Uphoff on Media Podcast

Tony Uphoff

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

  1. 1d ago

    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, I argued the shortlist gets built 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’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. 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

  2. 3d ago

    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, 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%. 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. 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, according to Live Data Technologies. Gartner predicted in October 2024 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: 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. Intuit, Meta, Cisco, Atlassian, Salesforce, and Oracle all paired 2026 layoffs with AI investment or efficiency claims. 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 with 10,205 people. About $1 million per person. * The plan: $12.2 billion for 2026 with just under 6,000 people. The stock jumped more than 20% after hours. * Now: guidance raised twice, most recently in August to $12.51 billion, 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 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 * Median public software company: about $395,000 * Median AI-native software company past $100 million of ARR: $655,000 (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. 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 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: 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 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 befor

  3. Oct 14

    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, 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. 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 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 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. 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 t

  4. Oct 12

    Skydance’s $110 Billion Bet on Netflix’s Order of Operations

    Paramount Skydance closed its acquisition of Warner Bros. Discovery on October 6. The combined company is now simply called Skydance: roughly $70 billion in revenue, more than $30 billion a year in content spend, and about $79 billion in debt. Most of the coverage will treat this as a Hollywood story. Studio lots, franchise slates, who runs HBO. That misses the point. This is a technology bet with a studio attached, and David Ellison has made that his whole argument: “Unless you can build a tech product truly competitive with Silicon Valley, you can’t compete.” You might be asking how the merger of Warner Bros. and Paramount is a B2B story. It's the clearest case study I've seen this year of a legacy business adapting to technology disruption. The real test isn't the deal. It's whether the company can change how it operates, and the order it has always done things in. Ellison is right about the problem. Whether he can fix it comes down to a sequence the traditional studios have gotten backwards for most of the streaming era. Netflix won on the how, then the who Netflix didn’t take over filmed entertainment because it had better content. It didn’t produce award-winning content until 2013, when House of Cards won its first Emmy. Netflix was 16 years old. It won because it started with the how: consumer choice, ease of access, and a user experience built by technologists, not bolted on by a distribution department. Only after it owned the how did Netflix go after the who: world-class creators on screen and behind the camera. By then it had something no studio could offer talent. It knew exactly who was watching, what they finished, and what they’d watch next. The how made Netflix the most attractive place for the who to work. That order matters. Netflix still doesn’t own a cloud. It rents Amazon’s, and still spent $3.4 billion on technology and development last year, because the advantage was never the servers. It was the product organization that decided what got built. The studios bought the business model but kept their old operating model The traditional studios watched this happen and responded by launching streaming services. Subscription pricing, original programming, a global app. They copied the business model. They never changed how they worked. The operating model stayed talent first and talent last, with user experience treated as somebody else’s job. The apps launched feeling like 2009 enterprise software draped over a content library. Warner Bros. Discovery renamed HBO Max to “Max” in 2023, erasing one of the most valuable brand names in media, then put it back two years later. That isn’t a technology company making a product decision. That’s a studio treating the one of the most valuable consumer brands as an afterthought. Every prior owner of Warner Bros. made a version of the same mistake. I traced that history in Bundling Didn’t Die. It Moved.: AOL, AT&T and Discovery each arrived with a distribution or cost thesis, and none of them changed how the place actually worked. Skydance is the first studio organized how-first Look at what Ellison built before the deal closed, and almost none of it is about movies. Paramount+, Pluto TV and BET+ are moving to a common Oracle backend. Skydance Animation already runs Oracle’s “Studio in the Cloud” production model, and Ellison intends to scale it across every production workflow. Safra Catz, Oracle’s former CEO, joined the board. Dane Glasgow, a former Meta and Google product executive, now runs product, digital platforms and AI. Larry Ellison personally guaranteed roughly $45.7 billion of the equity. The closing announcement reads the same way. More than $6 billion in run-rate synergies over three years, with technology the first source named. HBO Max and Paramount+ unified “into a single service over time.” And the org chart. Ynon Kreiz, the former Mattel CEO, became