Humans of Martech

What's up everyone, today we have the pleasure of sitting down with Kelsie Dube, Director of Marketing Operations and Automation at Unanet.

• (00:00) - Intro

• Kelsie's Marketing Operations Assessment Framework: https://drive.google.com/file/d/1x3IJf9tHku4-fASS9AQE_F6sJ1FLK3PN/view?usp=sharing
• Liza Adam's Digital Twin: https://www.linkedin.com/pulse/smartest-ai-teammate-youll-ever-build-liza-adams-c7rnc/

About Kelsie Dube

Kelsie Dube is the Director of Marketing Operations and Automation at Unanet, where she joined in 2026 to rebuild the company's measurement and reporting foundation. Before that she spent 7 years at Sophos, climbing 5 roles from Marketing Operations Specialist to Director and leading the team through much of that run. She started her career in data entry, scrubbing lead lists and learning the VLOOKUPs she still uses most days, and that ground-floor view shapes how she thinks about the craft.

Outside of work she's a mom of 2, an amateur basketball player, and a snowboarder who guards the hours between 5 and 7 for her kids.

Why New Marketing Ops Leaders Should Audit Before They Act

The first 90 days at a new company come with a quiet dare. Ship something. Plant a flag. Prove they were right to hire you. Most new marketing ops leaders answer that dare by ripping out a tool they never liked and installing one they used at their last job, all inside the first 2 weeks.

Kelsie did the opposite when she joined Unanet. Her first move was to change nothing.

The reasoning was twofold. Without a real map of how work gets done, any change you make is a guess, and you'll probably aim it at the wrong target. And Unanet had said in the job description that they wanted someone who'd slow down and understand the business first, which is part of why she took the role. For a marketer who came up through ops and now leads it, that patience is the whole point. You have to understand what's happening well enough to be dangerous, she says, before you can tell anyone what to actually fix.

The harder version of this is resisting your own experience. A new CMO walks in and wants a different marketing automation platform because they used one at the last company. Kelsie feels that pull too, since she's learning a few systems here for the first time. Her answer is to make the existing stack work before spending a dollar to replace it. Right now she's rebuilding how campaigns get captured and tagged to campaign members, and she's solving it with a Salesforce workflow or Power Automate instead of a purchase. Replacing the tool would cost more and probably solve less.

Then comes the part nobody warns you about, the clock. Listening mode has a shelf life, and everyone, including you, is quietly counting the days until you produce something. Kelsie gave herself a hard deadline of 30 days to finish the audit, then engineered small wins into every week so the learning never looked like stalling. New hires have one thing veterans don't, an empty calendar, and she guarded it. A recurring block every Friday at 2:00 to turn that week's discoveries into something usable. One of those blocks became a 30-minute campaign taxonomy diagram she walked the leadership team through, a quick and visible proof that better structure meant better reporting down the line.

The instinct to ship in week one is the most expensive habit in operations, because a fast fix aimed at the wrong problem still has to be undone later. The operators who compound value are the ones who can sit in not-knowing long enough to find the actual bottleneck.

Key takeaway: Give yourself a fixed audit window, 30 days is a reasonable default, and block a standing hour each week to turn what you've learned into one small, visible win. Keep a running log of quick wins as you find them so you always have something to ship while the deeper work is still in progress.

How to Run a Marketing Ops Audit When You Join a Company

"Do an audit" is the most repeated and least defined phrase in marketing ops. Everyone nods along. Almost nobody can tell you what's actually in one, which funnel stages it covers, or where it starts on a Monday morning.

Kelsie's version starts before day one. The moment she accepted the Unanet offer, she began mapping ownership lines: what marketing ops owns, what rev ops owns, what IT owns. She wrote out the questions she had and sent them to her leader ahead of her start date, partly out of genuine excitement and partly to get a head start on the RACI.

