Uphoff on Media Podcast

About Uphoff on Media Podcast

Uphoff on Media podcast: A practical take on how technology is rewriting the way B2B companies get built and run, and how to get ahead of it. For those founding, running, and funding B2B information, marketing, and technology companies. tonyuphoff.substack.com (https://tonyuphoff.substack.com?utm_medium=podcast)

Publisher

Tony Uphoff

Category

business · technology

Language

en

Explicit

No

First episode

19 Feb 2026

Latest episode

21 Oct 2026

Latest episodes

65 episodes in the feed.

1. 

   21 Oct 2026

   I Asked AI Who to Hire. It Recommended Me. Once

   B2B buyers still build shortlists. They just don’t build them alone anymore. Google “Tony Uphoff” and you get about 86,000 results. Thirty-five years of bylines, press releases, bios and CEO announcements make me discoverable, and by extension the two businesses I started in February. 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 10 to 17, produced 59 answers about my two 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 engine that answered knew the career. The models know who I am. They almost never recommend what I’ve built. That gap is where B2B buying has gone, and most companies aren’t measuring it. Search didn’t die. It 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 two 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 the shortlist. The human validates it: checks the names, reads the reviews, asks a colleague, asks the model a follow-up. In the same G2 survey, 69% said a chatbot led them to choose a different vendor than they had planned, and one in three 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 two 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 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 three engines that described me still call me CEO of Pipeline360. I led its carve-out and built it as a standalone business, and since February I’ve served as a board advisor. Only one of the three 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 two 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 Ink 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 the same engines “What is Demand-as-a-Service?” and all three 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 mention 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: “Who advises founders of B2B information and data businesses?” ChatGPT listed it second of four. The broad questions, the best strategic advisors 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 Dupple. The AI shortlist is assembled from lists other people already published. A May 2026 study by Maryanna Franco and João da Silva, run on Friction AI’s platform, found the same mechanism at scale. They sent 14,140 queries about twelve activewear brands to five 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 (https://tonyuphoff.substack.com/p/the-buyer-left-the-building), I argued the shortlist gets built before a human enters the conversation. In The CPM Collapse (https://tonyuphoff.substack.com/p/the-cpm-collapse-b2b-medias-25-year), I called 2026 the agentic inflection for B2B research and vendor discovery. This is what both look like from the vendor’s side. Change the scoreboard first If you’re building, running, funding, or starting a B2B information, marketing or technology business, here’s the sequence. I’m running it on 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. 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.” Add two or three that ask about you by name. Run them across ChatGPT, Gemini, Perplexity and Google AI Overviews and track two 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. 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 buyer reads when they check the machine’s work. 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. 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. 5. Get on the lists 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. Here is an Uphoff Advisory Framework you can download and use in your business. Run both tests on yourself and your top three competitors, then find your square. Each one tells you what to run first, what the risk is and what to watch. Discoverable Is Not Recommended View the full Uphoff Advisory Framework library at uphoffadvisory.com/frameworks (https://www.uphoffadvisory.com/frameworks/). 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 lists to earn. I’ll rerun the same 19 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. Keep going: B2B Branding Has a New Audience. Most Companies Are Talking to the Wrong One (https://tonyuphoff.substack.com/p/b2b-branding-has-a-new-audience-most) and B2B Thought Leadership and Content Marketing Are Broken (https://tonyuphoff.substack.com/p/b2b-thought-leadership-and-content) The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. “Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC (https://uphoffadvisory.com/), a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com (https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

2. 

