Podcast
Humans of Martech
Hosted by Phil Gamache · EN
Future-proofing the humans behind the tech. Follow Phil Gamache and Darrell Alfonso on their mission to help future-proof the humans behind the tech and have successful careers in the constantly expanding universe of martech.
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232: How taste can be codified into systems and is no longer a durable skill, and what's next with Sharon Gai
4d ago01:06:21Tap to summarizeWhat's up everyone, today we have the pleasure of sitting down with Sharon Gai, author, keynote speaker, and educator.We'll cover:(00:00) - Intro (01:21) - In This Episode (05:20) - How AI Moves Marketing Creativity Up the Abstraction Layer (09:08) - Why Taste Is No Longer a Durable Human Skill (15:33) - What Human Work Looks Like After AI Learns Taste (20:41) - How to Codify Taste Into Your AI Systems (27:20) - Why Hands-On Execution Still Builds Judgment AI Can't Copy (36:48) - How to Build a Personal AI Strategy and Learn How to Learn (39:31) - Why Seeing Your Job as Tasks Rather Than a Role Future-Proofs Your Career (48:12) - Why You Should Tinker With AI Before You Cut Back on Meetings (54:13) - How Students Can Adapt to Graduating Into an AI Economy (01:02:13) - How to Decide What Deserves Your Energy Summary: Sharon Gai wrote a book telling everyone that taste and judgment were the durable human skills AI couldn't touch, then went on record to take half of it back. In this episode she walks through why taste is now codifiable, why originality still belongs to humans, and how she pulled a McKinsey-grade deck out of Claude by feeding it her own best work. We get into the lost generation of junior executors, the beekeeper mindset that separates orchestrators from busy bees, and a token-maxing hot pot dinner that made her swear off wasting agents. She even hands over a deathbed test for deciding what deserves your energy. If you've ever wondered which of your skills actually survive the next few years, this one is a map.About Sharon GaiSharon Gai is an author, keynote speaker, and educator who helps professionals future-proof their careers in an AI-driven economy. She teaches on Maven and speaks to audiences around the world about how AI is reshaping creativity, work, and the roles people play inside their companies. Her latest book uses the image of busy bees and beekeepers to argue that the future belongs to the people who orchestrate AI rather than execute every task themselves.Before writing and speaking full time, she spent years in enterprise tech, starting out helping IT directors and CIOs build data centers at the dawn of the cloud era. That hands-on background shows up throughout her work, especially in how she thinks about the difference between doing the work and orchestrating it.How AI Moves Marketing Creativity Up the Abstraction LayerWhen cameras got cheap, painters thought they were finished. Why spend weeks rendering a face in oil when a machine could capture it in a fraction of a second? Plenty of people called photography mechanical, soulless, artless. The threat was real, and the outcome was stranger than anyone expected. Freed from copying the world exactly, painters walked through a door cameras couldn't follow, into Impressionism, Cubism, and everything that made modern art feel alien at first. Sharon sees the same door swinging open for marketers right now, and the word she keeps returning to is abstraction.Walk through any art museum and you can watch this happen on the walls. The 1800s rooms are full of precision: portraits rendered to the eyelash, battle scenes with every horse in place, oceans and landscapes you could almost step into. Then you reach the modern wing, and some people stop and say "I could have done that." The work got harder to read. You have to stand there a moment, wonder who the artist was and why they picked this particular black over a lesser one. The craft survived by climbing a level, from rendering reality to deciding what the piece should mean.Sharon's marketing version is concrete. A few years ago you drew up a Facebook ad yourself, designed the banner, and loaded it into the platform's back end by hand. Now that whole chain can run on its own. So what does the marketer actually do? The real decisions look different now. You choose whether to test one ad or several hundred, each personalized to the person about to see it, whether the creative should be a flat image or a video, and if it's video, what belongs on screen. The manual work shrinks and the number of real decisions explodes. That's the abstracted layer, and it's where the job is heading whether marketers are ready or not.Over the next 2 years, the marketers who struggle will be the ones who still measure their worth by the execution those tools just swallowed.Key takeaway: Write down every part of your last campaign that was pure execution: building the banner, resizing creative, loading it into the platform, pulling the report. Hand those tasks to AI on your next campaign and pour the reclaimed hours into the layer above them. Decide which audiences, how many variants, what format each one takes, and what the creative is actually trying to make someone feel.Why Taste Is No Longer a Durable Human SkillFor about a year, the safe career advice sounded identical everywhere. Let AI do the work, but hold onto taste and judgment, because those are the human skills a machine can't reach. Tech CEOs repeated it on every stage. Sharon believed it too, and wrote it into her book. A few months later she started taking half of it back.Her reasoning is uncomfortable if you've built a career on your eye. What is taste, really? For an expert artist it's roughly 10,000 hours of seeing the bad versions and the good versions until they can tell the difference on sight. That makes the taste expert something close to a mini LLM, a person pummeled with enough examples to develop a reliable read on quality. And if taste is just a very large training set, there's no obvious reason you can't hand those examples to a model.Sharon is careful about where she draws the new line. In AI time, she points out, a single day can feel like a year, so any strong opinion has a short shelf life. She'll grant that judgment might still belong to humans, but taste she no longer counts in that column. Her position in June of 2026 is that taste is codifiable, and that alone knocks it off the list of things only people can do.Where Originality Still Separates Humans From AIShe used to open talks with a slide that read: creativity does not equal originality. The words sound like twins, and they describe 2 different things. AI can be creative. It can paint in an impressionist style and produce something that would look at home in a modern gallery. Originality means something genuinely novel, something that hasn't appeared anywhere before, and everything a model makes was pre-trained on what already exists.The gap shows up most clearly in how humans move between domains. Narrow AI, the kind we mostly use today, is trained on one field. General intelligence takes a principle from physics class and applies it to biology, or borrows something from construction and uses it while cooking. People do this instinctively from childhood. Machines still struggle with the leap, which is why originality, for now, stays on the human side of the ledger even as taste crosses over.If taste is now a training asset, the marketers still selling "good taste" as their moat are pricing a skill the market is about to commoditize.Key takeaway: Stop treating your taste as something locked inside your head. Start capturing it instead. Save the emails, decks, and campaigns you consider excellent right next to the weak ones, label what makes each good or bad, and feed both to your AI tools so the model learns your specific standard rather than a generic one.What Human Work Looks Like After AI Learns TasteEvery few months someone publishes a confident map of what work looks like in 2030. Ask Sharon and she starts by lowering the temperature. Most of it is prediction, and prediction is ch...
