Marketers of Technology

TransUnion's marketing solutions business helps consumer brands and their agencies identify, reach, and measure their marketing to individual consumers, using the same data and identity infrastructure that powers TransUnion's core credit bureau business.

In a recent episode of Marketers of Technology, we sat down with Allyson Dietz, Marketing Strategy Lead of TransUnion, to learn how the company's own research into marketer AI adoption is reshaping how it thinks about data, ROI, and personalization at scale.

Topics Discussed:

• Why leading marketing strategy for TransUnion's marketing solutions business means marketing to marketers, one of the hardest audiences to reach
• TransUnion's AI adoption survey, conducted with UTA Advisory (formerly MediaLink), and the gap it found between marketer confidence in AI and proven ROI
• Why data, people, and process, not the technology itself, are the real constraints on AI adoption
• Why measuring AI success through cost savings is the wrong KPI for a marketing organization
• The difference between dabbling in AI-assisted creative and step-change AI marketing
• How TransUnion uses AI in creative production while staying committed to representing real people
• The data quality problem sitting underneath one-to-one personalization at scale
• Why human oversight, and better prompting, remain part of every AI-assisted marketing workflow
• How TransUnion is setting 2027 priorities around white space and a new CMO's brand narrative

GTM & Technology Adoption Lessons:

• Confidence in AI and proof of AI are two different problems: In survey research TransUnion conducted with UTA Advisory, Dietz said "89% of the marketers we interviewed said that they feel very confident in where AI is going and their adoption of AI, but very few of them, about 50%, actually have clear ROI associated with the investments they're making."

• Data is the real bottleneck, not the model: Dietz named the specific constraints she sees blocking AI adoption inside marketing teams directly: "the things that seem to be limiting most marketers today are data, people, and process." She was equally direct about the cost of skipping that step: "if you put bad data into AI, you're gonna get bad results.

• Cost savings is the wrong KPI to sell against: TransUnion's research found that "65% of marketers are really measuring AI success through cost savings and cost efficiency," which Dietz called a limitation, not a win. Her framing: "marketing's really our role as marketers, B2B or B2C, is to be a growth engine for the business," and a team that only tracks headcount or budget reduction from AI "is kind of focusing on the wrong KPI."

• Dabbling in AI is not the same as changing the marketing model: TransUnion has used AI to apply new outfits and settings to real photographed people in its creative production, a way to be more efficient without a full reshoot. But Dietz drew a hard line around what that actually represents: "I consider that to be dabbling.

• Personalization at scale is a data pipeline problem before it is a creative problem: TransUnion's marketing solutions business exists to enable one-to-one targeting, but Dietz was candid about what holds marketers back from using it: "I personally get nervous as I think about putting out an ad campaign.

• AI workflows still need a human in the loop, and the failure point is usually the prompt, not the model: Asked about mistakes that surface in AI-assisted marketing execution, Dietz described the standard the industry is converging on: "AI powered with human oversight," adding a specific caveat that reframes where accountability sits: "some of that is even human error and how we prompt it."

// Sponsors: Front Lines -- Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres talks with David Haas, Chief Marketing Officer of Haas Marketing Company, a digital marketing and modern tech stack agency that helps growing companies scale through search optimization, paid media, and outbound sales automation.

With more than thirty years in marketing, half of it built around live events and half around web and content, David explains why AI has changed his work more in three years than the previous twenty-five. He breaks down how his two-person team and five AI agents produce the output of a far larger shop, why Google's EEAT framework still decides what ranks in both traditional and AI search, and why the right answer to "how much of my content should AI write?" is that there is no right percentage.

