AI Visibility Podcast with Jason Ai Wade of BackTier

Andrew Shotland has spent roughly two decades in search.Right now, he says it is one of the most interesting periods the industry has seen in years. SEO is still here, but the machines interpreting businesses, services, locations, and recommendations have changed.In this episode of the AI Visibility Podcast, Andrew joins Jason AI Wade to talk about what is actually changing—and what still works.Andrew’s basic model is straightforward: SEO has always been about helping a machine understand what a business does and when it should be surfaced. That principle still holds. What has changed is the system doing the understanding.For local businesses, that means going beyond generic category language.If a plumber does water heater replacement, storm drains, or emergency service, those specific attributes need to exist clearly on the company’s own pages and across the external sources AI systems may use as evidence. Andrew argues that citations, listings, and third-party mentions are not just consistency signals anymore—they can help establish what a business should actually be associated with.The strongest example in the episode comes from a 160-location company.Andrew’s team found a visibility gap around construction job-site bathroom and toilet queries. They took 20 locations, added relevant language to their Google Business Profile descriptions and local citation ecosystem, and watched what happened.Within days, visibility moved in Google AI Mode. Later, those same test locations began gaining visibility in ChatGPT while the untouched locations initially stayed flat.Then the experiment got more interesting.About a month later, the broader location network began gaining visibility around the same concept. Andrew’s theory was that enough pages had connected the brand with that service category that the association began to spread across the larger entity.The episode also gets into competitor-adjacent content and conversational search.That leads to one of the larger shifts in AI-era discovery:Instead of optimizing only for a keyword, businesses increasingly need to think about the sequence of questions a person may ask before reaching a decision.Andrew calls this thinking through the “infinite conversational paths.”The discussion also covers agentic SEO, automation, content-gap analysis, entity corroboration, and the strategic value of using AI to reduce repetitive work.Andrew’s preference is not to pocket the saved time. It is to use that time to investigate harder questions, run better experiments, and improve the product or service being delivered.The larger lesson is simple:There is no finished playbook yet.The companies that learn fastest will likely be the ones willing to test, measure, and take more intelligent swings.As Andrew puts it, even the best baseball players do not hit every pitch. The advantage comes from taking enough swings to find what works.Andrew ShotlandFounder and CEO of Local SEO Guide.Andrew works across local SEO, multi-location search, AEO, and AI visibility. His current focus is understanding how businesses can become easier for Google, ChatGPT, and other AI systems to interpret and recommend.He has been working in search for roughly two decades and describes the current shift as one of the most interesting periods of his career.Local SEO in the age of AIGoogle AI Mode and ChatGPT visibilityLocal citations as AI evidenceService and attribute distributionEntity corroborationCompetitor-adjacent contentConversational search pathsContent-gap analysisAgentic SEOAI-powered automationMulti-location testingWhy experimentation matters more than a static GEO playbookLocal SEO Guidehttps://www.localseoguide.com/Andrew Shotland on LinkedInAndrew says LinkedIn is his primary social platform and where he posts most of his ideas and commentary.

Why Artificial Intelligence Will Decide Which Companies Get Discovered — Chapter OneFor decades, businesses competed for visibility in search engines. Success meant ranking on Google, attracting website visitors, and converting traffic into customers.Artificial intelligence is changing that relationship.In Chapter One of Why Artificial Intelligence Will Decide Which Companies Get Discovered, Jason A.I. Wade explores a fundamental shift in digital discovery: AI systems are increasingly becoming intermediaries between consumers and businesses.Instead of searching through websites, customers can ask AI assistants to identify companies, compare services, evaluate qualifications, and recommend providers.That changes the competitive landscape. A business may have an excellent website, strong traditional SEO, and years of experience yet remain absent from AI-generated recommendations.The emerging challenge is AI Visibility: ensuring that artificial intelligence can discover, correctly identify, understand, and substantiate information about a company.This opening chapter examines why traditional search visibility alone may no longer be sufficient and why businesses need to understand how AI systems select the entities and sources that appear in their answers.The central question: What happens to a business when AI becomes the customer's first point of contact—and never mentions its name?Jason A.I. Wade is a Central Florida-based AI entrepreneur, AI Visibility architect, and host of the AI Visibility Podcast. His work focuses on artificial intelligence, AI SEO, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and digital entity recognition.Through NinjaAI.com and BackTier, he develops projects and research examining how AI systems discover, classify, cite, and recommend businesses.NinjaAI: https://ninjaai.comJason A.I. Wade: https://jasonwade.comBackTier: https://backtier.comProduced by BackTier Media, an independent media initiative exploring artificial intelligence, business, digital identity, and the technologies reshaping how information is discovered and trusted.