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231: Why Lattice careers and communication skills are built for the AI era, with Michele Martin from Ticketmaster
2w ago00:56:04Tap to summarizeWhat's up everyone, today we have the pleasure of sitting down with Michele Martin, Senior Director of Global Brand and Integrated Marketing at Ticketmaster.Summary: Most career advice still hands you a ladder and tells you to climb. Michele makes the case for a lattice instead, the crisscross path of sideways and diagonal moves that built her range across banking, retail, a startup, and now Ticketmaster. Along the way she breaks down why the call center job she almost refused changed everything, how mentorship and sponsorship are 2 different engines, and why communication beats coding as the skill that survives the AI era. She even walks through what a genuinely good AI session looks like on her marketing team. If you've ever felt boxed in by the straight climb up, this one rewires how you think about your next move.About Michele MartinMichele Martin is the Senior Director of Global Brand and Integrated Marketing at Ticketmaster, where she leads brand strategy and ties together the channels that reach fans across sports, music, and the arts. Her career is a working example of the lattice she advocates: 11 years at TD Bank moving through corporate sponsorships, communications, a contact center, and sales strategy, then brand and customer roles at L.L.Bean and a stretch at a growth-stage startup.A first-generation college graduate, she builds communities everywhere she lands, from the Kind/Red marketing collective to Live Nation Women. She's also an avid traveler, an amateur golfer, and a self-described nonfiction reader who gets her fiction through Netflix and TikTok.What A Lattice Career Means And Why It Beats The LadderMost career advice still runs on a single image: the ladder. You start as a specialist, become a manager, then a senior manager, then a director, then a VP, climbing one predictable rung at a time. It's clean, it's legible on a resume, and it quietly convinces a lot of good marketers that any move which isn't straight up is a step backward. Michele has spent 2 decades proving the opposite, and she has a better picture in mind.Walk into any hardware store and find the fencing section. The lattice is that crisscross panel of wood, the one you'd nail under a porch for a little privacy. That shape is the whole philosophy. A lattice career moves diagonally, sideways, sometimes down for a stretch, and then back up from a completely different position than where you started.The freedom in that reframe is the part people miss. When you stop treating vertical promotion as the only valid direction, you give yourself permission to chase the work you're actually curious about. You might take a role that pays the same and reports to the same level because it teaches you something your current lane never will. A year later that detour is the reason you can do a job nobody on the straight-up track is qualified for. The lattice isn't a slower ladder. It's a wider map of where a career can go.The marketers who'll be hardest to replace over the next decade are the ones who collected range on purpose, because range is the thing no single promotion can hand you.Key takeaway: Stop scoring every potential move by whether the title goes up. Draw your own version of the lattice instead: list the 3 skills or domains you most want to understand, then look for roles that hand you those, even laterally. The diagonal move you resist now is usually the one that makes you uncopyable in 5 years.How To Decide If A Sideways Career Move Is Worth ItEvery marketer has a moment where someone dangles a role that feels wrong. Wrong department, wrong direction, wrong vibe for the brand you're trying to build for yourself. The instinct is to protect the climb and say no. Michele almost did exactly that, and the role she nearly turned down ended up shaping everything that came after.She spent 11 years at TD Bank, a place that treated career growth as a contact sport. The internal logic there was simple: if you want to grow, try a little bit of everything, because that's how you actually learn how the business makes money and how decisions land on customers and employees. So when a contact center role came up, it fit the bank's culture even though it terrified her. She'd never pictured herself there. She didn't know what the work even was beyond the frontline.What tipped her over was a mentor named Matt Chevalier, who passed away young and whose advice she still repeats. He'd watched her get comfortable, and comfort was the thing he refused to let slide. His argument wasn't that her current job was bad. It was that she'd convinced herself the fun she was having couldn't exist anywhere else, and that staying put meant never testing herself in the ways a harder room would. That landed.Inside the contact center she learned phone systems, quality programs, and the guts of customer technology she'd never have seen from a marketing desk. The next jump, onto a sales strategy team, was scarier still, because now she was building an incentive platform alongside genuinely technical people while feeling like she knew nothing. The relief came from a small realization that's stuck with her since. If you can explain a complicated system in plain words, you actually understand it.The roles people resist hardest tend to be the ones carrying the steepest learning curve, which is exactly why avoiding them keeps so many careers thin.Key takeaway: The next time a role feels like a sideways or scary move, separate the fear from the data. Ask what specific skill or exposure it would hand you that your current seat never will. If the honest answer is "a lot," treat the discomfort as a signal you've found a real growth edge, not a reason to pass.Why Integrated Marketing Is A General Contractor RoleMarketing invents job titles faster than anyone can define them, and "brand and integrated marketing" is one of those phrases that means something different at every company. At Ticketmaster, Michele uses a metaphor that finally makes the term concrete. Integrated marketing is the general contractor of a campaign.Think about what a general contractor actually does on a build. They don't pour every foundation or wire every outlet themselves, but they understand each trade well enough to know when something's off, and they're the one person responsible for making all of it connect into a house someone can live in. That's the role. You need real fluency across the components of a campaign, whether that's CRM, paid media, organic, or social, and then you need the rarer skill of wiring those parts together.Here's where the lattice pays off in a way no straight-line career could. Because Michele spent years jumping into unfamiliar functions and learning new technology on the fly, she walks into an integrated role already understanding most of the trades she's coordinating. That changes the quality of the work. Without that range, the job quietly degrades into project management, chasing status updates and herding stakeholders. With it, she comes to the table with a real point of view on every channel because she's actually done the work in them.The integrated marketer who can only schedule the trades gets treated like overhead, while the one who understands the trades gets treated like a leader, and the difference is almost always hands-on range.Integrated Marketing Versus Omnichannel MarketingPeople lump integrated marketing and omnichannel together constantly, and Michele doesn't fight that too hard. Where she sits, the 2 work almost the same way. The job starts with a business challenge for a given category, then asks how marketing can support it, then defines a single concept grounded in data and fan insight. Only after that does the work fan out to the channels.The discipline is in what stays constant ...
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230: Zero-click marketing broke the measurement layer, so what should ops teams do now, with Amanda Natividad
3w ago01:05:01Tap to summarizeWhat's up everyone, today we have the pleasure of sitting down with Amanda Natividad, Chief Evangelist at SparkToro and co-author of Zero Click Marketing.We'll cover:(00:00) - Intro (01:14) - In This Episode (06:37) - Why Attribution Breaks Down in a Zero Click World (16:09) - What the Alligator Graph Means for Ops Teams (22:46) - How to Run a Zero Click Launch Week You Can Actually Measure (32:35) - How Incrementality Testing Works at Enterprise Scale (38:09) - What Audience Listening Adds to the Marketing Ops Stack (42:50) - What Content Leaders Need From Marketing Ops (47:59) - How to Turn Audience Research Into an Operational System (52:00) - How AI Visibility Changes Zero Click Marketing (58:36) - Setting Boundaries to Protect Your Energy and Focus Summary: zero click marketing won the strategy war, and now nobody can prove it's working. Amanda has spent over a decade building marketing that lives where the audience already is, and in this episode she takes apart the measurement crisis that creates. We get into why your attribution dashboard is quietly lying to you, how a viral story about HubSpot losing 80% of its organic traffic actually hid record revenue, and what a 25 million dollar ad blackout taught Dropbox about the gap between credit and cause. She also shows how to turn audience research into an operational system and why winning AI visibility comes down to writing genuinely good stuff. Stick around for the launch-week playbook and the overslept-webinar story that completely reframes how she guards her time.About Amanda NatividadAmanda Natividad is the Chief Evangelist at SparkToro, the audience research startup, and the founder of Zero Click Marketing, a podcast and consultancy built around the framework she co-created with Rand Fishkin in 2022. She spent 4 and a half years as SparkToro's VP of Marketing, where she launched a newsletter that reaches more than 60,000 subscribers at a 35% open rate and built Office Hours, a webinar series that pulls as many as 1,200 registrants a show.She's keynoted at AdWorld, Content Marketing World, and MozCon, and guest lectured at Columbia, Cornell, and Stanford. A Le Cordon Bleu-trained chef and former journalist, she now teaches Content Marketing 201 on Maven.Why Attribution Breaks Down in a Zero Click WorldEvery marketing ops team runs on a dashboard that hands out credit. This lead came from paid search. That demo came from a LinkedIn ad. This signup traces back to the nurture email. The numbers look authoritative, and leadership treats them that way. The trouble is the data underneath has been eroding for years, and most teams still read the output like scripture.Amanda has spent more than a decade building marketing programs that don't depend on the click. Her take on measurement starts from an uncomfortable place. Attribution was never as precise as the industry sold it, and every year it gets less precise.4 separate forces have chipped away at what attribution can actually see, and they stack on top of each other.Third-party cookies barely function. Only about 30% of users accept them, and Safari rejects them by default., Ad blockers hide a huge share of traffic. Somewhere between 20 and 60% of people run one, and among tech-savvy B2B audiences that number climbs toward 60%., The multi-device journey is untrackable pre-login. People average 3.6 devices each, so stitching a single human across all of them is mostly guesswork., Privacy regulation makes persistent tracking impractical. GDPR, CCPA, and LGPD mean what's legal in the US often isn't legal anywhere else, a real burden for any team with a global audience.None of this means you