co-CEO on October 5 and owns day-to-day operations and integration. Ellison kept strategy, creative, talent, technology and capital allocation. Other than Netflix, no studio has made technology the centerpiece of its strategy the way Skydance is. Cloud + AI + IP is the how. It still needs the who. Ellison’s formula is Cloud + AI + IP. The first two terms are the how. The third is what the who produces. And the plus signs hide the real math: this works as multiplication, where a zero anywhere zeroes everything. Cloud is the easiest term to buy. Moving to Oracle can lower the cost to serve. On its own it builds nothing a competitor can’t rent next quarter. One catch: Oracle makes money when Skydance consumes more cloud, so watch whether that relationship gets priced and measured like any other vendor’s. AI is only as good as the data under it. Skydance today has two flagship streamers on two technology stacks, a free ad-supported service, a stack of cable networks and two newsrooms. That’s a lot of customer data and no single view of the customer. Netflix’s how was built on one identity, one product, one recommendation layer. Until Skydance has that, AI gets applied to fragments. The who is the term studios think they already have. They don’t, not anymore. HBO’s value was built on creators believing they’d be protected, and the Zaslav years eroded a lot of that trust. That’s why the leadership moves on the eve of closing matter. Casey Bloys, the steward of the most trusted creative brand in the building, is set to run all of streaming. And Kreiz is the executive who turned Mattel from a company that sold plastic into one that monetizes characters. The Barbie movie grossed more than $1.4 billion worldwide. Mattel's direct take was about $125 million, and Barbie toy sales rose 27% in the fourth quarter but just 3% for the year. The ticket sales were never the point. The movie proved Mattel's characters could carry a franchise, and that's the asset Kreiz knows how to build. Here’s the tension Ellison has to manage. The how-first instinct, applied by a company carrying north of 6x EBITDA in debt, points straight at cost. Fitch cut Paramount to junk in March, and the stated target is 3.0x leverage by the end of 2029. Every quarter between now and then will reward using technology to cut production cost. Netflix used technology to make the experience better, which is what made it the place creators wanted to be. Same tools. Opposite outcomes. The line between a technology company and a tech-enabled one is disappearing Netflix disrupted Hollywood as a technology company. It built its own product, its own recommendation engine and its own engineering culture, and it got valued like a tech company. Content was what the platform needed. Skydance is something else: a tech-enabled studio. It owns the IP, rents the infrastructure from Oracle, and hired product leadership instead of building an engineering organization from scratch. A few years ago, that structure all but guaranteed second-place software. Agentic AI changes the math. When agents write the code, run personalization and handle the workflow, capabilities that once took a large engineering organization can be rented and managed by a much smaller team. That’s the real experiment inside this deal. Skydance doesn’t need to become Netflix. It needs Netflix’s results from a different structure: IP it owns, technology it rents, and agents doing work that used to take an engineering department. Boards and investors are asking every B2B information CEO a version of the same question: do we need to become a software company? In the agentic era, the answer is increasingly no. You need to operate how-first. Who builds the stack matters less every year. Who decides what it’s for matters more. The same sequence error runs through B2B I’ve covered Oracle as an analyst for Cloud Wars, and I’ve spent most of my career running businesses that owned valuable content and rented the technology. B2B made the studios’ mistake first. B2B information companies digitized the business model years ago. Print subscriptions became digital subscriptions, then SaaS pricing, then data licenses. Most of them never changed how they worked. Editorial first, sales first, product experience somebody else’s problem. Plenty of B2B companies still have world-class experts and analysts sitting behind a product their customers tolerate rather than use. When we rebuilt ThomasNet from a 120-year-old industrial directory into a data platform, the infrastructure decision was the easiest one we made, and in the end, the step that opened the door to growth and value. We moved legacy, on-premise systems to the cloud and went all in on AWS, the same cloud Netflix rents. The hard part was changing the focus of the business: from obsessing about a declining base of advertisers to building the product and user experience around how buyers actually searched and sourced. Then we put a century of supplier content to work inside that experience. Agentic AI is about to make this sequence error expensive. When the buyer is an agent, the how is the whole front door. If the machine doing the buying can't find, read and use your content, it never gets considered. The who doesn't matter if the how can't get it through the door. Here is an Uphoff Advisory Framework you can download and use as a diagnostic tool in your business. Plot where you are on the how and the who today, and which one you’re actually investing in. View the full Uphoff Advisory Framework library at uphoffadvisory.com/frameworks. Four signals will show which sequence Skydance is running 1. Does the Oracle migration show up as a better product or only a cheaper one? Cost to serve is half the story. Watch the app. 2. When does one subscriber identity span HBO Max and Paramount+? “Over time” isn’t a date. 3. Do the first AI announcements cut production cost, or create new value from existing characters and catalog? 4. Does HBO’s talent stay? The creat