Topics Discussed:

• David's thirty-plus years in marketing, from live event-driven lead generation to web and content, and the "bomb" AI has set off in the last three years
• What Haas Marketing Company does and the small, growing tech and healthcare companies it serves, which often lack an established marketing department
• Why brilliant specialists at small companies hate documenting their expertise, and how an agency can amplify it
• Running a team of two humans and five AI agents that do the work David estimates would once have taken around twenty people
• Why churning out generic AI content fails, and how zero-click search changes the goal from ranking to earning the click
• Google's EEAT framework (experience, expertise, authoritativeness, and trust) and why it applies to traditional search and AI answer engines alike
• A repeatable content process: record a ten to fifteen minute call with an expert, feed it to an agent trained on the company's voice, then review it by hand for hallucinations
• How smaller companies using this approach are outranking much larger, established competitors
• Why most of Haas Marketing's opportunities come from word of mouth, and why that has to be paired with consistent content and LinkedIn presence
• Why there is no correct percentage of AI-written content, and what matters instead
• David's goal for the end of 2026: finding a handful of small, unknown companies with great ideas and helping them get found

GTM & Technology Adoption Lessons:

• Judge AI content by EEAT, not by how much AI wrote. David's answer to his clients' most common question is that no percentage applies; content built on real experience, expertise, authority, and trust can be 95% AI-drafted and still perform.

• Start with a human expert, not a blank prompt. A short recorded call with a subject matter expert gives an agent the seed ideas and examples it needs, producing content that reflects genuine expertise instead of generic output.

• Keep a human in the review loop. Even with agents trained on a company's voice and past content, David insists on human eyes to confirm the draft hasn't hallucinated or drifted off message.

• Optimize to earn the click, not just the ranking. As AI-generated answers and zero-click results spread, content has to give people a reason to visit the site rather than simply restate what they could read in the results.

• Small companies can beat the incumbents on search. David has seen smaller organizations using EEAT-aligned content outpace well-established competitors that have dominated Google for years.

• Pair network with content. Word of mouth from a thirty-year career drives most of Haas Marketing's opportunities, but consistent content and LinkedIn activity are what keep that referral flow going.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres talks with Katie Hayes, Head of Marketing at Ozone API, the standards-based platform that handles the complexity of open banking and open finance for banks and financial institutions worldwide. As Ozone API's first marketing hire, Katie has spent four and a half years building the department from a barely functional website into a global brand that looks far bigger than its four-person team.

She explains how she markets a white-labeled, highly regulated product to banks and regulators, why closed roundtables with a handful of regulators beat broader tactics, how a campaign built around "we do the boring stuff so you don't have to" gives a compliance product personality, and how DOJO AI and Claude let a lean team punch well above its weight.

Topics Discussed:

• Katie's path from agency work in South Africa to in-house roles across luxury brands and non-profits, and how she became Ozone API's first marketing hire in 2022
• What Ozone API does: helping banks and financial institutions deploy standards-compliant open banking and open finance APIs, from account access and payments to embedded finance
• How the founders' role in shaping the UK's open banking standards produced a blueprint for what good looks like, now deployed across regions at different regulatory maturity levels
• Why Ozone API is white-labeled, and what that means for marketing to banks, central banks, and regulators rather than end users
• Rebuilding the brand from a minimal, barely mobile-friendly website into a new positioning, visual identity, and messaging that has been sharpened through prospect and client feedback
• The "we do the boring stuff so you don't have to" campaign and the idea of marketing to people rather than just businesses
• Making thought leadership central through an active blog, an open finance tracker, video demos, and conference speaking
• Running marketing with four people in-house plus a brand agency, an SEO consultant, and AI marketing agents, while the brand is perceived as far larger than it is
• Using DOJO AI and Claude to automate reporting and scale output on a lean budget
• Why niche events and closed regulator roundtables work best, and which social channels Ozone API tested and dropped, from Instagram and X to Reddit as a listening channel
• Katie's goals for the rest of 2026: aligning marketing to a new go-to-market strategy and optimizing AI and HubSpot pipeline workflows

GTM & Technology Adoption Lessons:

• Make a compliance product feel human. Ozone API's long-tail campaign, "we do the boring stuff so you don't have to," turns regulatory complexity into a promise of relief, giving a technical category some personality.

• Market to people, not just to businesses. Katie's framing of "B2P" reflects that the CTO, procurement, legal, and compliance contacts at a bank all need different explanations of the same product.

• In regulated B2B, a small room of the right people beats a big crowd. Closed roundtables and niche open banking events, where a few regulators from around the world are in the room, have been among Ozone API's most effective channels.