rip attribution out of the stack. In a mature organization it's already there, already wired into the reports leadership reads, so ignoring it would be its own kind of malpractice. The shift Amanda argues for is one of posture. Go in knowing exactly where the model goes blind, then ask the more useful question of what you can measure next to fill the gaps. That's where incrementality, media mix modeling, geo-testing, and holdout tests start to earn their place. The teams that keep their budgets in a down market are the ones who stopped presenting attribution as ground truth and started presenting it as one flawed witness among several. A single confident number is easy to attack. A converging set of imperfect signals is much harder to argue with.Key takeaway: Audit where your attribution model goes blind before your next leadership review. Write down how much of your traffic Safari blocks, how many of your B2B visitors run ad blockers, and how many touchpoints happen before anyone logs in. Bring that context into the room so a drop in tracked conversions reads as a gap in measurement, not a failure in marketing.Why Dark Social Traffic Shows Up as DirectOpen Google Analytics on any given week and a fat slice of your traffic sits in a bucket labeled direct. The polite interpretation is that all those people typed your URL straight into the address bar. Almost none of them did. Most of that direct traffic is dark social, the shares that happen inside messaging apps and closed platforms where the referral information never makes it back to you.SparkToro put real numbers on it. About 2 years ago the team ran an experiment, sending more than 1,100 visits across 11 social networks and then checking what Google Analytics reported. For TikTok, Slack, Discord, WhatsApp, and Mastodon, every single visit landed in direct.Amanda has a theory about why, and it follows the money. The platforms can see the organic referral string. They keep it invisible, because the moment you pay to join their ad network, that traffic suddenly becomes visible and measurable. Organic reach stays in the dark so paid reach looks like the only reach worth buying. And it goes well past the obvious suspects. Facebook Messenger strips the referral about 75% of the time, Instagram DMs about 30%, and even LinkedIn hides it roughly 14% of the time. A share is a share whether it happens in a feed or a private message, but only some of them ever get counted.The practical lesson for an ops team is to stop treating the direct channel as a junk drawer. When word of mouth and private sharing drive a real share of pipeline, a measurement model that files all of it under direct is quietly erasing your best-performing channel. The brands that figure this out start asking where conversations about them actually happen, instead of waiting for a clean UTM that the platform was never going to hand over.Key takeaway: Run your own dark social test before you trust the direct bucket. Push a known batch of clicks through TikTok, Slack, and a few DM channels, then watch how your analytics file them. Use the gap to set expectations with leadership, and lean on post-purchase survey questions like "where did you first hear about us" to recover the attribution the platforms refuse to share.What the Alligator Graph Means for Ops TeamsThere's a chart making the rounds that content marketers have started calling the alligator graph. Impressions climb while clicks to your website fall, and the 2 lines drift apart until they look like an open set of jaws. For a content team this reads as proof the strategy is working, because more people are seeing the brand. For a marketing ops team staring at the same chart in Google Analytics, it reads as failure, because the dashboard they own is built to count clicks and the clicks are going down. The measurement layer is undercutting the exact thing the content layer is producing.Amanda's first move is to reframe the problem. The j...
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229: The Privacy lawsuits coming for every marketing team, with Cara Caruso and Dustin Taylor
4w ago01:06:32Tap to summarizeWhat's up everyone, today we have the pleasure of sitting down with Cara Caruso, CEO and co-founder of Sentinel Insights, and Dustin Taylor, counsel at Troutman Pepper Locke.We'll cover:(00:00) - Cara-audio (00:53) - In This Episode (04:40) - 1 — Why Your Website Is Running More Tracking Tools Than You Know (08:17) - 2 — How a Plaintiff's Firm Turns Your Website Into a Lawsuit (17:55) - 3 — The ECPA Wave and the Privacy Policy That Sues You (20:53) - 4 — What Consent Drift Actually Looks Like (25:16) - 5 — Who Actually Owns Privacy Compliance (30:49) - 6 — The Business Case for Privacy-First Marketing (35:52) - 7 — What Marketing Ops Can Do About Privacy This Week (42:35) - 8 — Why Email Marketing Is the Next Privacy Lawsuit (48:04) - 9 — AI, Consent, and Being Forced to Delete Your Data (51:46) - 10 — Why Small Companies Get Privacy Lawsuits Too (58:42) - 11 — How to Decide What Deserves Your Energy Summary: A privacy-software CEO and a litigation defense attorney walk into a podcast and proceed to scare every marketer in the room, in the most useful way possible. Cara Caruso has scanned over 10,000 websites and found nearly 90% of them non-compliant, while Dustin Taylor has defended more than 100 companies against the exact lawsuits that follow. Together they trace how a forgotten tag from three years ago becomes a seven-figure settlement, why your own privacy policy is the document most likely to sue you, and how one month of a broken cookie banner turns into 10,000 dollars per visitor. Then they flip the whole thing and make the case for why clean, consented data actually performs better. Stick around for the part where your email open rates might be illegal and the FTC makes companies delete four years of data.About Cara Caruso and Dustin TaylorCara Caruso is the CEO and co-founder of Sentinel Insights, where she leads a platform that monitors websites in real time for consent violations and privacy exposure. Before starting the company she spent more than 25 years in data and martech, building and scaling teams across technology and financial services in both B2B and B2C. She pairs strategic planning with hands-on execution, and she's also a certified yoga instructor who has been known to bring a workshop into the office.Dustin Taylor is counsel at Troutman Pepper Locke, where he defends companies at the intersection of privacy law and marketing technology. He's defended more than 100 companies in ad-tech privacy cases involving cookies, pixels, session replay, and website chat, secured dismissals at the pleading stage in federal court, and argued in California, New York, Florida, Texas, and New Jersey. He started out with an advertising degree before law school, which makes him fluent in the martech stack in a way most litigators never are. He also publishes monthly privacy litigation reports and tracks ECPA filings with AI.Why Your Website Is Running More Tracking Tools Than You KnowMost marketing teams believe they have a clean inventory of what runs on their website. There's a tag manager, a cookie banner, a vendor list in a spreadsheet somewhere, and a general sense that someone signed off on all of it. Then someone actually scans the site, and the number comes back two, three, sometimes four times higher than anyone expected.Cara has watched this play out thousands of times. Sentinel Insights has scanned over 10,000 websites in the past year, and the pattern barely changes from one company to the next. Nearly 90% of those sites are not compliant. Every new customer gets the same uncomfortable conversation on day one.The gap between what a team thinks is running and what's actually firing comes from two places. The first is history. Somebody three years ago added a tag for a campaign that ended, then left the company, and nobody ever took it down. The team you have today inherited a stack built by people they never met, and most of those decisions were never written down anywhere. Cara calls it the ghost of marketers past, and it's sitting on almost every site she scans.The second is piggybacking. You buy one tool, drop in one script, and that single tag quietly loads four more. Each of those can load more on top. An agency hard-codes a pixel straight into a landing page because they didn't have access to the tag manager, and now your customer data flows to companies nobody on your team could name. None of it shows up in the tidy spreadsheet. All of it shows up in a scan.This is the part most marketers underestimate. The real exposure comes from the tools nobody chose on purpose, the dozen scripts running quietly in the background, each one sending customer data somewhere you've never audited. They pile up while everyone assumes the banner has it covered. No marketing team actually knows what's on its website until a scan proves otherwise, and "we reviewed it last year" is closer to a guess than a control.Why Not Knowing Is Not a Legal DefenseThe instinct, once you find those orphaned tags, is to assume they don't count against you. You didn't install them. You didn't even know they were there. Dustin spends a lot of his time correcting that assumption. These privacy laws do have a knowledge component, but courts read knowledge very differently than a normal person would. As long as the person who installed the tag three years ago knew they were installing something, the legal requirement is met. The fact that today's marketing team forgot it existed changes nothing.There's a second trap hiding inside the same problem. What marketing knew, what IT knew, and what legal knew are rarely the same thing, and that fragmentation is its own risk. Each group assumes another group is watching the stack. The court doesn't care which department dropped the ball. It only cares that someone, at some point, hit install.Key takeaway: Run a full scan of your live website this month and compare the results against your documented vendor list. Flag every tag you can't immediately explain, especially anything loading third-party scripts you never installed directly. The tools you can't account for are the ones quietly building your legal exposure, and forgetting they exist won't protect you.How a Plaintiff's Firm Turns Your Website Into a LawsuitHere's the mental model most marketing teams carry around: we're compliant until someone complains. You picture a single annoyed customer who takes the time to file something against your little startup, and you quietly decide the odds are low. Who's really going to sue over one text message or one tracking cookie? That assumption is the most expensive thing on your website, because litigation in this space doesn't start with a complaint. It starts with a scan of your site that you never see.Plaintiffs' law firms run continuous automated audits of company websites, and they're looking for far more than a typo in your privacy policy. Dustin walked through exactly what their scanners check:What loads automatically the moment someone lands on the page, before any consent is given, Whether there's a banner at all, and what it actually does, What keeps firing after a visitor opts out, Whether anything is miscategorized, like a marketing cookie quietly labeled "essential" so it can't be turned offOnce a firm finds the exposure, they find a plaintiff. The demand letter arrives, and the dollar amounts climb fast. Cara breaks the pressure into three forces bearing down on marketing teams at once:Trophy-hunting plaintiff attorneys who come after you for small amounts individually, then scale it into a class action, Brand and reputational damage that lingers long after a settlement clears, State enforcement at the atto...
