In this solo podcast episode, Tony Uphoff explains how the B2B buyer journey has moved from traditional SEO‑driven discoverability to AI‑driven recommendation. He shares a personal experiment in which he queried four major AI models (ChatGPT, Gemini, Perplexity, Google AI Overviews) with 19 buyer‑focused prompts and found his advisory firm appeared on only 1 of 43 category shortlists, despite his name returning ~86,000 Google results. Citing recent G2 and Semrush surveys, he shows that a growing majority of B2B buyers now start research in chatbots, and that AI‑generated shortlists heavily influence purchase decisions. Uphoff explains why AI shortlists are assembled from existing third‑party roundup lists and why stale web records cause models to recommend outdated companies. He then outlines five tactical moves to close the gap: (1) change the scoreboard to track AI recommendation frequency, (2) fix the web record (Answer Engine Optimization) before chasing shortlists, (3) publish canonical explanations of your ideas, (4) own narrow, specific buyer queries before broader categories, and (5) earn placement on the roundup/comparison pages that AI models cite. He frames these moves as actionable guidance for B2B builders, operators, and investors, and invites listeners to download his Uphoff Advisory Framework and follow the show.
Chapters
Hey, it's Tony. For the last 20 years, I've run B2B businesses that spent serious time, energy, and money on SEO. Making our content, our marketing, and our websites discoverable was the whole game. Outsmarting the search engines was how you played it. That era is ending. Buyers now ask AI who to hire, and the machine hands them a shortlist. Discoverable isn't the goal anymore. Recommended is. And most B2B companies are still keeping score the old way. So I ran the test on my own 2 businesses. Google my name and you get about 86,000 results. Ask AI the questions my buyers ask and my advisory firm made 1 list out of 43. In this episode, I'll walk through what that gap means and the 5 moves that close it. whether you're building, running, or funding a B2B business. Okay, let's get to work. This episode is titled, I Asked AI Who to Hire, It Recommended Me Once. Most B2B companies still measure whether buyers can find them. The market has moved to whether AI recommends them, how to test the gap, and what to fix. B2B buyers still build shortlists. They just don't build them alone anymore. Google Tony Uphoff and you get about 86,000 results. 35 years of bylines, press releases, bios, and CEO announcements make me discoverable, and by extension, the 2 businesses I started in February of this year, as long as someone is searching for my name. AI is a different story. This month, I asked AI the questions my audience and prospective clients ask: who to hire, who to call, what to read. 19 prompts across ChatGPT, Gemini, Perplexity, and Google AI Overviews tracked in Ahrefs Brand Radar from October 10th through the 17th produced 59 answers about my 2 businesses and me. Uphoff on media showed up in 0 of 12 category answers. Uphoff Advisory showed up in 1 of 43. Who is Tony Uphoff? Every answer— every engine answered and knew the career. The models know who I am. They almost never recommended what I've built. The gap is where B2B buying has gone, and most companies aren't measuring it yet. Search didn't die, it just changed jobs. It used to be where buyers found you. Now it's where they check what the machine recommended. In a March 2026 G2 survey of 1,076 B2B software buyers, 51% said they now start their research in an AI chatbot more often than in Google. up from 29% a year earlier. In a Semrush survey of US B2B professionals fielded in March and April 2026, the most common workflow among those who use AI at work, 41%, was to start with AI, then verify its answers through traditional search. I've been brought in to transform 2 businesses built on the last era. At Business.com and ThomasNet.com, helping B2B buyers find suppliers was the product. The function hasn't changed— connect the buyer and the seller. The form has. The machine does the finding now, and the shortlist comes back already built. That's the new sequence. The machine drafts a shortlist, the human validates it, checks the names, reads the reviews, asks a colleague, asks the model to follow up. In the same G2 survey, 69% said a chatbot led them to choose a different vendor than they had planned, and 1 in 3 bought from a vendor they had never heard of before. The buyer still makes the call, but from the machine's list. If you're not on it, you're counting on the buyer to go looking, and those numbers show how often the machine's list wins. For 2 decades, the scoreboard was discoverability. SEO taught every marketing team to rank, earn the click, and get found. Most companies carried that scoreboard straight into AI. They ask a model about themselves by name, get an accurate answer, and conclude that they're covered. That's discoverability in a new channel. The scoreboard that matters now is recommendation. When a buyer asks for your category, does the model put you on the shortlist? For most companies, the pattern in my results is a false positive. A well-known founder, CEO, or legacy brand makes the business feel discoverable. Ask the model about the leader and the answer sounds right. Ask it what a buyer would ask and the company rarely makes the list. Measure only discoverability and you'll never see the gap. Answer engines describe the company you used to be. All 3 engines that describe me still call me CEO of Pipeline360. I let its carve-out and built it as a standalone business, and since February, I've served as a board advisor. Only one of the 3 mentioned Uphoff Advisory, the firm I run now. The sources included org chart aggregators, executive databases, and old press releases. Answer engines default to what the web has said about your company most often, and most often usually means longest ago. If you've changed leadership, renamed a product, repositioned, or entered a new market in the past 2 years, assume the models are