   19 Oct 2026

   Before You Flatten the Org Chart, Make Sure You Can Measure What AI Delivers

   I’m a fan of radical thinking. It forces you to test assumptions you didn’t know you were making. You don’t always have to agree with it. Agreement was never the point. The point is whether it makes you think. Jack Dorsey and Roelof Botha’s essay, From Hierarchy to Intelligence (https://block.xyz/inside/from-hierarchy-to-intelligence), did that to me. It lays out how Block is rebuilding itself as a company organized as intelligence rather than hierarchy, and it came a month after Block cut its workforce by about 40% (https://www.theblock.co/post/391520/block-inc-slashes-40-staff-jack-dorsey-pushes-smaller-flatter-ai-strategy). Most of what gets written about AI and organizations stops at copilots, a smarter assistant bolted onto the same old structure. They go further and ask whether the organization itself still needs to exist in its current form. It pushed me past the easy cliché that AI just means fewer middle managers. Here’s where that thinking led. The oldest constraint in business Dorsey and Botha trace two thousand years of the same fix. The Roman legion built command around eight soldiers to a tent. The Prussian General Staff existed to route information up and down the chain. Daniel McCallum drew the first org chart in the 1850s to keep a 500-mile railroad from killing people. Every version since, from Taylor’s pyramid to the matrix, worked around one constraint: a human brain can only hold so much context, so organizations add layers, and layers slow down information. Spotify, Zappos and Valve tried to break the pattern. All three scaled back into hierarchy or hit a ceiling. Dorsey and Botha’s claim is that AI removes the constraint, not the org chart around it. What Block actually built Block is building a company world model (what the business knows about its own operations) and a customer world model (what it knows about the people transacting with it), and using both to compose solutions directly, without routing decisions through management. What’s left are three roles: * Individual contributors who build * DRIs who own outcomes across whatever teams a problem touches * Player-coaches who develop people instead of running status meetings No middle layer required. Money is the one signal nobody fakes Here’s the sentence that matters most in the whole piece. “Money is the most honest signal in the world.”Jack Dorsey and Roelof Botha, From Hierarchy to Intelligence People lie on surveys. They abandon carts. They ignore ads. But when they spend, that’s the truth. Block can build a company-as-intelligence because it sits on both sides of millions of transactions every day, through Square and Cash App. That gives it a signal almost nobody else has: honest, at scale, compounding. Where this breaks for the rest of us That’s also where the model runs into trouble for most of the people reading this. Very few B2B information, marketing, or technology companies have anything close to a money-grade signal. What they have instead is engagement data: opens, clicks, content consumption, lead scores, intent signals bought from a vendor. I’ve run enough pipeline reviews to know how much of that gets gamed on purpose, by the same marketing teams reporting on it. The essay’s point about people lying on surveys is more true of modern B2B marketing than its authors probably intended. A lead score isn’t a fact about someone’s business. It’s a fact about how well your content ranks. Most B2B signal sits at the wrong end of the spectrum That gap is the actual work before anyone in B2B tries to run Block’s playbook. Think of it as a spectrum, honest signal on one end and performative signal on the other. Self-reported data and form fills sit at the weak end: cheap to produce, cheap to fake. Here is an Uphoff Advisory Framework you can download and use in your business. Put every number your leadership team reviews each week on it, and see how many sit right of center. View the full Uphoff Advisory Framework library at uphoffadvisory.com/frameworks (https://www.uphoffadvisory.com/frameworks/). Engagement metrics sit a notch above: harder to fake outright, but easy to optimize for the metric instead of the buyer. Usage depth and renewal behavior sit higher still, because pretending costs the