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228: The Dispatch tower (The Dungeon of martech architecture, part 4)
Jul 1401:01:14Tap to summarizeWhat’s up folks, welcome to our 4 part series of crawling through the dungeon of martech architecture. You’ve arrived at our final episode, part 4: The Dispatch Tower.We’ll tackle:(00:00) - Intro (00:59) - In This Episode (01:30) - Sponsor: Mammath Growth (02:33) - Sponsor: GrowthLoop (03:58) - FLOOR 4: THE DISPATCH TOWER (04:39) - What Happens When 30 Vendors Turn On AI at the Same Time (21:24) - Boss Battle: The Agent Avalanche (22:53) - Why You Need a Central Referee, With Agent Guardrails (26:48) - Sponsor: Knak (27:55) - Sponsor: MoEngage (28:52) - Privacy Compliance and Data Minimization (38:38) - The Dispatch Layer that Controls Routing Logic (44:00) - The Layer Above the Dispatch is the Interface (47:20) - What Production AI Agents Look Like on a Governed Data Foundation (50:27) - The Deterministic Layer: Why the Interface Can't Run on Guesses (53:09) - System Stewardship: The Role That Emerges When You Clear This Floor (59:33) - FINAL ACHIEVEMENT: The Dungeon Is Cleared ---------------------------------------------------------------------------OPENING---------------------------------------------------------------------------Welcome back to the Dungeon of Martech Architecture.You've arrived at the final floor. Parts 1 through 3 built the foundation, the meaning layer, and the causal memory. Part 4 is the floor most organizations have been living on the whole time, often without knowing it.Episode 1: CRM GravityWe conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.Episode 2: The Eye of ContextWe learned why AI fails without context engineering and built the shared meaning infrastructure that keeps agents from misinterpreting what they read.Episode 3: The Correlation MasqueradeWe escaped the correlation trap and built the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior.Episode 4: The Dispatch TowerToday, we tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it.Let's finish our descent.------------------------------------------------------------------------------FLOOR 4: THE DISPATCH TOWER---------------------------------------------------------------------------Congratulations martech crawler, you’ve arrived to the final floor of the dungeon. Let’s face it, most of your fellow crawlers will never make it this far. They’re still lost in political battles debating why the CRM shouldn't hold product activation data or they ended up falling prey to the correlation trap and I forever stuck in the boomerang room. But you’ve made it. You’re proudly wearing the 3 badges of honor on your jacket: data janitor, context king and causal brainiac. This floor is the hardest because it combines collaborative political battles but some serious technical obstacles as well. The layout of this floor is best described as a hot mess of agent spaghetti. Every vendor in your stack has an agent now. Your ESP has one. Your CDP has one. Your MAP has one. Your CRM has one. Your ad platform has one. Each one has separate logic, separate assumptions, separate permissions, and a roadmap slide that says “autonomous.”None of them knows what the others are doing.And nobody volunteered to referee this stuff.At the top of this floor, there is a door most organizations never noticed. Whoever controls that door controls what instructions get through, which systems can act, and which agent gets authority over the customer.What Happens When 30 Vendors Turn On AI at the Same TimeYour inbox is probably already full of emails from every platform in your stack, all saying some version of “Turn on AI today.”Each one promises autonomous intelligence. Each one wants permission to write, recommend, segment, suppress, score, enrich, personalize, or trigger something. Each one is operating from its own model of the customer. None of them can explain what happens when two agents try to update the same record, message the same person, or optimize against conflicting goals.You are now the referee. Congratulations. Rich Waldron, CEO of Tray.ai, describes how this feels on the ground:RICH WALDRON, Episode 162"If you're the marketing ops leader and your 30 vendors are telling you to go turn on the AI for their application, you sort of become like an AI referee. You have to figure out, well, do I turn it on for this one and not for this one? And is this one gonna overwrite what occurs here? You feel it already. Your inbox floods with vendors begging you to 'just turn on our AI capability' -- 30 different platforms all promising transformation. Suddenly you're an unwilling AI referee asking impossible questions: which AI systems should I activate first? What happens when one AI's decisions contradict another's? Are all these systems feeding sensitive data into the same models?""Agents backed by an iPaaS give you full governance and control of where execution happens. For teams drowning in vendor AI chaos, the third option -- iPaaS-backed agents that naturally integrate with everything -- provides centralized governance, unified control points, and consistent execution environments."Rich is describing the nightmare version of the final floor: every vendor ships an agent, and marketing ops gets stuck deciding which ones are safe, which ones are redundant, which ones are risky, and which ones quietly conflict with each other.The answer is not to let every team turn on whatever they want. It is also not to create a central AI police force that blocks everything until innovation dies in a committee.The middle path is an operating model: a small central group that sets standards, reviews risk, creates reusable patterns, and lets the business move quickly without turning the stack into a haunted appliance storeFunny enough, I spoke with someone who actually did volunteer for that AI referee job – her title is a bit fancier though. Here’s Lindsay Rothlisberger, Director of GTM Innovation at Zapier and also a member of their AI Center of Excellence. At Zapier, she sits at the spoke of a hub-and-spoke AI Center of Excellence, a small central team led by a Chief AI Officer, with Lindsay as the GTM representative responsible for translating governance into go-to-market practice. Her work covers deciding which AI tools get activated, setting the standards for what a shared skill has to pass before it enters the internal library, and keeping track of what people are building before it fragments into 100 unsanctioned experiments.LINDSAY ROTHLISBERGER, Zapier"We have a skill that reviews our skills for data and security compliance. It gives you a red, yellow, green. Red — here's what you definitely need to fix. And in most cases, Claude can just say, 'I can go fix that for you. Would you like me to do it?' And just do it. So it's a pretty simple process. But it is a standard review skill that runs through all of these different things."Every shared skill at Zapier has to have a named owner. If it's used across a team, someone is accountable for maintaining it. Skills covering go-to-market operations are owned by RevOps. New builders who want to ship a skill that runs through a cross-functional workflow pair with a RevOps partner to build it. The governance operates as a co-authorship model, designed to make s...
