of deep-page indexing. The original model involved building a network of high-authority sites like Forbes and the Wall Street Journal to help big companies gain visibility. This legacy approach eventually gave way to a software platform launched in 2011, which grew to $30 million in revenue in just 3.5 years. Conductor later sold the business to its third-biggest customer in 2018, only for the employees to buy the company back in late 2019 or early 2020.
The evolution from link building to AEO represents a fundamental shift in how search value is captured. While link building focused on the “what” of the internet (the link), AEO focuses on the “how” (the narrative). The current leadership, including CEO Wei Zheng, transitioned the company to a data platform architecture to manage 15 years of data. This transformation allows brands to move from simply existing in a search index to being actively recommended by AI engines.
What is AEO?
AEO stands for AI Engine Optimization, which is the process of optimizing content to be recognized and recommended by AI search engines. It moves beyond traditional rankings to ensure that LLMs like ChatGPT or Claude accurately represent a brand’s value. This matters because as users move toward AI-generated answers, the brand’s narrative becomes its primary discovery mechanism.
Prioritizing recommendation share over traffic metrics
Recommendation share serves as the primary KPI for the AI era, measuring how often and how accurately an AI engine recommends a specific business. Traditional traffic metrics, which measure raw clicks, are becoming less relevant as search moves toward a zero-click reality. The shift is driven by the fact that users no longer need to click a link if the AI provides the final answer directly. Recommendation share ensures that when an AI speaks, it speaks in the brand’s favor.
What is Recommendation share?
Recommendation share is a metric that measures the extent to which an AI engine includes a specific business in its narrative or outputs. It is calculated across multiple AI engines to determine how often a brand is recommended to users. This matters because it provides a tangible KPI to show the effectiveness of an AEO strategy beyond traditional click-through rates.
AI search optimization relies on a different set of requirements than standard SEO. If you already focus on traffic metrics, none of this changes your current reporting, but it may change your long-term strategy.
The cost of being wrong on this metric is a loss of narrative control. If a brand fails to influence how an AI describes it, the AI may provide a generic or incorrect answer, potentially recommending a competitor who has better-packaged content. This holds while the brand is still in the discovery phase; once a user has a preference, the narrative becomes secondary to the existing relationship.
Why technical standards are critical for non-Google crawlers
Technical web standards act as the floor for LLM visibility because AI engines like OpenAI and Claude lack the rendering maturity of Google. Google’s crawlers can often make up for a lack of technical competency by rendering pages more effectively. In contrast, non-Google crawlers require a cleaner, more structured data architecture to interpret brand information correctly. This makes strict adherence to standard technical protocols a non-negotiable requirement for AI search.
The mechanism of this difference is rooted in how the models are trained and served. AI engines act as aggregators of the web, requiring content that is easily consumable by a machine. Think of the AI as a village elder: you ask a question, and they give you an answer directly without showing you the library of books they consulted. If the data is messy, the elder gives a wrong answer. Because LLM crawlers are less sophisticated at interpreting complex visual layers or heavy scripts, the data must be “packaged” in clean technical web standards to be consumed.
Guessing wrong on technical standards means your brand remains invisible to LLMs like OpenAI or Claude. The cost of poor rendering is a total lack of representation in the AI’s output. To fix this, practitioners must audit their site’s technical compliance specifically for non-Google bots.
Packaging content for AI consumption and personalization
Packaging a company into consumable content allows AI engines to provide highly personalized results for diverse user personas. A brand should not just provide one generic answer for every query. Instead, content must be structured to address specific needs, such as a senior citizen asking about the safety of a sport versus a high school student asking if the sport is for “old people.” By creating distinct paths for different users, a brand ensures the AI has the correct information for every specific prompt.
The path to winning AEO involves aligning this content with a holistic digital brand strategy. This cannot be a siloed task for a single person in a corner. It requires the CMO and digital leadership to ensure that the brand narrative is consistent across all touchpoints. When the brand narrative is unified, the AI’s ability to provide accurate, personalized recommendations increases significantly.
Conductor has not announced pricing tiers for its AI optimization platform
- Conductor has not announced the specific pricing tiers for its AI optimization platform. Readers should check the current service catalog for enterprise packages.
- The specific ROI percentage for increased recommendation share has not been established. Practitioners should establish a baseline narrative score for their brand to measure growth.
Sarah’s strategy for a pickleball brand
Suppose Sarah manages the SEO for a high-end pickleball equipment company. She identifies two core personas: senior citizens and high school athletes. Sarah creates two distinct FAQ sections for the “how to play” page, one tailored to the safety concerns of seniors and one to the “is it for old people” questions from students. She audits the site’s technical standards to ensure they are optimal for non-Google crawlers like OpenAI and Claude. She begins reporting on “recommendation share” in AI summaries instead of just organic clicks.
The company’s products are frequently recommended by AI for both seniors and students, increasing brand trust and sales. If Sarah had only optimized for general keywords, the AI would provide a generic answer. This would likely result in the AI recommending a competitor who provided more specific, persona-based answers for those two groups.
Success in the next decade depends on a brand’s ability to be accurately narrated by AI agents rather than just appearing in a list of blue links.
Frequently Asked Questions
How does AI search differ from traditional SEO?
AI search is more about how the AI talks about, recommends, and narrates a business, whereas traditional SEO was often more focused on rankings and traffic. While traditional SEO relies on search rankings, AI search focuses on the narrative the model builds around a brand.
Why is technical SEO more important for AI than for Google?
Technical SEO is more important for AI because Google has mature web crawlers that can make up for some lack of technical competency, while LLM crawlers like OpenAI or Claude do not. Ensuring technical web standards are met is the primary gatekeeper for LLM visibility.
What is the best way to win in AI search?
Winning in AI search is about building a holistic digital brand, ensuring content is consumable by AI, and providing highly personalized experiences. A brand must ensure that its information is packaged so that AI engines can easily include it in their recommendations.
How much does it cost to implement an AI search strategy?
Conductor has not announced a specific cost for implementing an AI search strategy, as it requires a shift in resources and alignment rather than just throwing money at it. Practitioners should consult with their internal teams to determine the necessary resource shifts for brand alignment.
Source: Why Conductor’s Co-Founder Stepped Down and What’s Next for AI Search (Search Engine Land).