customer something real. Contract value, expansion revenue, and churn sit closest to money itself. The businesses already living near that end of the spectrum are the ones actually positioned to build a world model worth trusting. Everyone else is redesigning an org chart around a signal that isn’t honest yet. Everyone else is redesigning an org chart around a signal that isn’t honest yet. This isn’t just Silicon Valley talking It would be easy to dismiss this as some crazy idea from the tech elite. Don’t. The data says otherwise, and it’s not subtle. The number of managers and executives at public companies fell 6.1% between May 2022 and May 2025 (https://www.hr-brew.com/stories/2025/09/05/middle-managers-are-getting-exorcised-from-org-charts), according to Live Data Technologies. Gartner predicted in October 2024 (https://www.gartner.com/en/newsroom/press-releases/2024-10-22-gartner-unveils-top-predictions-for-it-organizations-and-users-in-2025-and-beyond) that through 2026, one in five organizations would use AI to flatten their structure, eliminating more than half of current middle management positions. We’re in the last quarter of that window. I’ve been the skeptic on AI-attributed layoffs. In June I called most of it a misattribution problem (https://tonyuphoff.substack.com/p/the-ai-layoff-myth): pandemic correction and PE margin engineering, relabeled as AI. That’s still true of last year’s numbers. But manager headcount specifically, not total headcount, is the number that’s moved. Nobody announced that. They just stopped backfilling the role. This isn’t theoretical, and it isn’t just Block. GitLab and Coinbase both cut management layers this year and pointed to AI when they did. Cloudflare’s cuts reached middle management too. Google said last year it had eliminated 35% of its managers overseeing small teams (https://www.nbcnews.com/business/business-news/google-eliminated-35-percent-managers-overseeing-small-teams-exec-says-rcna227486). Intuit, Meta, Cisco, Atlassian, Salesforce, and Oracle all paired 2026 layoffs with AI investment or efficiency claims (https://sea.peoplemattersglobal.com/article/strategic-hr/microsoft-to-oracle-major-companies-that-laid-off-employees-in-the-first-half-of-2026-50732). Block didn’t start this. It’s just the company doing it with the strongest claim to a signal that actually justifies it. Revenue per employee is how the market keeps score Block’s reorg came with a scoreboard, and it isn’t the org chart. Run Block’s own numbers: * 2025: $10.36 billion of gross profit (https://www.sec.gov/Archives/edgar/data/1512673/000162828026012254/xyz-20251231.htm) with 10,205 people. About $1 million per person. * The plan: $12.2 billion for 2026 (https://s29.q4cdn.com/628966176/files/doc_financials/2025/q4/Q4-2025-Shareholder-Letter_Block.pdf) with just under 6,000 people. The stock jumped more than 20% after hours (https://www.aol.com/finance/block-stock-explodes-jack-dorsey-171300607.html). * Now: guidance raised twice, most recently in August to $12.51 billion (https://siliconangle.com/2026/08/05/block-shares-slip-despite-second-quarter-beat-raised-2026-guidance/), after second-quarter gross profit grew 25%. About $2.1 million per person, double where it started the year. The market wasn’t paying for the layoff. It was paying for the output per head. Then: a vanity stat. For most of my career, revenue per employee was a number nobody built a valuation around. A 2013 analysis of public software companies (https://tomtunguz.com/revenue-per-employee/) found it had almost no relationship to market value. Hiring waves, sales ramp times and company age moved it more than strategy did. Now: the cleanest read on scale. AI changed what the number measures. It shows whether technology or headcount is doing the work, and that’s the question every buyer and investor is asking. Here’s where the bar sits: * Marketing agency average: about $163,000 per employee (https://www.hausadvisors.com/blog/marketing-agency-industry-statistics) * Median public software company: about $395,000 (https://www.thesaascfo.com/arr-per-employee-benchmarks/) * Median AI-native software company past $100 million of ARR: $655,000 (https://saastr.com/whats-truly-great-now-in-b2b-ai-per-iconiq-115-growth-at-100m-55-gross-margins-and-655k-in-revenue-per-employee) (ICONIQ, September 2026) * Top