  5. Oct 7

    AI Stopped Being a Story. Now It's in Your Multiple

    The Uphoff Value Index tracks what buyers pay for B2B information, marketing and technology businesses, and why it changes. Each quarter, this report shows what moved and what it means if you're building, running or funding one. What the UVI is, and who it’s for What it is. The Uphoff Value Index is a valuation benchmark for B2B information, marketing and technology businesses. It starts where a buyer starts, with an EBITDA multiple range for each of eight categories, then estimates where a company sits in that range based on three factors: * Growth and margin, read together, so strength in one can offset the other * Revenue per employee, measured against the category’s own peers * Protection against AI disruption, new this quarter The eight categories: B2B Media · Media & Events · Business Intelligence · Data · Marketing Services · MarTech and AdTech · Demand Generation and Content Syndication · B2B Events What it’s built on. Three sources: disclosed M&A transactions; the trading multiples of a fixed set of public companies in each category; and research from the advisers who run these sale processes. This quarter that includes Collingwood’s 2026-27 Market Report, a survey of about 100 active acquirers across information, media and events, and the source of the deal counts and buyer findings below. The ranges are checked every quarter and fully reset at least once a year. A range moves only when the evidence moves it by half a turn of EBITDA or more, so a company’s number changes when the market does, not when a stock has a bad week. The UVI Review Panel, a group of operators, investors, investment bankers and industry analysts who work across these categories, reviews every update before it ships. How you use it. The Uphoff Advisory client tool pairs that estimate with a diagnostic of the five Value Drivers that build what a business is worth over time: Revenue Quality · Market Position · Operational Leverage · Leadership Depth · Exit Readiness. This quarter’s ranges, the new AI protection score and revenue per employee for every category go live in the client tool on uphoffadvisory.com later this month. Who it’s for. Operators running these businesses, investors funding them, and founders building them. As I argued in How Much Is My Business Worth Is the Wrong Question, the exit multiple is a report card on decisions made years earlier. I’ve been CEO of five businesses, each one a turnaround or a transformation. Every time, the value the next owner paid for was built or spent long before anyone talked about a sale. Most leadership teams learn what their business is worth when a buyer tells them. I built the UVI to show them while there’s still time to change it, and to line up today’s decisions with the value they want to create and the exit they’re planning for. Because every decision has a multiple. Q3 2026: six of eight categories repriced The dividing line is AI exposure at the revenue line. Data, B2B Media and MarTech fell because AI is already visible in their revenue. Business Intelligence held its ceiling because buyers keep paying for the data AI needs. The deal counts agree. Information deals rose 88% in the first half of 2026, from 51 to 96, and events deals rose 31%. Media deals fell 46%, from 54 to 29. Size now shows up in price. The year’s headline deals carry a scale premium most businesses in these categories won’t see, so two ranges moved down to price the lower middle market. AI defensibility has become the gate. Buyers test it before they look at growth or margin. On average, 60% of the sale processes buyers entered never closed, and more of them now end quietly before launch. The UVI Index, Q3 2026 Where AI reached the revenue line, the multiple followed Data. In July the debate about contact and intent data was whether the market had priced AI disintermediation too early. ZoomInfo's second quarter settled it: revenue up 1.2%, a full-year outlook that still points to a decline of about 3%, a $650 million impairment, and the stock down about two-thirds in a year. Buyers have stopped discounting a future threat and started discounting revenue that no longer grows. ZoomInfo’s 35% adjusted operating margin doesn’t earn a premium on a flat top line. I looked at what that means for go-to-market data businesses in Monday’s post, B2B Marketing Is Driving Forward, Looking Backward. B2B Media. The category's best public reference point used to be TechTarget, with margins north of 30% before the Informa combination. Informa TechTarget now runs 10% to 13% on revenue down 3%, and it has written off nearly $1 billion of goodwill since the start of 2025, including $45 million in the first quarter of this year. Buyers value digital media businesses with $5 to $10 million of EBITDA at 6.5 to 9.5 times EBITDA. Advertising-led media lost its best argument for a higher multiple. The value hasn't left the category. It's moving to the businesses