• Optimize for LLM discovery, not just Google. Because Katie's buyers increasingly search their category in AI tools, the team is prioritizing AEO and the keywords that get Ozone API indexed and recommended.

• A lean team has to be ruthless about channels. After testing Instagram and X without results, Ozone API concentrates on LinkedIn and YouTube and uses Reddit mainly to listen to technical questions.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

Native Commerce makes it possible for anyone to turn an idea into an online business, then launch and grow it from one place, combining an AI-built storefront with deep e-commerce fulfillment and logistics expertise.

In a recent episode of Marketers of Technology, we sat down with James Wall, VP of Brand and Marketing at Native Commerce, to learn why the company is deliberately running two completely different buyer journeys at once.

Topics Discussed:

• How a career that started in DJing and event promotion shaped James's self-taught approach to brand and marketing

• How Native Commerce evolved from a 20-minute grocery delivery company called Jiffy into an AI-powered e-commerce platform

• Why Native Commerce split its go-to-market into a multi-step enterprise funnel and an immediate self-serve funnel

• Why the company is migrating some existing enterprise clients onto its newer self-serve AI product, not the other way around

• Why James refocused Native Commerce's marketing budget on search after testing PMax, display, LinkedIn, and events

• Why Native Commerce leads sales conversations with the customer's destination, not the mechanics of how it gets them there

• Why Native Commerce ties its own commercial terms to how much a client's brand actually grows

• How building a same-day grocery delivery operation became the company's deepest operational advantage

GTM & Technology Adoption Lessons:

• Run two distinct funnels for two different buyer maturities, don't force one message: James said Native Commerce deliberately splits its go-to-market into an enterprise buyer journey, with as many as ten or eleven vetting steps for complex clients like a business with a hundred and twenty physical locations and no e-commerce, and a simple, immediate self-serve journey for someone just starting a project, rather than trying to serve both audiences with the same funnel.

• Reverse the usual migration path to prove a new product can handle enterprise scale: Native Commerce is migrating some of its existing enterprise clients onto its newer AI-driven self-serve product, the opposite of the typical self-serve-to-enterprise upgrade path, specifically to elevate the new product from what could look like a novelty into something proven at real operational complexity.

• Refocus budget on the channel that's actually converting, not the ones you're used to running: After trying PMax, display, LinkedIn, and events, James said the team made a deliberate decision in the last six months to focus primarily on search, because it is what is actually producing qualified, converting leads, and they are preparing to add Product Hunt specifically for the AI product's launch.

• Sell the destination, not the mechanism: James cited a philosophy that people buy the outcome, not the process behind it, comparing it to travelers who care about the resort, not the airplane's fuel efficiency. In sales conversations, Native Commerce leads with what a client's business will look like once fulfillment and logistics complexity are handled, rather than the technical details of how.

• Align commercial incentives with customer growth, not just retention: James said most e-commerce providers charge on a GMV basis, so Native Commerce's own revenue depends on their clients' brands actually growing, which led the company to launch a growth acceleration program aimed at expanding client revenue, not merely hosting their storefront.

• Let deep operational expertise, not just software, be the retention moat: James said e-commerce providers are notoriously hard to switch away from, and Native Commerce's real differentiator is operational knowledge, like warehouse placement and picking efficiency.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres talks with Kelly Owen Grover, Chief Marketing Officer at eVerged, an EV charging company that pairs its chargers with renewable energy and battery storage to unlock business models rivals can't easily copy.

A fifteen-year biotech go-to-market veteran who made an intentional pivot into clean tech, Kelly explains her signature reframe — an EV charger isn't infrastructure, it's a marketing tool that outperforms paid search and social because drivers are always in market for it. She also walks through EVerged's deliberate choice to stay US-focused rather than expand internationally, and shares how her solo side practice, Stay Fresh Consulting, grew entirely through relationships built in past roles.