quartile of that group: $890,000 Why it’s hard to fake. Anyone can say AI made their team more productive. Revenue per employee shows whether the productivity reached the P&L. That’s why I added it to the Uphoff Value Index this quarter as a driver in all eight categories we benchmark (https://tonyuphoff.substack.com/p/ai-stopped-being-a-story-now-its). A services firm approaching the software standard earns up to a full turn of EBITDA. Two agencies with identical P&Ls, one running with 68 people and one with 40, now land nearly $1 million apart. One caution from the operator’s chair. Revenue per employee can be gamed too. Push work to contractors or offshore partners and count only full-time staff, and the number looks better than the business is. Expect buyers to start asking for revenue per dollar of total labor cost, not just per head. Measured honestly, it’s the closest thing most B2B companies have to a money-grade signal about themselves. Marketing and sales break first If you’re running marketing or sales, none of this is abstract. These are the two functions most exposed, and the exposure isn’t only about headcount. It’s about who you hire, how you coach them, and what “good” looks like once the AI is closer to the honest signal than your team is. Marketing: the funnel was never honest. Marketing has been running on performative signal for years, and the funnel proves it. Forrester has found that fewer than 1% of leads (https://www.forrester.com/blogs/saying-goodbye-to-mqls-6-mql-myths-squashed-to-shift-your-leads-based-culture) make it from the top of the funnel to a closed deal. The industry knows the model is broken, and vendors are already pitching a replacement. Docket calls it the Agent Qualified Lead (https://www.docket.io/blog/what-is-an-agent-qualified-lead): a lead qualified by an AI agent in a live buyer conversation, capturing real intent instead of inferring it from a click or a form fill. Hiring for that looks nothing like hiring for lead-gen volume. Coaching it looks nothing like reviewing MQL counts in a Monday pipeline meeting. The SDRs who survive aren’t dialing more. They’re coaching AI performance. Sales: the job is splitting in two. It’s happening right underneath your comp plan. Regie.ai’s analysis of LinkedIn data (https://www.regie.ai/new-rules-of-outbound/sdr-vs-full-cycle-ae-headcount-shift) found full-cycle AE seats up 10.8% while SDR headcount fell 22.7%. Two-thirds of teams cut SDRs. Fewer than one in five added them. The SDRs who stayed are running AI on the accounts that matter, not working the phones. That’s a different hire, managed a different way, measured against a different definition of quota progress. The playbooks most of us built our marketing and sales organizations on assumed a human was doing the qualifying, the outreach, and the judgment calls. Agentic AI is already doing large parts of all three. Keep running the old playbook and you’ll be coaching people to compete with a system that’s already faster and can’t be gamed. The real work comes before the reorg That’s the mistake I expect to watch a lot of companies make over the next two years: skipping straight to flattening the org before doing the harder, less glamorous work of finding or building a signal that can’t be gamed. Speed is the advantage Dorsey and Botha are chasing, and they’re right that it compounds. It compounds in the wrong direction just as fast when the thing moving fast is built on noise, not signal. Before anyone touches their org chart, they should answer one question honestly: what’s the one number in this business that nobody can fake. If there’s a real answer, the reorg is worth having, and revenue per employee will tell you within a year whether it worked. If there isn’t, that’s the actual project, and it has nothing to do with headcount. Keep going: AI Stopped Being a Story. Now It’s in Your Multiple (https://tonyuphoff.substack.com/p/ai-stopped-being-a-story-now-its) and B2B Marketing Is Driving Forward, Looking Backward (https://tonyuphoff.substack.com/p/b2b-marketing-is-driving-forward) The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. “Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC (https://uphoffadvisory.com/), a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com (https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