that turn an audience into data a buyer can use. Media companies with real data revenue already price that way, and they belong in Business Intelligence. MarTech and AdTech. The ceiling moved because the company holding it up moved. The Trade Desk set the top of this range for years and now trades around six times EBITDA, with the stock down three-quarters in a year. Zeta Global, growing 44%, trades around 19 times forward EBITDA, but a single company growing at that rate is an outlier. Criteo’s falling revenue marks the floor. In this category growth is the multiple, and very few businesses have it. Intelligence held its ceiling. In events, scale is what gets paid. Business Intelligence. Two strategic buyers paid up for data that AI agents need: Publicis for LiveRamp at about 12.3 times forward adjusted EBITDA, and Nielsen for DoubleVerify at about $2.15 billion. DoubleVerify’s growth had slowed to under 3%, so the low end of the range widened. Proprietary data still commands a premium. Slow growth still costs you, even when the data is good. Events. Emerald, CloserStill and Hyve priced at an estimated 11 to 14.5 times EBITDA, and the same handful of private equity firms is now buying events businesses from each other. That's what scarcity looks like at the top of a market. Most events businesses aren't at the top. Buyers value organizers with $5 to $10 million of EBITDA at 9 to 12.5 times for tradeshows and 6 to 9.5 times for conferences, and the largest buyers are already refusing to look at anything under $2 or $3 million of EBITDA unless it fits closely with what they own. The demand for events is real. An agent can't replicate a room full of decision-makers. The UVI range moved down, from 10 to 16 times to 8 to 14, to price the business most operators actually run. In services, the spread inside the range is the story Demand Generation and Content Syndication. No new disclosed transaction this quarter. Public comps and private-market buyer conversations point the same way: demand generation and sponsorship businesses owned by media companies trade at 6.5 to 10x. Two things hold that pricing down: the hangover from the pandemic boom, when event budgets moved to leads and then moved back, and a flood of low-cost lead providers that has taught buyers to discount lead quality across the category. The top of the 7 to 12x range comes from the exception, and the exception is real. Intent-led businesses with recurring revenue that can show which accounts are in market and prove the return are starting to pull away from media pricing. My read: the market is two to three years from treating that revenue as subscription-like, and pricing it that way. (Disclosure: I'm a board advisor to Pipeline360, which competes in this category.) Marketing Services. The range held at 8 to 14x. What changed is how buyers place a firm inside it. The holding companies set the ceiling for generalists: Omnicom trades at about 7.8x EBITDA, which limits what the biggest buyers can pay for an agency that looks like what they already own. The premium goes to firms that don’t look like that. The average agency does about $163,000 of revenue per employee. The median public software company does about $395,000. Agentic workflows are closing that gap one firm at a time, and buyers pay for the firms that close it. Take two agencies with identical P&Ls: $11 million of revenue, $2 million of EBITDA, 15% growth. One runs with 68 people, the other with 40. Last quarter the index valued both at 9.8x. Now the 40-person firm is worth 10.3x, about $1 million more on the same earnings. Services is where the gap is widest, and where it will reprice firms fastest. Revenue per employee now moves the multiple Revenue per employee is the cleanest single read on whether technology or headcount is doing the work. Starting this quarter, the UVI measures it in all eight categories, each against its own public peers, because the bar isn't the same everywhere. Listed B2B media companies run about $260,000 to $370,000 per employee, research and advisory firms about $280,000 to $320,000, and MarTech and AdTech platforms about $390,000 to $780,000. A firm at its category's baseline gets no adjustment. A firm approaching the top of its peer range earns up to a full turn of EBITDA, and one well below takes a discount. A B2B media business doing $333,000 per employee picks up more than half a turn, about $1.7 million on $3 million of EBITDA. Growth and margin set the multiple. AI defensibility decides whether you get one. For years, growth plus margin explained most of the difference in what these businesses were worth. It explains less now. Companies with similar growth and similar margins are trading far apart, and the gap comes down to how defensible they are against AI. In Collingwood’s survey of about 100 buyers, 91% called the AI resilience of the business model essential or important. 86% said the same of the end market, and 74% of a direct relationship with the cu