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227: The Correlation masquerade (The Dungeon of martech architecture, part 3)
Jul 701:04:14Tap to summarizeWhat’s up folks, welcome to our 4 part series of Crawling through the dungeon of martech architecture. You’ve arrived at Part 3: The Correlation Masquerade.We'll cover:(00:00) - Intro (01:16) - In This Episode (01:36) - Sponsor: Knak (02:44) - Sponsor: MoEngage (04:02) - FLOOR 3: THE CORRELATION MASQUERADE (05:08) - Why Agentic AI Optimizes for the Wrong Thing at Scale (11:30) - The Boomerang Effect on AI that Erodes Revenue (20:40) - Why Marketing Attribution Data Can’t Tell AI Agents What Actually Caused the Result (28:28) - Sponsor: Mammoth Growth (29:31) - Sponsor: GrowthLoop (33:56) - How Bad Signals Masquerade as Evidence (42:27) - BOSS BATTLE: The Correlation Boomerang Archer (43:27) - Reducing Exposure While the Foundation Is Built (49:07) - Building a Causal Memory Layer With a Context Graph (01:02:15) - Achievement unlocked: Causal Evidence Layer Established ---------------------------------------------------------------------------OPENING---------------------------------------------------------------------------Welcome back to the Dungeon of Martech Architecture.You've arrived at part 3. If you're just joining, go back to parts 1 and 2, where we demoted the CRM, built the warehouse, engineered the context layer, and built the shared meaning infrastructure that keeps agents from misinterpreting what they read.Episode 1: CRM GravityWe conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.Episode 2: The Eye of ContextWe learned why AI fails without context engineering, built the shared meaning infrastructure, and dug into why the industry built the wrong kind of meaning infrastructure in 2012.Episode 3: The Correlation MasqueradeToday, we escape the correlation trap and build the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior.Episode 4: The Dispatch TowerNext, we tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it.Let's continue our descent.---Okay so we’re making our way down to the third floor with blood sweat and tears. But we’re feeling good. Our data is clean-ish. You’ve built a context bundle that we’re proud of and we collaborated on it with multiple people and shared definitions. We’ve got a nice big fancy data warehouse as our source of truth.Our warehouse holds a complete record of what happened. We can query patterns, correlations, historical campaign data, audience behaviors, outcome signals: all of it is available. But the problem we’re about to find out is that none of what we’ve built so far can tell an agent whether the thing it's optimizing for was ever the right thing to optimize for. None of it explains why an intervention worked, or whether it worked for the reason the model assumes it did.Let’s step through.---------------------------------------------------------------------------FLOOR 3: THE CORRELATION MASQUERADE---------------------------------------------------------------------------The layout of the correlation masquerade is like a high speed train to nowhere. You’ve spent two whole floors meticulously cleaning the “atoms” of your historical customer data and building a sturdy warehouse so that you can let AI and agents loose on the data. Maybe you’re starting to play with ‘next best action sequences’, building propensity models predicting the likelihood that certain cohorts of users will churn, maybe running reinforcement learning loops on historical context and doubling down on your best campaigns. Everything looks like it's working... until the world rumbles and you realize you're still in a trap.The layout of this floor is a room where every single action comes back wrong. And it’s not technically AI’s fault, they’re just optimizing for a finish line that’s actually just the end of the first heat of 8 heats. It’s a trap.Why Agentic AI Optimizes for the Wrong Thing at ScaleJason Dobbs, the Head of Marketing Ops and GTM Engineering at Kumo describes it like this:JASON DOBBS, Kumo AI"A warehouse is a record of what happened. It's not a rulebook for what an agent should do next. If you let a generic agent optimize directly on historical correlations with unbound authority, you can absolutely scale the wrong behavior. A product that correlates with high LTV does not necessarily cause high LTV. Prediction is not policy. Once you cross into action, you still need guardrails, business rules, approvals, evidence that it's actually driving business outcomes."An AI system that treats prediction as their gold standard will happily optimize for proxies while the actual outcome you care about degrades.The Prediction TrapTobias Konitzer spent years studying this failure as a computational social scientist before bringing that lens to marketing. His argument is that predictive models describe what's already happening, and marketing is about changing what's going to happen. Those are different jobs, and the data warehouse doesn't distinguish between them by default.TOBIAS KONITZER, Episode 212"The nature of predictive models is they represent the status quo. Someone is going to churn, or someone is not going to churn, but that is the status quo. And there is really no point for marketing if everything is just status quo. There is no marketing role here."He describes a CRM head at a billion-dollar outdoor brand who found that high LTV customers had a strong correlation with viewing a specific product, a pair of jeans. The obvious response was to push those jeans into the welcome flow. The correlation ran in the wrong direction, though. Those customers already had high LTV before they saw the jeans. The jeans showed up alongside the relationship, long after it was established.Scaling that logic into the welcome flow pushed irrelevant products onto a broad cohort with nothing in common with the original high-LTV segment. The analysis was reproducible and data-supported; it just never verified whether the jeans caused the high LTV or merely accompanied it. An agent running the same logic would scale the error across every customer who matched the surface pattern, efficiently and invisibly, before anyone stopped to ask.Tobias calls this lazy thinking, and risky thinking: analysis that feels like rigor because it's data-supported and reproducible, but skips the question that would disqualify the conclusion.The Boomerang Effect on AI that Erodes RevenueWhen you let agentic AI loose on the data warehouse it has access to a TON of data. That's amazing. But raw data and then events and actions often leads to predictions that are based on correlation, and not causation.For example, let's say you task an agent with improving the number of free users that convert to paying users. The agent sees in the past that a discount campaign to a certain cohort of active free users on a certain day resulted in a high % of paying user conversions.The agent concludes: this campaign works. Scale it.But what it can't see: those active free users were already the most likely to convert, they were on the edge of paying with or without the discount. The campaign didn't cause the conversions. It just correlated with them. The agent found the easiest pattern in the data and called it a lever.Run that campaign at scale and a few things happen. You hand discounts to users who would have paid ...