3. 

   14 Oct 2026

   Your AI Pilot Doesn’t Have a Job

   Somewhere in your organization right now, a team is piloting technology nobody can explain the job of. They can describe the vendor. They can describe the demo that got everyone nodding. Ask them what specific job that technology was hired to do, and watch the pause. That pause is the whole problem. Most AI pilots that fail were never given a job. Nobody wrote down the job description the technology was hired to do, who does that work today, and what would prove it got done better. Writing it down costs nothing. Skipping it costs the pilot. Procurement gave this mistake a name. It doesn’t stay in procurement On a recent episode of the Art of Procurement podcast (https://artofprocurement.com/blog/podcast-the-cpo-agenda-scaling-ai-beyond-the-pilot), Darshan Deshmukh, President of ProcureAbility, gave this pattern its name: “death by a thousand pilots.” His diagnosis was just as plain. Organizations keep leading with the technology instead of the outcome. Philip Ideson, who co-founded Art of Procurement with Kelly Barner and built it into the source procurement leaders actually rely on, put the consequence best in his write-up of the episode: “A compelling platform or use case can be a source of inspiration, but it cannot define the destination.” (Disclosure: Art of Procurement is an Uphoff Advisory client.) None of this is new. Procurement made this mistake with ERP, then with e-sourcing, and every other function made it with CRM. Same mistake, different software. The ERP era supplied a half-billion-euro example. In 2018, Lidl walked away from a seven-year SAP project after spending roughly €500 million on it, according to Handelsblatt (https://www.retaildetail.eu/en/news/food/lidls-failed-it-project-cost-half-billion). The sticking point was not complex. Lidl valued its inventory at purchase price. The software was built around retail price. In my read, nobody settled up front which way the business actually needed that job done, and hundreds of consultants spent years trying to bend one to fit the other. I’ve never run a procurement organization. I have spent three decades running media, marketing, and technology businesses, and every one of them had its own version of this: the CRM migration that died in month four, the martech stack nobody fully turned on, and now the AI copilot every vendor in your inbox is pitching your team. The martech version is measurable. Gartner found (https://www.gartner.com/en/newsroom/press-releases/2023-08-23-gartner-survey-finds-63-percent-of-marketing-leaders-plan-to-invest-in-generative-ai-in-the-next-24-months) marketers used 58% of their martech stack’s capabilities in 2020, 42% in 2022, and 33% in 2023. The spend kept going up. The use kept going down. The failure point is identical everywhere it shows up. The technology got approved. The job never got defined. Why do most AI projects fail? Rarely because of the technology The failure rate for this generation of pilots is well documented. S&P Global Market Intelligence found that 42% of companies (https://www.ciodive.com/news/AI-project-fail-data-SPGlobal/742590/) abandoned most of their AI initiatives in 2025, up from 17% the year before, and the average company scrapped 46% of its proofs of concept before they reached production. The more useful finding is why. When BCG surveyed 1,000 senior executives in 2024, it attributed roughly 70% of the challenges in getting value from AI to people and process, 20% to technology, and only 10% to the algorithms themselves. The part every demo is built to sell is the smallest slice of the problem. Now look at the budget for your last pilot. If most of it went to the vendor, you funded the 30% and left the 70% to chance. McKinsey’s August 2026 State of AI survey points the same direction from the other end. The companies getting real earnings impact from AI are a small group, about 6% of respondents. Nearly three-quarters of them say they have fundamentally redesigned workflows around it. Among everyone else, the figure is about a quarter. The winners started with the work. Everyone else started with the tool. The business case already exists. It was built backwards Almost every one of those pilots cleared some kind of approval process. Somebody wrote a business case. There were ROI projections, a vendor comparison matrix, maybe a proof of concept demo that went well. This is not usually an absence-of-diligence problem. It’s a diligence-aimed-at-the-wrong-target problem. The business case documented what the technology could do. It rarely documented, in plain words a non-technical person in the business could repeat back accurately, the job the organization actually needed done. Capability and job are different questions. Most business cases answer the first and assume the second. Pilots multiply for another reason nobody puts in the business case. A pilot is the one way to say yes to the vendor and no to accountability in the same meeting. Nobody’s name is on it. Nobody set a date to decide. It costs less than the argument about killing it would. Death by a thousand pilots is death by a thousand decisions nobody made. A job is not a use case I’ve made a version of this argument before, about customers rather than internal technology decisions. When I was brought in to transform ThomasNet.com, my team thought we understood the dozen or two things our customers were trying to accomplish on the platform. The real number, once we did the research, was over 350. We had been building for what we assumed the job was, not what it actually was. I wrote about what that discovery did to the company, and to my own thinking, in the post You Don’t Know What Your Customers Are Actually Trying to Do (https://tonyuphoff.substack.com/p/you-dont-know-what-your-customers). The same blind spot shows up on the buy side, and it may be more dangerous there, because nobody outside your own walls is going to do that research for you. A vendor’s use case library describes what other companies did with the tool. It does not describe the job inside your organization, under your specific circumstances, that this technology is being asked to do. If you cannot state that job without naming the technology, you haven’t defined the job. You have named a category. A better demo makes the mistake easier to make An ERP demo in 2004 was a slide deck and a promise. An AI demo in 2026 is the product working on your own data, in your own language, in front of the people who will approve it. The gap between the demo and the job has never looked smaller. It has rarely been larger. A demo shows one job done well, under conditions the vendor chose. Your organization runs hundreds of jobs, under conditions nobody chose. That was the lesson of the 350 at ThomasNet, and no demo is