  6. Oct 5

    B2B Marketing is Driving Forward, Looking Backward

    Ninety-four percent of B2B buyers now use AI somewhere in the buying process. Forrester and 6sense surveyed buyers separately and landed on the exact same number. Eighty-nine percent of B2B marketers use AI to create content Same technology. Opposite jobs. Buyers use it to decide. It does the research, narrows the field, and builds the shortlist, and 95% of purchases still come from that initial shortlist. Marketers mostly use it to produce: more content, more sequences, more campaigns, aimed at a buyer whose AI is now filtering most of it out. That’s an industry driving forward while looking in the rearview mirror. And the market has started pricing the difference. The questions were about leadership. Now they’re about value. In January I published 3 Questions B2B Marketing Still Hasn’t Answered: * Ownership. Who actually owns demand creation and is accountable for revenue? * Demand quality. What does quality mean when buyers are anonymous and AI-driven? * Outcomes. Can the industry move from fragmented marketing tools and systems to trusted, outcome-based systems? It’s still a leadership problem. Nine months later it’s a valuation problem as well. ZoomInfo took a $650.5 million goodwill impairment in its second quarter, on revenue growth of 1.2%, and guided to a full-year revenue decline. In September, Clay, which sells signal and workflow to the same go-to-market teams, raised money at $7.1 billion, more than double the valuation it carried thirteen months earlier, after quadrupling revenue in 2025. Same buyers. Same category. Opposite valuations. It shows up inside single companies too. Salesforce stock was down 26% by early February, as software shed roughly $1 trillion of market value. By late August, its Agentforce and Data 360 recurring revenue had reached nearly $3.9 billion, up more than 210% in a year. One P&L, moving in two directions at once. Agentic AI doesn't lift or sink a sector. It splits product lines on two variables: what you get paid for, and whose AI you're building for. Most of the industry is getting the second one wrong Question #1: The buyer's AI now owns the front door of demand creation. The January question asked whether marketing, sales, or ops owns demand creation. The answer came from a direction none of them were watching. The buyer owns it, and the buyer's machine does more of the work every quarter. Here is the counter-intuitive part. Buyers are reaching out to sellers earlier than they used to: about 61% of the way through their journey, down from 69% a year before, according to 6sense. They still start that conversation themselves roughly 80% of the time. And 95% of purchases still come from the shortlist they built before the first call. Earlier contact hasn’t reopened the field. Buyers are calling to check a decision they’ve mostly made. Gartner expects AI agents to intermediate more than $15 trillion of B2B spending by 2028. Even if Gartner is off by a year, the direction isn't in question. So the ownership question has changed shape. The issue now is who owns what the buyer's AI knows, believes, and repeats about you before the buyer ever raises a hand. In most marketing organizations, nobody does. A Scribewise and Scrunch survey of 602 marketers this spring found 91% claim a documented AI search strategy, while 62% are still measured on traditional SEO metrics. The job moved. The scoreboard didn’t. Question #2: Quality is whatever the buyer’s AI trusts. In January I wrote that the industry still sells leads while buyers behave like a buying group. The buying group just picked up a member nobody counts: the agent doing the research. Forrester's 2026 research puts the typical decision at 13 internal stakeholders and nine external influencers. When the product itself has generative AI features, the group doubles, from seven people to 14. A lead is one person. An agent doesn't fill out a form. For twenty years, the seller defined quality: a scoring model, an MQL threshold, a handoff rule. The buyer's AI defines it now, and it isn't grading on machine logic alone. It rewards what's verified across independent sources, specific enough to answer a real question, and solid enough that someone in the buying group will stand behind it. It discounts volume. Volume is where most seller-side AI went. A 10Fold survey of 400 B2B tech marketers found 46% producing three to five times more content than the year before, on budgets up 10% or less. The Content Marketing Institute found 87% of marketers using AI for content report productivity gains, and 39% report better content performance. Output multiplied. Results didn’t. Compare what happens when quality measurement follows the buyer. Palo Alto Networks dropped MQLs for buying groups and, per a Forrester case study, saw a 17% higher win rate, with multi-stakeholder opportunities eight times more likely to advance. The value math follows. Anything sold by a unit the