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226: The Eye of context (The Dungeon of martech architecture, part 2)
Jun 3001:02:52Tap to summarizeWhat’s up folks, welcome to our 4 part series of Crawling through the dungeon of martech architecture. You’ve arrived at Part 2: The Eye of Context.We cover:(00:00) - Intro (00:56) - In This Episode (01:28) - Sponsor GrowthLoop (02:32) - Sponsor: GrowthBench (03:32) - Welcome Back (04:09) - FLOOR 2: THE EYE OF CONTEXT (06:15) - Why AI Produces Believable Nonsense (09:00) - BOSS BATTLE: The Hallucination Oracle (10:07) - Data Quality: When Agents Read Your Messy Data (22:33) - Context Engineering: What It Is and Why It's Not the Same as Prompt Engineering (24:28) - Sponsor: MoEngage (25:25) - Sponsor: Knak (26:30) - Context Eng vs Prompt Eng (38:58) - Why the Industry Built the Wrong Semantic Layer in 2012 (46:33) - How Context Rot and Fragmentation Break AI Agent Performance (49:59) - BOSS BATTLE: Rotten Context Mage (50:37) - How to Build a Shared Context Layer for AI Agents (58:17) - Testing Whether Your Context Layer Works (01:01:35) - NEW ACHIEVEMENT: The Meaning Layer Is Live ---------------------------------------------------------------------------OPENING---------------------------------------------------------------------------Welcome back to the Dungeon of Martech Architecture.You’ve arrived at part 2. If this is your starting point, check out part 1 where we cleared the first floor’s boss in 2 forms: The False Truth King in the CRM, and The Export Hydra that spread it everywhere. That said, if you already have a data warehouse, you might be able to start right here.Episode 1: CRM GravityWe conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.Episode 2: The Eye of ContextToday, we learn why AI fails without shared meaning, build the context engineering layer, and dig into why the industry built the wrong kind of meaning infrastructure in 2012.Episode 3: The Correlation MasqueradeNext, we escape the correlation trap and build the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior.Episode 4: The Dispatch TowerThen, we tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it.Let’s start our descent.---------------------------------------------------------------------------FLOOR 2: THE EYE OF CONTEXT — AI Hallucinations, Data Quality, and Context Engineering---------------------------------------------------------------------------The layout of the second floor down the dungeon of martech architecture actually looks pretty fancy. It’s cozy, it looks modern, the whole palace is lined with mirrors. But it’s a bit creepy because once you look a little closer at the reflections, you notice that some of the details are off. The boss on this floor is low key danger that sneaks up on way too many teams – not like the big flashy monsters from the past 2 floors.Let’s say you have a new AI system running on your marketing data. You’ve got it producing stuff like scores, recommendations, campaign ideas. Initially, it actually looks solid.There’s no obvious AI sentence structures in the summaries, they read well.The scores next to accounts seem to make sense: higher ones next to well known brands and lower ones are gmail accounts.The campaign ideas are actually pretty fresh, you can tell that it’s tailored for your ICP.Every output is delivered with impeccable confidence.So the next step is asking yourself… how would you know if it was wrong?There’s a lot of obvious hallucinations that you probably catch when you chat with GPT or Claude, like totally inventing stuff. I’m talking about the details. The kind of wrong that passes the first glance. We’ve all seen that viral post on r/analytics about a company that found out AI has been making up analytics data for 3 months. Whether this post is from a real story or not, some versions of this are happening inside companies today. But this is happening everywhere right now. Even at some of the top AI companies on the planet. I’ve talked to technical marketing leaders that have greenlit agentic tools at their startups before the data definitions were settled. One of them called it ‘believable nonsense’, and that term kinda stuck with me. It’s the most dangerous form of hallucination, it sneaks up on you.This floor is harder than the last because the traps on the previous level were visible, once you knew what to look for: CRM exports nobody trusted, audience logic duplicated across platforms, copies drifting from the original. You felt that, you saw that. This floor’s failures are designed to look like success, until you dig into the details and look under the hood.Let’s look at the origins of The Hallucination Oracle boss. Why AI Produces Believable NonsenseHumans are wired to trust smooth, confident talkers. It’s actually baked into our evolution and how our brains develop from infancy. Studies on babies and brain scans show this is an innate thing that kicks in early.A person who sounds certain usually knows something, right?LLMs and AI systems break this calibration, they produce fluency without necessarily producing correctness. At first glance, the output sounds right for structural reasons, not evidential ones. And once something sounds right, we engage with it differently. We forward it. We build on it. We present it to stakeholders who don't have the context to question it.This is similar to the The False Truth King boss from the first floor in episode 1, but this is at the intelligence layer: output that has been processed, synthesized, and returned with all the structural markers of a trustworthy answer, but the problem is the reasoning underneath it is hollow.That’s Jason Dobbs, Head of Marketing & GTM Engineering at Kumo. He greenlit agentic analytics and predictive workflows at his startup before the team had settled on shared data definitions. The system produced outputs that looked reasonable right up until someone started asking follow-up questions:JASON DOBBS, Kumo AI"What made it dangerous wasn't really like the obvious hallucination. What we were seeing looked polished enough to be operational. At first glance the scores looked precise, the summary sounded coherent, the recommendations felt data-backed. But the moment you ask the simple follow-up questions -- why did you choose this account? What data drove this decision? -- the logic started to thin out. The lesson wasn't that the data was bad or the warehouse was bad or the model was bad. What was wrong is we were trying to automate ambiguity. We were asking AI to solve for confusion that we hadn't yet ourselves solved for internally. And once you do that, you enter the danger zone because the failure is essentially believable nonsense."This is some scary stuff right? When this believable nonsense gets trusted long enough to make it into a campaign, a decision, a board slide. Obvious hallucinations are easy to catch. Confident, polished, data-backed nonsense gets through more often than we think.The good news is that we already have a weapon perfect to slay this boss, we shaped it in the last episode: the data warehouse. It houses data. Data is how we defeat believable nonsense… but we need to enhance it.---------------------------------------------------------------------------BOSS BATTLE: The Hallucination Oracle----------------------------------------...
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225: The Fall of CRM gravity (The Dungeon of martech architecture, part 1)
Jun 2301:02:28Tap to summarizeWhat’s up folks, welcome to our 4 part series of Crawling THROUGH THE DUNGEON OF MARTECH ARCHITECTUREYou’ve arrived at Part1 : The Fall of CRM Gravity (00:00) - Intro (00:57) - In This Episode (01:31) - Sponsor: MoEngage (02:28) - Sponsor: Knak (04:53) - FLOOR 1: Why the CRM Lost Its Authority (06:09) - Why Every Team Moved Into the CRM (And How It Lost Its Authority) (13:57) - Why Sharing CRM Data Always Breaks It (18:02) - Why CRM Gravity Outlasts the Technical Argument (24:04) - BOSS BATTLE: The False Truth King (25:44) - Sponsor: GrowthLoop (26:48) - Sponsor: GrowthBench (34:56) - Why Centralizing Data Only to Copy It Out Defeats the Purpose (39:31) - BOSS BATTLE: The Export Hydra (40:53) - How to Move to a Warehouse-Native Architecture (46:36) - How to Achieve Portable Audiences (56:52) - How CLI/MCP Servers Are Changing Marketing Stack Integration ---------------------------------------------------------------------------OPENING---------------------------------------------------------------------------Welcome to the descent into the Dungeon of Martech Architecture, a 4-part journey through the unhinged and constantly expanding world of marketing technology.As a massive sci-fi fan currently reading the Dungeon Crawler Carl books, I have used their level-by-level progression as the direct inspiration for this 'dungeon crawl' analogy, and while you don’t need to know the books to enjoy the journey, those who do will recognize some of the gaming lore and achievement-style rewards woven into our descent. This will be educational and helpful for anyone that works and builds martech, and hopefully it’s also a bit fun. Without a doubt though, it will be weird. Here is your quick guide to the floors ahead:Episode 1: CRM GravityYou’ll conquer the source of truth and discover that the data warehouse replaces the CRM with portable audiences.Episode 2: The Eye of ContextYou’ll learn why AI fails without shared meaning, why context engineering is the layer between data and agent authority, and why the industry built the wrong kind of meaning infrastructure in 2012.Episode 3: The Correlation MasqueradeYou’ll escape the correlation trap and build the causal memory layer that separates agents that optimize correctly from agents that confidently scale the wrong behavior.Episode 4: The Dispatch TowerYou’ll tackle the governance chaos of 30 vendors all claiming authority, and confront the interface decision that most organizations already made without realizing it.Let’s start our descent.