built to show it to you. A good demo makes defining the job feel unnecessary. That is what it was built to do. So I have a rule, and vendors do not love it: no demo until the job is written down. Two companies wrote two different sentences Klarna is the clearest recent example of what happens when the job gets written wrong. In early 2024 the company said its AI assistant was doing the work of 700 customer service agents and handling two-thirds of its customer chats. By May 2025, CEO Sebastian Siemiatkowski told Bloomberg the company was investing in human support again. His explanation was unusually candid: “As cost unfortunately seems to have been a too predominant evaluation factor when organizing this, what you end up having is lower quality.” The job got written as “cut the cost of support.” The job customers needed done was “get my problem resolved.” The pilot did the first job and failed the second. Morgan Stanley did the opposite. Before rolling out its GPT-4 assistant to wealth management, the firm started with a narrow job: help a financial advisor find the right answer, fast, in a library of roughly 100,000 research documents. Every use case was tested against real advisor tasks, with advisors grading the output, before it shipped. According to OpenAI’s case study, more than 98% of advisor teams now use it. Same class of technology as the pilots in the S&P numbers. The difference was a job defined before the purchase order was sent. I wrote the jobs down before I hired a developer When I set out to build the decision tools I use in my advisory work, the easy path was obvious: pick a fast AI development platform, sketch a rough concept, and let a good demo carry the rest. I did the opposite. Before I hired a developer, I wrote out the job to be done for each tool. The valuation tool’s job: when a CEO is weighing a major decision, show what that decision does to the value of the business, and on what timeline. Not “calculate what the company is worth.” That distinction decided what got built. A valuation calculator answers a question once. A decision tool gets checked before every big call, so it needed drivers an operator can actually move, benchmarked against real trading multiples. The talent tools’ job: when a manager has to hire, coach, or restructure a sales or marketing team, help them make that call on evidence, starting with telling a strong resume from a strong hire. Writing that down ruled out the obvious product, an employee-tracking system. The job is the decision, not the monitoring. The technology, the specific platform, the build sequence, all came after those jobs were written down, not before. That ordering is the entire difference between a tool people keep opening and one that stops getting used after the second demo. Agentic AI removed the friction that used to slow this mistake down The old version of this mistake had natural speed bumps built in: long procurement cycles, an IT approval queue, a budget committee that met quarterly. A bad idea had time to die on its own before real money moved. Agentic AI tools mostly skip all of that. A capable pilot can now show up as a subscription one manager puts on a corporate card, no committee required. Zylo’s 2026 SaaS Management Index found expensed software spend up 267% in a year, with ChatGPT now the single most expensed application, and business units, not IT, controlling 81% of software spend. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, and its analysts describe most current projects as experiments “mostly driven by hype.” The old failure mode took a fiscal year to play out. This one takes a sprint. A twenty-year-old mistake now compounds at the speed of a subscription click, and most operators have not updated their discipline to match. The instinct is to add process: a committee, a review, a form. That loses. Governance that costs more than the subscription it governs gets routed around, by the same manager, with the same card. Whatever discipline you add has to be cheaper and faster than the pilot it gates. That is why the test below is three questions and not a forty-page deck. Three gates, cleared before the PO gets signed Here is the test I now run on every technology pitch that reaches my desk, whether I am advising a client or approving something inside my own business. I call it the Application Test: three gates, cleared in order, before a dollar moves. * State the job. In plain words a non-technical person in the business could repeat back accurately, with no vendor name and no product name in it. * Name the owner and the baseline. Who is doing that job today, and how well, in a number you already have: time, cost, error rate, or volume. A committee is not an owner. * Set the 90-day signal. The one measure you will check in ninety days that tells you the job is closer to done, separate from whether the pilot is still running. Logins, seats, and prompts measure the tool, not the job. Decide in advance what you will do with the answer: scale it, fix it, or stop it. Fail any gate, and you do not have a business case. You have a good demo and an open checkbook. Klarna’s pilot would have cleared gate one on cost and failed gate three the first time someone checked whether customers were getting their problems solved. The Application Test decides whether a pilot earns its first dollar. Whether it earns the second is a different discipline, the one I laid out in Your Agentic AI Isn’t Failing. Your Math Is. (https://tonyuphoff.substack.com/p/your-agentic-ai-isnt-failing-your): never fund the next stage without naming what it has to pay back. Here is an Uphoff Advisory Framework you can download and use in your business. The full worksheet adds the checkboxes, the red flags, and the scoring. Print it, hand it to the team bringing you the next pitch, and ask them to fill it in before the demo, not after. View the full Uphoff Advisory Framework library at uphoffadvisory.com/frameworks (https://www.uphoffadvisory.com/frameworks/). BCG put the algorithms at a tenth of the problem and people and process at 70%. In my experience, most of that 70% starts with a job nobody wrote down. Here is the fastest way to find out whether that is true in your business. Pull up the last three pilots you approved. Write down the job for each one now, after the fact, with no vendor name in it. If you can do it in under a minute, the pilot probably has a future. If you cannot, you already know how it ends. You just haven’t admitted it yet. The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself. “Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, (https://uphoffadvisory.com/) a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com (https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1)

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