buyer’s AI makes redundant (contact records, gated-content leads, undifferentiated intent, outbound volume) is being repriced down. Verified signal that a buyer’s agent needs and can’t scrape is being repriced up. The market is paying for signal and marking down records. Question #3: Trust moved from the tool to the result. The third question asked whether the industry could move from fragmented tools to outcome-based systems. The software market answered for it this year. As I argued in The Stack That Broke Marketing, the fix for sprawl was never more technology. It was a different application of it. Buyers have stopped trusting claims and started testing them. More than 60% now run a trial before they buy, according to Forrester, and only about a third of those end up buying from the vendor whose trial they ran. The trial has become the place where shortlists flip. And buyers trust the machine about as much as they trust you. Gartner found 51% of buyers worry about misleading information from generative AI, and 49% worry about misleading information from sales reps. So they check one against the other: 69% want a sales rep to validate what the AI told them. Trust sits with whoever is accountable for the result and can prove it. Features, attribution models, and dashboards still don’t answer the CEO’s question from January. Evidence does. Both org charts are being redrawn. Only one side is doing it on purpose. The buyer’s side added people to check the machine. Buying groups double when AI is part of the purchase. Procurement is a decision-maker in 53% of buying cycles, per Forrester, and is increasingly involved from the earliest stages. 6sense found 58% of buyers engaged sellers earlier specifically to evaluate the vendor’s AI. Buyers built a verification layer, and it is getting larger. The seller’s side added machines and subtracted titles. Spencer Stuart found that 31% of S&P 500 companies no longer have an enterprise CMO, and the ones who remain average 4.1 years in the job. Postings for GTM engineers, the people who wire data, AI, and workflow into the revenue engine, grew 205% year over year to roughly 3,000 open roles in early 2026, according to one job-market analysis, and 91% of the companies that have hired one employ exactly one. Gartner expects AI agents to outnumber sellers ten to one by 2028, while fewer than 40% of sellers will say the agents made them more productive. The money follows the same pattern. Gartner’s 2026 CMO Spend Survey puts marketing budgets at 7.8% of revenue, with 15.3% of that going to AI. Only 30% of those CMOs say their organizations are ready to use it, and 56% say they lack the budget for their own AI strategy. Both sides are running into the same wall. Gartner’s procurement research calls it a productivity paradox: AI makes individuals faster, and the gain shrinks at the team level. The buyer’s side is answering with more judgment. The seller’s side is answering with more output. One side is building a windshield. The other is polishing the mirror. Five predictions Here is what I expect to happen from here, and what I’m watching for. Each one cuts against the consensus that AI makes B2B simpler, cheaper, and less human. * AI is making B2B buying more human, not less. Groups double when AI is involved. Two-thirds of buyers say they prefer a rep-free experience, and more than two-thirds want a rep to check what the AI told them. Both are true at once. The seller’s job is moving from persuasion to verification, and the best-paid sellers of 2028 will be the ones buyers call to check the machine. * Ten agents per seller will make outbound worth less. When every seller has ten agents writing to buyers whose own agents read the mail, unsolicited volume gets filtered at machine speed. The cost of a message falls toward zero. The cost of a real meeting with a real buying group goes up. Anyone pricing on outbound volume is pricing the wrong side of that trade. * More content will make you harder to find. Teams producing three to five times more content are not seeing three to five times the results. Buyers’ AI rewards sources that are specific, verified, and consistent. I expect the strongest marketing teams to publish less, prove more, and show up on more shortlists because of it. * Procurement, not marketing, will set the new unit of value. Procurement already holds a decision vote in more than half of buying cycles, and it will be the first function to hand comparison work to agents. Activity pricing (per lead, per impression, per seat) is the easiest thing for an agent to line up in a spreadsheet and squeeze. Outcome pricing is the hardest. That asymmetry will reprice whole categories faster than any analyst model expects. * The most important marketing hire of 2027 won’t carry a marketing title. The CMO role is already splitting. One half is becoming the owner of what machines know and say about the company. The other half is becoming the owner of the outcome number. The leader