---Be honest: when was the last time you pulled up a number in your CRM and actually trusted it? like… no second-guessing, no “that feels a bit off”… just total confidence?Maybe you didn’t really have time to double check the logic behind the number and you were too excited to share the positive results. So you forwarded it to a peer. Or maybe you’ve been in that meeting… 2 people arguing over a number, both pull it up in the same CRM, and somehow get 2 completely different answers… and no one can explain which one’s actually right.We’ve all been there, we’ve felt it. That dark, creeping dread. When “which number is right?” gets answered with “well… it depends who built the report,”. They know it. You know it. The CRM admin knows it. Everyone in the room knows it. You don’t have a source of truth… just a CRM that’s turned into a dumping ground of lost updates that have slowly compounded into competing versions of reality.Call it counterfeit truth or data mirage… I call it bad data. Data that has the appearance of authority without the actual authority behind it. It's everywhere in the modern marketing stack. And the CRM is often where it starts.That’s where our first boss is hiding. ---------------------------------------------------------------------------FLOOR 1: Why the CRM Lost Its Authority---------------------------------------------------------------------------If you’re in B2B or B2C the first floor looks a bit different but only because of terminology. In B2B the 2 cornerstone platforms are the CRM and the MAP: the Customer Relationship Management software and the Marketing Automation Platform. Sales works in the former, marketing works in the latter, ops is stuck making the two talk to each other.In B2C though, for some reason you all decided that the MAP is actually called a CRM and the B2B version of the CRM isn’t really needed because there’s often no sales team, instead it’s a customer support or product led motion.In both scenarios though the same thing happens to that central platform. It gets inherited by teams that weren't its original audience. It accumulates data it wasn't designed to hold. And it becomes the unofficial source of truth for the whole business without anyone explicitly deciding that was a good idea.Why Every Team Moved Into the CRM (And How It Lost Its Authority)So how did we get here? CRMs were built for one job: tracking the sales motion. Contacts, deals, stages, activity logs. They were good at that job. Then marketing moved in. Marketers ruin everything. But leadership is worse. Leadership started pulling board metrics from the CRM. Then the product team added usage data. Then we added ABM and account signals, and we had to push that data somewhere. Then AI interactions needed a home.What a mess.Everyone needed a record of the customer, and the CRM was already there. It’s literally called the Customer Relationship Manager. So it became the shared folder everyone saved their customer work into, even though it was designed for a very specific kind of work.The problem is that once data is stored in a CRM, it starts reflecting the team that works there. Sales edits the contact. Marketing overwrites a field. Customer success adds a note. Each edit is local logic applied to what everyone assumes is shared truth. The data looks official but you know deep down that the authority behind it belongs to whoever edited it last.Meg Gowell, Head of Marketing at Elly.ai and former Head of Marketing at Typeform crossed over from a Salesforce-first organization to one where the warehouse had already taken over:MEG GOWELL, Episode 155“The tricky part of our tech stack is that I’m used to Salesforce or HubSpot being the single source of truth. Here, our core business is represented more in the data warehouse than anywhere else, and Salesforce supports the sales-led part of the business.Understanding how those data pieces come together is something I’m still working through. I’ve only been here three and a half or four months, and it’s tricky. The biggest challenge is figuring out how the self-serve and sales-led motions fit together. In PLG, they have to serve one another. If your tech stack doesn’t support that, it becomes really hard.We run into questions like: do we have all the right data points in the right places for people to act on them? Do we know everything we need to know? I’ve really experienced how important the underlying data structure is, and how important consistency across tools is.In the past, there was this wide spectrum. In one area, we had a very advanced multi-touch attribution system. In another area, it was very basic reporting. So there was this weird mix of super deep and super surface-level, but without an underlying structure that fully worked.I think that happens to a lot of companies when they’re growing fast. You take opportunities where you see them, and you move quickly. Now we’re taking a step back and saying: we really ...
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224: How OpenAI’s GTM leader structures teams and spots standout candidates with Keith Jones
Jun 1601:02:24Tap to summarizeWhat's up everyone, today we have the pleasure of sitting down with Keith Jones, Head of GTM Systems at OpenAI.Summary: Keith's GTM systems team at OpenAI got split across 2 orgs, ran into the most wildly practical cost center problem imaginable, and ended up proving exactly why distributed systems teams at high-velocity companies don't work. In this episode, he walks through the full restructuring journey, explains why "be close to the money" now means be close to the budget rather than the revenue motion, and breaks down Symphony and harness engineering — the open-source agentic code orchestration tools his team built to ship production-ready GTM changes without going to the nth degree of "write this Apex class." He also has a filter for separating human candidates from AI-generated applications that is simple, specific, and immediately usable. If you run a GTM systems team, build one, or just want to understand what operating at 10x growth actually requires, this one is worth your time.About Keith JonesKeith Jones is the Head of GTM Systems at OpenAI, where he leads the team responsible for the tools, platforms, and technical infrastructure behind the company's go-to-market motion. He began his career across sales ops and marketing ops roles before joining Mural, where he built and led the GTM Systems function. He later served as Senior Director and Analyst at Gartner, covering revenue technology, before moving to OpenAI. Keith joins this episode as a technologist and practitioner; the views and opinions he expresses are his own and do not represent OpenAI.What Separates GTM Ops from GTM SystemsThe naming debate in martech ops has been running so long it's almost a genre. Marketing ops, revenue ops, GTM ops, GTM systems — the titles keep multiplying and nobody agrees on where one ends and the other begins. If you're in this function, you've had the conversation. In job interviews. In org design meetings. In budget justifications. It goes nowhere, and it keeps happening.Keith has a more useful framing. When he first came on the show, he drew a clean line. GTM ops handles process design, training, and the frontline support that keeps the humans in your GTM org running. GTM systems owns the tools, the technical infrastructure, the back-end work: Salesforce, integrations, scaling, the stack. That line still holds. But he's added something that makes it more useful than a job description.They're the ones in the room with every sales segment leader, every functional head, absorbing what the business actually needs and translating it into something buildable. Without that translation layer, a systems team is guessing. And guessing at OpenAI's pace doesn't go well.At OpenAI, both functions have kept evolving alongside the company. Denise Dresser came in as CRO with a complete vision for reshaping the go-to-market org. B2B marketing got folded in. The company launched ads. The org changed repeatedly and fast. Through all of it, the underlying logic held: GTM ops partners with the business, GTM systems delivers what that partnership requires.As for the labels, Keith's position is that they're the wrong thing to anchor on. At OpenAI, the specific titles of marketing ops or rev ops matter less than who owns the stakeholder work and who owns the technical delivery. The names on the teams are almost secondary. The friction comes from not having clarity on which team does which job and what flows between them. Most organizations that treat these two functions as interchangeable tend to find out why that's a problem the hard way.The clean requirements that GTM ops provides to GTM systems aren't a process nicety. They're what keeps a systems team from building the wrong thing at the wrong pace.Key takeaway: Draw a line in your own org between who owns stakeholder requirements and who owns technical delivery. If one person or team is carrying both, something is consistently slipping. Establish a regular meeting rhythm where GTM ops and GTM systems leaders hash through priorities together, and treat that handoff as seriously as any technical dependency.The Cost Center Problem That Reunited OpenAI's GTM Systems TeamOpenAI's GTM systems team didn't move under finance because someone had a grand theory about org design. They moved because of a cost center problem. And the cost center problem showed up in the most unglamorous way possible: headcount.The original case for moving was practical. Keith's team needed to accelerate a set of deep financial integrations — Salesforce data flowing into ERP systems, billing pipelines, downstream finance reporting. The work required close collaboration with the finance function. The initial plan was a wholesale move. What the org settled on instead was a compromise: split the team. Some engineers stayed under go-to-market. The rest moved into what OpenAI calls Enterprise Platform Technology (EPT), the org that reports to the CFO. On paper, the logic held. In practice, the friction started almost immediately.Two separate cost centers sharing an overlapping team create problems that don't announce themselves upfront. They surface sideways:2 separate budget owners with different priorities pulling the same engineers in different directions, Shared consulting firms split across orgs, with different teams allocating the same people to different workstreams, Tooling budgets that required negotiation across reporting lines rather than a single decision, Headcount competing directly against a new CRO's vision for building out the go-to-market orgThat last one is what forced the decision. Denise Dresser joined as CRO after budgets were already set, bringing a complete vision for reshaping the go-to-market org and the headcount requirements to execute it. Keith found himself competing against her priorities for resources from the same finite pool. Not by design. Just by the math of 2 leaders sharing one budget.The conversation was brief. Dresser knew Keith's team would keep supporting go-to-market regardless of which org they sat in. She knew she could hold him accountable. But she couldn't justify choosing between revenue-generating hires and systems resources from the same budget line when the answer was that obvious.The reunified structure looks different from what existed before. Keith now has a peer leading quote-to-cash and revenue-adjacent systems. Keith owns top-of-funnel data enrichment, pre- and post-sale workflow, and the support systems org. The org got flatter, the division of responsibility got cleaner, and the cost center competition disappeared.How GTM Systems and GTM Ops Stay Aligned After the SplitGTM ops stayed under the go-to-market umbrella when GTM systems moved to EPT. The obvious question: how do they stay connected? Keith's answer is a biweekly meeting he calls the most productive hour on his calendar. Six to seven people in the room from both sides of the new org boundary:Keith and his peer leading go-to-market systems, The manager running all of Enterprise Platform Technology, including people systems, supply chain, and revenue systems, The most senior leaders from growth, go-to-market ops, and rev opsNo prep deck. No pre-circulated agenda. Everyone spends 5 to 10 minutes writing down their top of minds — what's keeping them up at night, what's shifted, what needs cross-functional attention. Then the group talks through it. Where do the priorities overlap? Where are they diverging? Which teams need to be working together on something they're currently doing separately?It's not a status meeting. It's a priority alignment session with people who have the authority to act on what comes out of it.The distributed period was hard. It was also clarifying. The experience exposed exactly which parts o...