Topics Discussed:

• Kelly's path from fifteen years in biotech go-to-market roles to an intentional pivot into clean tech and EV charging infrastructure
• What EVerged does differently: pairing EV charging with renewable energy and battery storage to unlock more flexible business models
• The dual B2B/B2C nature of EV charging marketing: winning over "site hosts," the businesses that buy, while also designing for the drivers who use the chargers
• Reframing EV charging itself as a marketing and demand-generation tool rather than pure infrastructure, and the data behind why it works
• Creative ways businesses are monetizing chargers, from loyalty-tier discounts to valet bundling
• Why the EV charging market is more fragmented by business model than it first appears, and how that shapes real competition
• EVerged's deliberate choice to stay focused on the US market, plus a small Canada presence, rather than expand internationally
• Signs of accelerating EV adoption across Latin America, and the contrast even within neighboring countries
• Kelly's solo consulting practice, Stay Fresh Consulting, and how it cross-pollinates insight back into her day job
• Her broader case for why sustainability has a place in nearly every company and career, not just climate-focused ones

GTM & Technology Adoption Lessons:

• Reframe your product as a marketing channel, not just a cost center. Kelly's central pitch to prospects: an EV charger isn't infrastructure spend, it's an always-on demand-generation tool that beats the conversion rates of paid search and social.

• Design for both sides of a two-sided market. EVerged's marketing has to work for the businesses that buy, the site hosts, and the end users who never pay directly, the drivers — two different value propositions under one brand.

• Not every competitor can copy your angle. A rival's back-end business model may not support positioning charging as a marketing tool or bundling it with renewable energy, which narrows the real competitive set more than market size suggests.

• Expansion discipline can be a strategy, not a limitation. EVerged is deliberately staying US-focused, plus Canada, because the domestic opportunity is still large and international regulatory complexity isn't worth it yet.

• Trust compounds across companies. Kelly's solo consulting business grew entirely through relationships built at past jobs — a reminder that reputation built in one role is a durable, transferable asset.

• Sustainability is a horizontal, not a vertical. Her advice to listeners: look for where sustainability already touches your company or career, because most organizations now have some exposure to it, climate-focused or not.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres talks with Kieran McQuilkin, Director of Product Marketing at Macabacus, the Excel, PowerPoint, and Word add-in used by investment banks, private equity firms, venture capital groups, and other financial services teams to keep deal documents accurate and on-brand.

Kieran explains why a company built a decade before generative AI existed refuses to brand itself as an "AI company," how its deterministic, non-AI functions build trust where a single wrong number can mean real financial and legal exposure, and why its AI features run on each client's own LLM connection instead of a proprietary model. He also shares how his small marketing team mines its own sales calls for content research, and why customer webinars and third-party review sites now matter as much for AI-driven search as they do for human buyers.

Topics Discussed:

• Kieran's path from business journalism to product marketing, and how he became Macabacus's first dedicated product marketing hire

• What Macabacus does inside Excel, PowerPoint, and Word for investment banks, private equity, venture capital, and other financial services teams

• The shift from a largely self-serve, product-led business to a blended enterprise sales motion, and what that meant for his role

• Why Macabacus, built as an Excel plugin a decade before generative AI existed, doesn't position itself as an "AI company"

• The case for deterministic functions over AI guesswork when a single wrong number in a deal document carries real financial and legal risk

• DeckCheck AI: how it catches contextual errors across a presentation and points directly to the slide and number at fault

• Why Macabacus runs its AI features on a client's own LLM connection — Gemini, Claude, or OpenAI — rather than building and hosting its own model

• How the internal marketing team uses AI to connect CRM, call-recording, and ABM data into fast dashboards and segment recommendations

• Using an MCP-connected tool to mine sales-call transcripts for first-draft blog research, while keeping final writing human-led

• Why Kieran is doubling down on customer webinars and third-party review sites now that AI search tools lean heavily on outside validation rather than owned content

GTM & Technology Adoption Lessons:

• Don't let "AI company" become your whole positioning. Macabacus leans on ten years of deterministic, non-AI functionality as its credibility anchor, using AI to complement the product rather than define it.

• The riskier the output, the more verification matters. As AI makes first drafts faster and more confident-looking, Kieran argues the "last mile" of human and deterministic review becomes more valuable, not less, especially in financial services.

• Build on your customers' existing AI stack, not your own. Letting clients plug in their own LLM connection avoids the cost, privacy, and trust issues of building a proprietary model.