  7. Sep 30

    “Thanks for the Lecture on Price”

    The largest contract Computer Intelligence had ever sold came down to one meeting. I was leading sales and marketing for Ziff-Davis’s research businesses, and on the other side of the table was the president of a large enterprise software company. We gave a long presentation, slide after slide breaking down the pricing. When we finished, he looked at us and said, “Thanks for the lecture on price. Perhaps we can now have a discussion about value.” We had presented the price as if it were the value. Software companies and services firms still make that mistake every day, and the market has stopped letting them get away with it. Three in four B2B software companies changed their pricing or packaging in the past year. Most of them changed the model. Almost none of them changed the process that sets the price. The numbers come from Kyle Poyar’s 2026 State of B2B SaaS and AI Monetization Report, a survey of more than 230 software companies this spring. Hybrid pricing jumped from 25 percent of companies to 37 percent in a single year. Per-seat pricing hangs on mostly at the top of the market: 29 percent of companies above $150 million in ARR still use it. And when the survey asked which pricing model investors would prefer, hybrid was the outright favorite at 35 percent, ahead of outcome-based (26 percent) and usage-based (24 percent). Flat-fee and seat-based barely registered, at 10 percent and 5 percent. The clearest sign the shift isn’t finished: the single loudest monetization complaint in the survey, especially among flat-fee and seat-based companies, is not enough expansion revenue. That’s exactly the symptom you’d expect when what it costs to deliver the product changed and the price never fully caught up. Even at the earliest stage, when a company has no pricing legacy to unwind, more than a third of companies under $5 million in ARR are still choosing flat fees, a model with no built-in mechanism for expansion revenue at all. The gap says more about leadership than about software None of this is really a software story. I've been brought in as CEO five times, most often to turn around a business in decline or to lead a significant transformation, and pricing was one of the first things I examined. Not because it's a marketing question or a finance question. Pricing is the fastest read there is on whether a leadership team understands its unit economics, and whether it has a real process for setting price to value and managing it over time. A real pricing process has three parts: * Market analysis: what competitors, substitutes, and the buyer’s other options cost, including the option of doing nothing. * Financial analysis: what it costs to win and deliver a unit, and what margin that leaves. * Positioning: what the outcome is worth to the buyer, and how the offer is framed so they can see it. This is where price turns into value. Most companies run one of the three and call it a process. Cost-plus shops do the financial analysis and stop. Rate-card followers do the market analysis and stop. Positioning, the part that connects price to value, is the one that gets skipped. It’s exactly what that president was asking for. Three questions tell you whether the process exists at all: * What was the price set against? Cost, a competitor’s rate card, or what the outcome is actually worth to the buyer. * What process produced it? All three analyses, run on purpose, with someone accountable for the result, or whichever department had the loudest voice in the room the year it was decided. * How is it monitored? A number someone checks every month against current delivery data, or a number everyone quotes from memory that nobody has verified since the day it was picked. A leadership team that can’t answer all three in one plain sentence each is running a guess it stopped checking a long time ago. The bigger mistake is usually what you’re pricing, not the price When I came in as CEO, the pattern I found most often had little to do with the rate. It was the menu. Products were priced à la carte, line by line, when the customer was trying to buy an integrated solution that worked like a recipe. We were selling ingredients to people who came for dinner. At Thomasnet.com, moving from à la carte to solution pricing lifted our average sale by 40 percent, and the integrated programs performed better for customers than the same products bought one at a time. At Pipeline360, we built a Branded Demand solution that combined programmatic display and data-driven demand generation under one price. Campaign performance improved by more than 35 percent on average, and average order size and customer retention both went up with it. Neither move was a discount or a price increase. Both decisions weighed unit economics and customer value together: what each sale earned us, and what the customer was actually buying. That's the question the software survey never asks. It tracks how companies charge. It doesn't track whether they're charging for the thing the buyer came for. À la carte pricing almost always lands in the bottom half of the map below. Every line item gets priced against its own cost, and nobody prices the value of the combination. Download the Price-to-Value Map and plot your own business on it. Be honest about the vertical axis: it’s the line between a lecture on price and a discussion about value. Most teams that think they price to outcome are pricing to last year’s rate card. Most companies are hiding in one of two quadrants Flying Blind is the default position for any company that inherited its pricing instead of designing it: no process behind the number, no one checking it, a rate everyone repeats because no one has ever been asked to defend it. Cost-Plus Discipline looks like the safer place to be. There is a process, and someone reviews the number on a schedule. But if that process is only the financial analysis, running the price against cost instead of what the buyer actually gets, the discipline is rigorously protecting the wrong number. That quadrant is more crowded than it looks. When the same survey asked what drove pricing on new AI capabilities, the most common answer, at 54 percent, was internal costs and margins. Even the newest thing these companies sell got priced against what it costs to deliver, not what it’s worth. Value Left on the Table shows up wherever good instinct meets no process: someone in the room already senses the outcome is worth more than the invoice says, but there’s no positioning work or pricing policy to charge for it consistently, so it varies deal to deal and disappears into discounting. Priced to Value is the only quadrant where the answer to all three diagnostic questions is a current, verified number instead of a guess repeated until it sounded like a fact. The map covers the first two questions. The third decides whether you stay where you land. A price nobody rechecks drifts back toward Flying Blind, whatever quadrant it started in. Each quadrant has one move out of it: * Flying Blind: put a process in place, starting with the financial analysis, so you know what a unit costs to win and deliver before anyone debates what it should earn. * Cost-Plus Discipline: keep the financial rigor and add the other two parts: the market analysis and the positioning work that turns price into value. * Value Left on the Table: turn the instinct into a process. Do the positioning work, write the pricing policy, and stop letting each deal renegotiate it. * Priced to Value: put the number on a monthly review against current delivery data. That’s what keeps it there. B2B marketing has less excuse than software does for missing this Agencies and consultancies carry a version of this cost that almost never gets allocated to the deal that required it: discovery calls, a strategy deck built before the contract exists, a campaign concept pitched on spec to win the account. That’s a real cost of winning the business, and most agencies book it as overhead instead of against the account it won. So gross margin can be right in total and wrong for every individual account, and leadership ends up pricing off a number that was never true at the unit level. And every one of those hours just got cheaper to produce, with agentic AI drafting the first pass of the research, the deck, or the campaign brief. A marketing services business that hasn't repriced against that changed cost is making the same mistake software companies are only now getting measured on, just with a campaign plan instead of a product roadmap. I built Uphoff Advisory on the bet I laid out in Solo Scale: The New Business Model AI Just Made Possible, that a services business can run priced and structured like a software company. The test above isn’t one I’m suggesting to readers from the sidelines. It’s the one I run against my own business. The model matters less than the number underneath it Every business should be checking two numbers every month, side by side. What a unit of whatever it sells earns once every cost of winning and delivering it is counted, not just the ones that show up on an invoice. And what that same unit is worth to the customer who buys it. Per seat, per hour, per project, per outcome, the unit changes. The process that keeps those two numbers in the same conversation doesn’t: market, financials, and positioning, run on a schedule, with someone accountable for the result. The investors funding your category have already worked out which pricing models reward value. Your buyers are next. Sooner or later, someone across the table will thank you for the lecture on price and ask to talk about value. Have that discussion before they ask, or a competitor will have it with them first. 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 deb