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223: How Zapier uses a shared brain to manage AI context and skills with Lindsay Rothlisberger
Jun 900:56:48Tap to summarizeWhat's up everyone, today we have the pleasure of sitting down with Lindsay Rothlisberger, Director of GTM Innovation at Zapier.(00:00) - Intro (01:23) - In This Episode (02:00) - Sponsor: Knak (03:08) - Sponsor: MoEngage (05:49) - How Zapier's RevOps Team Built Its AI Foundation (19:43) - Why Visibility Has to Come Before Governance in AI Adoption (24:58) - Sponsor: GrowthBench (25:58) - Sponsor: GrowthLoop (29:48) - How Zapier Fights Context Rot in Its AI Shared Brain (35:55) - How Zapier Governs Shared AI Skills from Review to Long-Term Ownership (39:27) - What Happens to RevOps When Everyone Around Them Can Build (45:05) - The Director of GTM Innovation Role and the Sharing Problem Nobody Has Solved (50:47) - What Keeps Lindsay Grounded in the Middle of All This Change (52:00) - Lindsay on Getting Buy-In and What She's Reading Summary: When a startup claimed in April 2026 that it invented the marketing engineer role and that RevOps professionals "just do tool integrations," Lindsay Rothlisberger had heart palpitations. Her team at Zapier had been building AI into GTM workflows for years before the announcement. In this episode, she walks through the 6-component AI governance model she published publicly: a golden path to Cursor, a structured shared brain in Google Drive, data policies built with the security team, a visibility layer powered by a custom Zapier agent, a context engineering strategy that fights context rot, and a red-yellow-green skills review gate. She also names the part of the model that's still broken, and it's more honest than most AI governance conversations allow. If your team is figuring out how to govern AI at scale without killing the momentum, this is the inside view from someone who's done it.About Lindsay RothlisbergerLindsay Rothlisberger is Director of GTM Innovation at Zapier, where she leads the company's AI-powered GTM transformation internally and works alongside customers navigating the same shift. She spent 4 years building Zapier's RevOps function from zero, scaling it into a cross-functional engine covering AI, systems, analytics, planning, and enablement, and growing ACV 10x in that time. Before moving into the innovation role, she led marketing operations and lifecycle programs at UNiDAYS across B2B and B2C markets. She writes on LinkedIn about what Zapier is actually shipping, what works, and what doesn't.How Zapier's RevOps Team Built Its AI FoundationMost RevOps teams doing serious AI work have been doing it longer than the current conversation suggests. The tools are newer and the terminology has changed, but building automated workflows that take unstructured data and produce structured, actionable outputs for salespeople and marketers? That's exactly what good RevOps teams were doing before anyone put a trending name on it.Lindsay's team at Zapier started experimenting with AI several years ago, when it was first becoming accessible. Zapier gave its RevOps team the tools to experiment early, and rather than waiting for a strategy to materialize, they picked a specific, annoying problem: sales handoffs. Salespeople were going into first calls without enough context about the lead. The team pulled all the relevant unstructured data, engagement records, support tickets, email threads, and used AI to generate clean, contextualized briefing materials. The result was a measurable lift in lead-to-opportunity conversion rates, and a pattern the team has used ever since: find something specific that's visibly broken, prove AI fixes it, then apply that logic somewhere else.That early foundation matters now because the landscape has shifted in a way that affects RevOps directly. Claude Code, Cursor, and similar tools have made it possible for people with no engineering background to build real things. Sales managers are writing AI skills that generate quarterly revenue strategies for reps. CS reps are building account monitoring tools. Lindsay's read on this is that the RevOps team's job isn't to slow that down. It's to give it a governance structure so it can scale without creating a mess, and to be the team that built the foundation those builds are operating on.At Zapier, that governance structure is anchored by an AI center of excellence led by a chief AI officer. The architecture is a hub-and-spoke model: the central team sets the frameworks, the guidelines, and the enablement resources; Lindsay serves as the spoke into go-to-market, with a partner who works alongside her. The 2 of them act as a feedback loop between what's happening on the ground in sales, marketing, and CS and what the central team needs to know. The center of excellence is small, just a handful of dedicated people, but it reaches into every function through the spoke structure.The first thing the center of excellence built for non-technical GTM employees was the golden path to Cursor. Cursor had already been adopted by Zapier's product and engineering teams. For GTM, the barrier wasn't the technology itself; it was the setup. Someone who's spent their career in spreadsheets and CRM doesn't automatically know how to configure a development environment. The golden path is step-by-step onboarding: from installation through a fully configured Cursor environment with the right MCP connections (Databricks, Zapier), the right rules, and the right context already loaded. The whole point is removing the 2-hour configuration overhead that otherwise kills adoption on day 1.That context is the shared brain: a structured Google Drive hierarchy with company-level, department-level, team-level, and working group-level folders. The first iteration meant converting existing documentation into markdown files and organizing them into a folder structure that agents could traverse predictably. Lindsay describes the experience of setting it up as oddly satisfying for an ops person who has spent years wishing the organization's institutional knowledge lived somewhere findable instead of scattered across a Google Drive that nobody had cleaned up in years. The goal of the initial build wasn't completeness. It was a working foundation that gave people enough context to get value from their agent setup without needing to build from scratch.The companies operating furthest ahead in AI adoption right now are the ones that treated the shared brain as infrastructure rather than a side project. Getting every GTM employee configured, context-loaded, and working from a shared knowledge base is unglamorous work, but it's the layer every other build depends on.Key takeaway: Before anyone on your GTM team builds anything with AI, create a centralized setup guide that handles environment configuration, approved MCP connections, and context loading from a structured knowledge base. Start with the tools your technical teams are already using and build a version of that golden path for non-technical employees. The 2-hour configuration friction that stops people on day 1 is a solvable problem, and solving it once prevents you from solving it individually for every person who tries to onboard.How Long It Actually Takes to Build a Shared BrainThe shared brain question that comes up in every version of this conversation is a practical one: how long does it actually take? Zapier's first rollout was a 4-week sprint, and the design of that sprint was deliberate about scope. Rather than trying to capture everything the organization knew, the team focused on what Lindsay calls the slow layer of context: things that don't change often. Company strategy documents. Ideal customer profile definitions. Lead and opportunity definitions. Basic playbooks. These documents already existed. The sprint was mostly ...
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