From there it's a series of conversations that snowball. She interviews everyone from her own team to her peers to the level above to her business partners, and she ends every conversation the same way: who else should I talk to? The list never stops growing, but the patterns start to show. Along the way she confirms the systems in her swim lane against the job description, asks what she's missing, and books dedicated working sessions where someone walks her through a live task like a lead import. She records those sessions, then feeds the transcriptions to an LLM to pull out the patterns, a tip she picked up from a mentor that turns hours of shadowing into a usable roadmap.

She also runs the work herself.

Joining a much smaller team than she was used to, she asked people to hand her a lead import and a campaign creation task so she could run them personally. Part of it was capacity, since a director who can still execute is extra hands when the team is slammed. Part of it was fluency. As she says, a director isn't really a "do-rector," but being right there in the work is how you understand it well enough to change it later.

Data privacy rides shotgun through all of this. Coming out of the cybersecurity world, she asks about data retention and opt-in policies early, then files them under "needs struct...

What's up everyone, today we have the pleasure of sitting down with Kathleen Schaub, Author, Strategist, and Advisor in Marketing Management and Organizations.

We cover:

• (00:00) - Intro

About Kathleen Schaub

Kathleen Schaub is an author, strategist, and advisor focused on marketing management and organizations, working through KathleenSchaub.com. She spent 9 years leading IDC's CMO Advisory practice, where she advised hundreds of technology marketing leaders, and she's a longtime contributor to leading martech publications including CMSWire. Her work argues that markets are complex adaptive systems rather than predictable machines, and her book lays out 4 mindset shifts, investor, navigator, statistician, and ecologist, for leading marketing in a volatile world. These days she splits her time between writing, advising, passion projects, and being a grandmother of 2.

Why Marketing Behaves More Like Weather Than a Machine

Every marketing leader has sat across from a finance executive who wants one thing. Put a dollar in, know what comes out. Budgets get built on that promise. Forecasts get defended on it. And every year, the results wander off somewhere the model never predicted.

Kathleen has spent more than a decade explaining why that keeps happening. Her line is that marketing is not a vending machine. A vending machine is controllable and predictable. You put your money in, you press the button, you get the exact thing you chose. Executives, she says, would give almost anything for marketing to work that cleanly. Marketing never has.

The weather comparison is a humbling one. We carry supercomputers in our pockets, we have decades of atmospheric data, and we still can't say for sure whether it'll rain tomorrow. Marketing runs on something messier than the atmosphere, which is people. Buyers, brands, influencers, partners, and the economy, all reacting to each other at once. Kathleen calls the result a complex system, the kind scientists study, where every interaction feeds back into the next and quietly introduces unknowns into every situation.

That's the part most measurement frameworks skip over. Marketers, salespeople, and customer experience teams all work at the edge of the company, the seam where the controllable inside meets the uncontrollable outside. Their whole job is to manage, predict, and measure a world that refuses to hold still. No amount of dashboard polish changes the fact that half the inputs live outside the building.

Kathleen keeps the parts of the old factory toolkit that still earn their place. The shift she wants is smaller and harder to swallow. Accept that markets are what the military calls VUCA, volatile, uncertain, complex, and ambiguous, then adopt measurement practices built for that reality instead of pretending the reality is something tamer. The methods already exist. Other fields use them every day. Marketing just hasn't bothered to translate them yet.

The uncomfortable implication is that a lot of the dashboards marketing teams present with total confidence are measuring a machine that was never really there. The teams that win the next decade will be the ones who stop apologizing for uncertainty and start building for it.

Key takeaway: Audit your current reporting for any number you present as a guarantee. Rewrite each one as a range or a probability, and rehearse saying "here's what's likely and here's what could move it" before your next budget review. Present every forecast as a weather report, a set of ranges you can actually defend.

The 4 Mindsets Marketing Leaders Need for Complex Markets

You can buy a new attribution platform, restructure the team, bolt on a dozen dashboards, and still land exactly where you started. Kathleen thinks she knows why. You changed your operations without changing the thing that steers them, which is your mindset. She treats mindset as the director of every action a team takes. Leave it untouched, and every operational upgrade delivers roughly the same results you've always had.