Four AI Engines. One Question. Four Different Answers.Ask ChatGPT, Gemini, Claude, and Perplexity the same question. Will they recommend the same businesses, cite the same sources, or reach the same conclusions?Often, they won't.In this episode of the AI Visibility Podcast, Jason A.I. Wade examines one of the fundamental challenges of AI search: different AI systems can produce substantially different answers to identical questions.For businesses investing in AI SEO, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO), this creates a serious measurement problem. Being visible in one AI engine does not guarantee visibility in another.AI visibility isn't a single ranking. It's a changing landscape of models, retrieval systems, sources, and recommendations.The question isn't simply whether AI can find your business. It's which AI systems recognize it, what they believe about it, and whether they recommend it consistently.Jason A.I. Wade is an AI Visibility architect, entrepreneur, and host of the AI Visibility Podcast. Based in Central Florida, he researches how artificial intelligence discovers, classifies, cites, and recommends businesses and digital entities.Through NinjaAI.com and BackTier, Jason explores AI search, machine-readable identity, digital authority, and the evolving relationship between businesses and AI-powered discovery.NinjaAI: https://ninjaai.comJason A.I. Wade: https://jasonwade.comBackTier: https://backtier.comThe AI Visibility Podcast is produced by BackTier Media, exploring artificial intelligence, search, digital identity, emerging technology, and the systems increasingly influencing what people discover and trust.

What actually makes ChatGPT or Google AI recommend one local business instead of another?Andrew Shotland, founder and CEO of Local SEO Guide, joins Jason AI Wade to break down what his team is testing right now across local SEO, AI visibility, citations, service pages, and multi-location businesses.Andrew’s core point is simple: the fundamentals of SEO still matter. A machine has to understand what your business does before it can recommend you. The difference now is that AI systems are assembling that understanding from more pieces of evidence and using it inside conversational answers.That changes how businesses should think about visibility.It is not enough for a plumber to say, “We’re a plumber.”If the business wants to be recommended for water heater replacement, emergency service, or storm drains, those specific services need to appear clearly across the company’s website and the third-party sources AI may use to corroborate them.Andrew describes those external citations almost like additional sides of a die: the more relevant sources that connect a business with the right location, service, and attributes, the more opportunities an AI system has to encounter the same association.Then he shares a real test.His team analyzed search data across a 160-location company and found a missing concept around construction job-site bathrooms and toilets. They added that language to the Google Business Profiles and local citation network for 20 locations.Within days, visibility changed in Google AI Mode.Later, the test locations began improving in ChatGPT while the untouched locations initially remained flat.Roughly a month later, visibility began increasing across the wider network as well.Andrew’s working theory: enough pages had connected the brand with the concept that AI systems began applying that relationship more broadly across the entity.The conversation also gets into competitor-focused content, local search strategy, agentic SEO, automation, content-gap analysis, and what Andrew calls the “infinite conversational paths” created by AI search.The practical lesson is not to wait for a perfect GEO playbook.Test something.Measure whether visibility changes.Then apply what works.Andrew compares the process to baseball: even a great hitter misses most of the time. The advantage comes from taking enough intelligent swings.Andrew ShotlandFounder and CEO of Local SEO Guide.Andrew has worked in search for roughly two decades and now focuses heavily on local SEO, AEO, multi-location businesses, and how AI systems understand and recommend companies.He describes the current period as one of the most interesting moments he has experienced in search.AI recommendations for local businessesLocal SEO and GEOGoogle AI ModeChatGPT visibilityLocal citationsEntity corroborationService-page strategyContent gapsCompetitor alternativesConversational searchAgentic SEOMulti-location testingAI visibility experimentsAndrew Shotland / Local SEO Guidehttps://www.localseoguide.com/Andrew Shotland on LinkedInAndrew identifies LinkedIn as his main social platform and where he posts most often.AI Visibility PodcastBackTier

Why Consistent Anchor Information Matters in AI VisibilityOne of the most overlooked problems in AI visibility is consistency.Companies tend to think about their website, LinkedIn page, directory listings, biographies, press mentions, podcasts, and third-party profiles as separate marketing assets. AI systems do not necessarily experience them that way. They encounter all of these sources as evidence about the same entity.That creates a simple but important problem: if the basic facts keep changing depending on where the system looks, confidence in the entity can weaken.I call these basic facts anchor information.Anchor information is the relatively stable information that helps a machine determine who or what an entity is. For a company, that includes the company name, founders, location, primary category, core services, major products, important dates, leadership, and the language used to describe what the business actually does.For a person, it may include name, professional role, company relationships, areas of expertise, location at an appropriate level, published work, and other persistent associations.None of this sounds particularly sophisticated. That is exactly why companies underestimate it.The problem begins when the same organization describes itself five different ways across five different sources. One profile says the company is an SEO agency. Another describes it as an AI consultancy. The website calls it an AI visibility company. An older directory calls it a digital marketing agency. The founder's biography uses yet another category.A human can usually reconcile those descriptions.A machine has to decide whether they represent complementary facts, historical changes, marketing language, or genuinely different classifications.That uncertainty becomes more important as AI systems move beyond finding pages and begin comparing entities, selecting sources, recommending companies, and answering questions directly.The objective is not to make every page identical. That would be unnatural and unnecessary.It is an information architecture problem.Before an AI system can cite a business, recommend it, or associate it with a particular area of expertise, the system first has to determine what the entity is and what facts about it can be trusted.That starts with anchors.A company should be able to answer a basic question: if an AI system encountered our website, founder biography, LinkedIn profile, podcast appearances, business directories, press coverage, and third-party references, would those sources collectively describe the same organization?His central thesis is straightforward: before a company can influence how AI describes it, the company first has to make the truth about itself clear enough for machines to understand.Jason Wadehttps://jasonwade.comBackTierhttps://backtier.comNinjaAIhttps://ninjaai.com