  8. Sep 28

    Goals Are for Losers. Systems Are for Winners

    That line has been founder gospel for a decade. Scott Adams coined it, and James Clear’s Atomic Habits turned the idea into a movement. It’s also half right. A goal without a system is hope with a deadline. You know where you’re headed, and nothing on the ground tells you when you’re drifting. A system without a goal fails more quietly: consistent activity that feels like progress and runs straight past the point where it stopped mattering. The two aren’t rivals. They’re a flywheel. The goal sets the direction. The system checks your position against it. The correction closes the gap. And every correction teaches you something that makes the next one faster. Remove any piece and the wheel stops. Most of the operating rhythms I’ve inherited as a CEO were missing the correction. OKRs got relabeled “the system.” So did the weekly cadence and the dashboard. None of it changed. It just got a better name. A real system and a goal with a calendar invite look identical in the board deck. They only diverge when the number comes in wrong. The test is what happens when the number comes in wrong One question separates them: when reality diverges from the plan, does someone with the authority to act change course before the next scheduled review, or does everyone wait for the meeting? If the answer is “we flagged it and we’ll revisit at the QBR,” you don’t have a system. You have a goal with a calendar invite. The rhythm is decorative: the feeling of discipline without the function of it. Does your operating rhythm generate decisions, or reports? Here is an Uphoff on Media framework you can download and use as a diagnostic tool in your business. I learned this at InformationWeek before I had words for it I was recruited from Ziff-Davis to CMP Media to take over InformationWeek. It was a $15 million brand, losing money, dead last in a field of seven IT titles on both ad pages and readership. The goal came first, and we made it impossible to miss: TTL, Take The Lead, on the sign-off of every email, voicemail and meeting. The system came next: four pillars (People, Product, Process, Performance) applied to every function, from sales to editorial to audience development. Not a scoreboard. A diagnostic each team used to find what was holding it back and change what it did next. Three years later InformationWeek was a $175 million business running at 50% margins and #1 in readership. The goal never got smaller to make the system easier to run. The system existed to serve the goal. The losers had goals too Two companies had everything except the correction. Friendster * The goal: grow faster than anyone. It had the social network idea years before Facebook. * The missing system: engineers wanted to fix buckling infrastructure. Leadership kept shipping features, because “grow faster” won every argument. * The cost: pages took forty seconds to load, and MySpace took the market. WeWork * The goal: become the biggest workspace company on earth. * The missing system: nothing could force a correction when the unit economics broke, and governance kept one person’s desk out of reach. * The cost: by the 2019 IPO filing, $47 billion in lease obligations sat against $4 billion in committed revenue. The pattern: both had a goal you could say out loud. Neither built the systems that would force them to notice, and act, when reality stopped agreeing with the plan. Agents will run the system. Someone still has to design it. Agentic AI collapses two costs that used to hide a weak operating rhythm: the cost of execution, and the time it takes a signal to reach a decision. When agents do the work continuously, the quarterly check-in becomes an hourly one. The flywheel spins at machine speed whether or not you built a correction into it. Hand an agent a goal without a system and it will hit the goal. Give a demand-generation agent an MQL target and it will deliver the MQLs. It will also fill your sales team’s calendar with people who were never going to buy, and do it faster than any human team could. That’s Friendster’s mistake, automated. The systems version gives the same agent a target and a loop: watch MQL-to-opportunity conversion by source, throttle the sources that decay, escalate to a human when conversion drops below a set threshold. The goal stays human. The correction runs around the clock. It’s the loop I described in Loop Engineering applied to the operating model itself. The market is already sorting this out. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Read that list again. It describes goals without systems. When execution gets cheap, the scarce skill is designing the loop: what the agent measures, what it can correct on its own, what threshold pulls in a human, and what pulls the plug. That’s systems-level thinking, and it’s a different muscle than setting ambitious targets. The goal-setters who never built a correction mechanism are about to get exposed at machine speed. The operators who did are about to compound faster than anyone in their market. Hire and promote the loop designers You find them in the interview. Ask: walk me through a time a plan you were running turned out to be wrong. Goal-driven candidates describe how hard they pushed to hit the number anyway. Systems thinkers describe what they changed, and how fast. You keep them by what you reward. Praise someone for hitting a number while ignoring three warning signs, and your best people learn to bury the signal. Reward the one who flagged the problem early and changed course, even when the quarter came in softer. And you give loop design real weight. Whoever designs correction into your agentic workflows should report to whoever owns the outcome, not sit buried in IT. Whether you’re building it, running it or funding it Building it: if your last three “system reviews” ended with “let’s keep an eye on it,” you have goals with better branding. Running it: give the correction mechanism teeth before you hand the workflow to an agent. Automating a rhythm nobody acts on just multiplies the theater. Funding it: an operating cadence tells you a founder shows up. Ask what changed the last time the number came in wrong. Call them goals, systems, OKRs or TTL. None of it works unless something notices when the number comes in wrong and pushes back, whether that’s a person in a QBR or an agent at two in the morning. The goal gives the flywheel direction. The correction gives it speed. Build only one and you don’t have a flywheel. You have a story you tell your board about one. 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, 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

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About

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

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  • Creator
    Tony Uphoff
  • Years Active
    2026
  • Episodes
    65
  • Rating
    Clean
  • Copyright
    © Tony Uphoff
  • Show Website
    Uphoff on Media Podcast

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