TL;DR: A landmark Harvard Business Review article confirms a trend we at Mercury Technology Solutions have been tracking obsessively: consumers are rapidly shifting from traditional search engines to AI platforms like ChatGPT and Gemini for product discovery and recommendations. This necessitates a new strategy focused on "Share of Model" (SOM)—how favorably and frequently your brand is cited by AI. The time to act is now, as early movers who optimize for resolution and authority will build a formidable, lasting advantage in this new landscape.
In my role, I make it a priority to analyze the foundational shifts that redefine how businesses connect with their customers. A recent, must-read article in the Harvard Business Review, "Forget What You Know About Search. Optimize Your Brand for LLMs," validates a transformation we are witnessing firsthand: the consumer journey no longer begins with a Google search bar; it starts with a dialogue with an AI.
According to research cited in the article, a 2025 survey of 12,000 consumers revealed that 58% now turn to Gen AI tools for product recommendations, a dramatic increase from just 25% in 2023. Another study highlighted a staggering 1,300% surge in AI search referrals to U.S. retail sites during the 2024 holiday season. These are not just statistics; they are signposts to a new reality.
The New Battlefield for Brand Awareness: Understanding "Share of Model" (SOM)
The HBR article introduces a crucial new metric for this era: Share of Model (SOM). It's a measure of how often, how prominently, and how favorably a brand is surfaced by Large Language Models (LLMs) in response to consumer queries. This is fundamentally different from:
- Share of Search (SOS): Which reflects human intent through search query volume.
- Share of Voice (SOV): Which measures the volume of available content about a brand.
SOM uniquely captures the AI's perception and recommendation of your brand. At Mercury, our Mercury LLM-SEO (GAIO) services are precisely designed to measure, analyze, and strategically improve your brand's SOM.
The analysis presented in the article reveals two critical realities. First, a brand's SOM can vary dramatically between different LLMs (e.g., a laundry detergent brand having 24% SOM on Llama but less than 1% on Gemini). Second, and more alarmingly, a brand can be entirely absent from a model's consideration set. As the authors rightly state, "On ChatGPT, unlike Google, there is no ‘page two.’”
Where Does Your Brand Stand? The Human-AI Awareness Matrix
A brand's visibility in the AI's "mind" can be significantly different from its real-world market share. The HBR article proposes a "Human-AI Awareness Matrix" that classifies brands into four distinct categories:
- Cyborgs: High awareness among both humans and LLMs (e.g., Tesla).
- AI Pioneers: Low general awareness but high visibility within LLMs (e.g., Rivian).
- High-Street Heroes: Established brands with high public awareness but are underrepresented by LLMs (e.g., Lincoln).
- Emergent: Brands struggling with low awareness across both humans and AI (e.g., Polestar).
This matrix is a powerful diagnostic tool for any business leader today. You must ask: where do we stand, and what is our strategy to move up and to the right?
Why Your Business Must Act on Generative AI Optimization Now
The insights from the HBR article underscore a sense of strategic urgency. This isn't a future trend to monitor; it's a present reality to act upon. Here’s why starting now is critical:
- Secure a First-Mover Advantage: The landscape of AI recommendations is new territory. Brands that successfully establish themselves as trusted, authoritative sources for LLMs today will build a compounding advantage that becomes increasingly difficult for competitors to overcome. This is a rare "ground-floor" opportunity.
- Capture a High-Value Demographic: The article highlights that consumers using LLMs for discovery are, on average, younger, wealthier, and more educated. Ceding this ground by delaying your AI optimization strategy means willingly ignoring a highly valuable and forward-thinking customer segment.
- "Sticky" Authority and AI Learning: LLMs learn and refine their understanding over time. Once your brand is established as a reliable source for a particular topic, that association can become "sticky." Delaying action allows your competitors to build that foundational trust with AI systems first, making it harder for you to break in later.
- Proactive Reputation Management: You need to actively shape the AI's perception of your brand. If you don't provide clear, structured, and authoritative information, AI models will form their understanding based on whatever incomplete, outdated, or potentially negative data they can find across the web. Our GAIO services, which include "Comprehensive Online Reputation Monitoring & Enhancement," are designed to address this proactively.
The Winning Strategy: Optimizing for Resolution, Not Just Attention
So, how do you increase your Share of Model? The HBR article's findings align perfectly with the principles we champion at Mercury. LLMs are not optimizing for attention; they are optimizing for resolution. They prioritize content that solves a user's problem or answers their question with precision and authority.
This means a shift in content strategy:
- Focus on Context and Use Cases: Instead of just proclaiming "we sell superb running shoes," the winning approach is "our carbon-plated midsole design improves performance for long-distance runners."
- Provide Proof of Expertise (E-E-A-T): A skincare brand referencing dermatologist-backed studies will outperform competitors that don't. Our Mercury LLM-SEO (GAIO) services are built around "E-A-T Amplification" to showcase these trust signals.
- Embrace Structured Content: Brands like The Ordinary, with highly structured product pages detailing ingredients and the science behind them, perform exceptionally well in AI analysis. Our Mercury Content Management System (CMS) is designed to facilitate the creation and management of such structured, AI-friendly content.
- "Narrowcast" on Pain Points: Address specific needs, questions, and tasks your audience has. This is more effective than broad, aspirational marketing messages. Our Mercury Muses AI can help identify these niche questions and draft targeted content to address them.
Navigating a Multi-LLM World
Crucially, the HBR analysis shows that each LLM applies its own unique algorithmic lens. For the travel brand Airbnb, Llama focused on the uniqueness of offerings, ChatGPT on local options, and Perplexity on flexibility.
This highlights the complexity of modern digital strategy. A one-size-fits-all approach is insufficient. At Mercury, our Mercury SEVO (Search Everywhere Optimization) Services embody this multi-platform reality, helping brands tailor their content to the nuances of dominant models while maintaining a consistent core message.
The Future of Marketing is Here
The shift from optimizing for keywords to optimizing for "problem-share" is a fundamental change in consumer behavior and marketing strategy. It requires a move from persuasion to precision, from chasing market share to solving problems authoritatively.
At Mercury Technology Solutions, we are already helping our clients navigate this new terrain. By implementing comprehensive GAIO strategies, we establish them as essential, trusted participants in the algorithmic conversations that are increasingly shaping consumer decisions. The time to optimize your brand for this AI-driven future is now.