Her favorite way to explain this comes from Buddhism. Your mind is the ox, and the cart is everything the ox drags behind it, every outcome your marketing produces.

The leverage is quieter than it looks. Change your mindset and the dozens of small decisions you make every day start bending, even slightly, and over enough decisions you end up miles from where the old direction would have taken you. That's why the mindset has to move first. The 4 she recommends all come from worlds that are genuinely volatile and complex, but they're worlds anyone can picture, and each one maps onto something marketing leaders already wrestle with.

Investor: stop treating the budget like a cost center you spend down, and start treating it like a portfolio you risk for a better future return., Navigator: change the relationship between your plan and your measurement, adapting like a pilot or a sailor who reads the conditions in front of them instead of clinging to the route., Statistician: give up the hunt for certainty and accept that everything in the human and natural world runs on probability. Kathleen calls this the hardest of the 4 because our brains hate it., Ecologist: manage the conditions people work in, because behavior arises from the intersection of an individual and the context around them, so managing the person by themselves only covers half of it.

Read them together and a pattern shows up. Every one asks you to trade the illusion of control for the ability to adapt. That trade is where most transformation efforts quietly die, because swapping tools is easy and swapping beliefs is not.

Key takeaway: Before you approve the next platform or reorg, write down the belief driving it in one sentence. If that belief still assumes marketing is predictable and controllable, fix the belief before you spend the money. Pick one of the 4 mindsets and name the single daily decision it would change for you this quarter.

Why ROI Doesn't Work as a Marketing Measurement

Ask a room of marketers what keeps them up at night and ROI lands near the top every time. It's the question waiting in every board meeting. What drove revenue last month, and how much will this next bet add? Kathleen has been chasing that question longer than most. Running IDC's CMO advisory practice for 9 years, she kept asking leaders whether they were actually getting ...

What'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

About Sharon Gai

Sharon 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 Layer

When 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 Skill

For 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 AI

She 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 Taste

Every 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...

What'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 Martin

Michele 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 Ladder

Most 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 It

Every 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 Role

Marketing 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 Marketing

People 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 ...

What'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

About Amanda Natividad

Amanda 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 World

Every 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 Direct

Open 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 Teams

There'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...

What'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

About Cara Caruso and Dustin Taylor

Cara 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 Know

Most 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 Defense

The 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 Lawsuit

Here'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 off

Once 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...

What’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

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 Gravity
We conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.

Episode 2: The Eye of Context
We 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 Masquerade
We 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 Tower
Today, 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.

---

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FLOOR 4: THE DISPATCH TOWER
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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 Time

Your 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 store

Funny 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...

What’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

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OPENING
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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 Gravity
We conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.

Episode 2: The Eye of Context
We 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 Masquerade
Today, 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 Tower
Next, 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.

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FLOOR 3: THE CORRELATION MASQUERADE
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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 Scale

Jason 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 Trap

Tobias 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 Revenue

When 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 ...

What’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

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 Gravity
We conquered the source of truth and discovered that the data warehouse replaces the CRM with portable audiences.

Episode 2: The Eye of Context
Today, 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 Masquerade
Next, 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 Tower
Then, 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.

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FLOOR 2: THE EYE OF CONTEXT — AI Hallucinations, Data Quality, and Context Engineering
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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 Nonsense

Humans 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.

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BOSS BATTLE: The Hallucination Oracle
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What’s up folks, welcome to our 4 part series of Crawling THROUGH THE DUNGEON OF MARTECH ARCHITECTURE

You’ve arrived at Part1 : The Fall of CRM Gravity 

• (00:00) - Intro

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 Gravity
You’ll conquer the source of truth and discover that the data warehouse replaces the CRM with portable audiences.

Episode 2: The Eye of Context
You’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 Masquerade
You’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 Tower
You’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. 

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FLOOR 1: Why the CRM Lost Its Authority
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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 ...