How to Get Recommended by AI Chatbots, According to Dan Sherrard-Smith

In a recent LinkedIn post, Dan Sherrard-Smith discusses a novel strategy for business leaders to leverage artificial intelligence, specifically large language models (LLMs) like ChatGPT, to generate leads and gain visibility. He introduces the concept of “GEO,” or Generative Engine Optimisation, as the LLM equivalent of traditional Search Engine Optimisation (SEO) for Google.

Sherrard-Smith argues that optimizing one’s LinkedIn presence can lead to recommendations from AI tools when users query them for professional services. He notes that this isn’t a matter of luck but a repeatable strategy, observed across hundreds of clients.

“I’ve now seen this happen across enough of our 400+ clients to know it’s not luck. It’s a strategy. And most founders have no idea it exists.”

The Rise of Generative Engine Optimisation (GEO)

The core of Sherrard-Smith’s argument is that LinkedIn has become a primary source for AI models seeking professional information. He cites analysis from Profound, which found LinkedIn to be the most cited source for professional queries across major LLMs.

As Dan Sherrard-Smith explains, when an AI is asked to identify the best professional for a specific task, it scans LinkedIn for consistent expertise. The goal of GEO is to ensure that when such queries are made, the user’s profile and content are structured in a way that makes them a prime candidate for recommendation.

The ‘Proof Test’ for AI Visibility

To illustrate the current state of AI recommendations, Sherrard-Smith proposes a simple test. He encourages readers to query AI chatbots like Claude or ChatGPT with a specific service and industry, such as “Who is the best [your service] in [your industry]?”

According to Dan Sherrard-Smith, if one’s name does not appear in the results, their content is not yet optimized for these AI engines. He shares an anecdote about a client named Jessica who, after four months of consistent posting, saw her name appear first in such a query, leading to a significant client acquisition.

“If your name doesn’t come up, your content isn’t structured for it yet. Jessica did this after 4 months of consistent posting. Her name came up first. That recommendation led to a £3.5K client who arrived pre-sold.”

Three Pillars of GEO

Sherrard-Smith outlines three key areas for optimizing one’s LinkedIn presence for AI recommendation:

1. The Profile as a Landing Page

He emphasizes that a LinkedIn profile should function as a landing page, not merely a CV. LLMs seek clear, citable statements like “I help X do Y,” rather than lists of past job titles. Adding concrete evidence, numbers, and outcomes is crucial, as AI looks for proof of capability.

2. Content Strategy for AI Queries

The content posted should directly address the specific questions that ideal clients are asking AI chatbots. Sherrard-Smith advises against generic posts, focusing instead on answering real user queries. Consistency is key, with a significant percentage of cited authors posting frequently.

“75% of cited LinkedIn authors post 5+ times in four weeks. Consistency is how LLMs verify you’re a real authority, not someone who posted twice in 2023.”

He also notes that virality and likes are less important to LLMs than relevance to the query.

3. The ‘Pre-sold’ Client Effect

Sherrard-Smith highlights the distinct advantage of being recommended by an AI. Unlike clients found through traditional search, who might compare multiple options, an AI recommendation presents the user as the definitive solution. This leads to clients who are already convinced, as exemplified by Jessica’s client who was ready to pay without needing a formal proposal.

“When ChatGPT recommends you, you are the answer.”

In conclusion, Dan Sherrard-Smith advocates for a proactive approach to LinkedIn content and profile optimization, framing it as a strategic imperative for founders seeking to leverage the growing influence of AI in lead generation and professional recommendations.