describing your old company to your new buyers. The stale record costs you twice. The machine uses it to decide whether to recommend you. The human uses it to check the machine's work. Fixing that is the job of answer engine optimization, AEO, and most companies haven't started. A Corporate Inc. study reported by eMarketer in May found only 29% of marketers actively track and measure AI visibility. The idea travels, the name stays behind. Ask these same engines what is demand as a service, and all 3 define it accurately, each one citing Pipeline360's own pages. I helped create it as the foundational strategy when we built Pipeline360. None of the answers mentioned me. And for Pipeline360, that's exactly right. The company owns the product and the category language. For any business that runs on a point of view, it's a warning. The models credit whoever publishes the clearest explanation, not whoever had the idea first. AI shortlists are built from other people's shortlists. The one time Uphoff Advisory made a list, the question was narrow. Quote, who advises founders of B2B information and data businesses, end quote. ChatGPT listed second of 4. The broad questions, the best strategic advisor for B2B media and who to call about a turnaround, returned nothing. The citations explain why. When the models answered category questions, they rarely cited the firms or newsletters they named. They cited roundups, best AI newsletters and best B2B marketing podcasts, lists from Zapier, Content Allies, Breaking B2B, The B2B Playbook, and Duple. The AI shortlist is assembled from lists other people already published. A May 2026 study by Mariana Franco and Julio da Silva, run on Friction AI's platform, found the same mechanism at scale. They sent 14,140 queries about 12 activewear brands to 5 AI engines. Nike and New Balance carry the identical Google Knowledge Graph description, footwear company. Nike appeared in about 71% of athleisure recommendation answers. New Balance almost never did. In third-party content, Nike shows up alongside Lululemon roughly 50 times more often than New Balance does. Equally discoverable, only one recommended. In The Buyer Left the Building, I argued the shortlist gets built from before a human enters the conversation. In The CPM Collapse, I called 2026 the agentic inflection for B2B research and vendor discovery. This is what both look like from the vendor side. Change the scoreboard first. If you are building, running, funding, or starting a B2B information marketing or technology business, here's the sequence. I'm running it in my own businesses now. Move 1 changes what you measure. Move 2 serves both sides because the machine and the human read the same record. Moves 3 through 5 earn the recommendation. Number 1, change the scoreboard. Rankings, traffic, and AI mentions tell you whether you're discoverable. Add the number that tells you whether you're recommended, how often you make the list when a buyer asks for your category. Report it first. Write 10 to 15 prompts the way your buyer asks them: the category, the problem, who should I hire for, and 2 or 3 that ask about you by name. Run them across ChatGPT, Gemini, Perplexity, and Google AI Overviews and track 2 numbers. How often you make the list, and whether the description is right. A tool like Brand Radar does this weekly. A spreadsheet and an hour a month works too. Number 2, fix the record before you chase the shortlist. Read the sources the models cite when they describe you. Update the pages you control, your site, LinkedIn, leadership bios, press page. Request corrections on the ones you don't, org chart aggregators, executive databases, directory listings. After any leadership change, rebrand, or new pricing model, Assume the models haven't heard. This is also the record your buyers reads when they check the machine's work. Number 3, publish the canonical version of your ideas. If you coin a term, a model, or a method, publish the definitive explanation under your brand with your name on it before anyone else writes the explainer. Then make sure the people who write about it link back to yours. Number 4, win the narrow question first. Pick the most specific way a buyer describes the problem you solve and own that phrase before you chase the broad category. Advisor for founders of B2B data businesses made the list. Strategic advisor for B2B media didn't. Narrow first, then wide. Number 5, get on the list the models read. Find the 10 to 20 roundup and comparison pages the models cite in your category. The citations are visible in the answers. Earn a place on them next to the names already being shortlisted. That's a PR and analyst relations job. Give it an owner and a target like any other pipeline source. If you run a B2B media or information business, read Move 5 from the other side. The lists the models read are published by someone. In the discoverability era, you sold traffic. In this one, you could own the inputs to the shortlist. If you're funding these businesses, add one diligence question. When a buyer asks AI for your category, how often are you on the list? A company That's missing there has a pipeline problem that hasn't reached the forecast yet. If the answer comes back as a traffic chart, they're measuring the last era. I'll rerun the test in January. Zero and one are where my two businesses start. I know which pages to fix, which phrase to own, and which list to earn. I'll rerun the same nineteen prompts in January and publish the numbers either way. The models are drafting your buyer's shortlist right now from the record the web already holds on you. Write that record on purpose, or a stale org chart will write it for you. Well, that's the episode. To download the Uphoff Advisory Framework, Discoverable Is Not Recommended, and use it in your business, go to Uphoff on Media on Substack or the framework library at uphoffadvisory.com/frameworks. If you found this useful, please do one thing: follow Uphoff on Media on Apple Podcasts or Spotify and leave a review. It helps other operators find the show. Back at it next week.