• Third-party validation now feeds AI search directly. With LLM-based search citing review platforms and outside voices, customer webinars and sites like G2 and Capterra matter as much for AI visibility as for human buyers.

• A product-led motion and an enterprise motion can coexist. Macabacus kept its self-serve funnel healthy while building a sales-assisted path for larger, more customized enterprise deals.

• Use AI to mine your own first-party data for content ideas. Pulling insights from recorded sales calls gives marketing a faster, more specific starting point than a blank prompt.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres talks with Julian Meinke, founder and CEO of Postelix, the full LinkedIn engine for B2B teams. After six years leading marketing at two European hypergrowth startups, Prewave and Sereact, taking both from zero to a combined $20 million in ARR and helping raise over $200 million in funding, Julian built Postelix around a single obsession: turning an anonymous target market into a named list of actual buyers.

He explains why he deliberately walked back from AI-automated outbound messaging in favor of human-written outreach, how Postelix is evolving into a Slack-based "coworker" that flags buying signals in real time, and why he still has Claude and ChatGPT check each other's work rather than trusting either one blindly.

Topics Discussed:

• Julian's path through fifteen years in marketing, including six years leading marketing at the hypergrowth startups Prewave and Sereact, before founding Postelix roughly six months ago

• What Postelix does: turning an anonymous target market into a named list of real buyers, so companies know exactly who to advertise to and which events or influencers to track

• Why Julian walked back Postelix's original AI-automated outbound messaging feature in favor of human-written outreach

• How a named buyer list feeds everything downstream: ad targeting, event selection, influencer partnerships, and sales follow-up

• Postelix's upcoming shift toward a Slack-based "coworker" that proactively flags buying signals, like a target account's employee viewing a LinkedIn profile after a campaign

• Running a two-person company supplemented by "a bunch of virtual" AI agents, including using Claude and ChatGPT to check each other's work

• Why Postelix deliberately limits its own go-to-market to two channels, founder-led outbound and LinkedIn ads, rather than spreading across many

• How Postelix prices around the size of a company's target account list rather than tiered feature plans

• Onboarding new customers by mapping the problem that actually motivates a buyer, rather than relying on static demographic ICP data

• Julian's advice for a first marketing hire at a B2B startup: talk to customers directly rather than relying on secondhand input from sales

GTM & Technology Adoption Lessons:

• A named list beats an anonymous audience. Postelix's entire value proposition rests on converting a vague target market into specific identified buyers, which then sharpens every other marketing motion built on top of it.

• Don't let AI automate your way into the spam folder. Julian deliberately pulled back from automated AI outbound messaging because he believes inboxes are already flooded with it, choosing human-written outreach as a differentiator instead.

• Buying signals belong where the team already works. Postelix's next iteration moves intelligence out of a standalone dashboard and into Slack, so account signals reach the right marketer or sales rep in the moment rather than waiting to be checked.

• Pick two channels early, not ten. Julian's rule for an early-stage company: one higher-reach channel and one one-to-one channel, rather than spreading thin across every available tactic.

• Verify AI output by cross-checking tools against each other. Julian's habit of having Claude and ChatGPT review each other's work is a simple, repeatable way to catch mistakes neither tool would flag on its own.

• Talk to buyers firsthand, not through secondhand filters. His strongest advice to a new marketing hire: get direct, first-person interviews with customers rather than relying on what the sales team reports back.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres talks with Cristina Restrepo, Head of Marketing at Statt, an end-to-end AI workflow platform built for public policy, government affairs, and regulatory teams. As Statt's first and only marketing hire, Cristina built the brand's entire foundation from scratch, then repositioned the company as a purpose-built AI company, borrowing cues from category leaders like Claude, Harvey, and Rogo, to win over a historically tech-averse industry.

She shares how predicting a State of the Union address almost word for word became Statt's biggest brand moment to date, how an ongoing midterm election forecast report turned into the company's single largest source of qualified leads, and how a 25-person team uses its own product to build internal workflows that keep marketing on top of the news cycle.

Topics Discussed:

• Cristina's path through fifteen years in marketing and communications at pre-seed and Series A B2B and AI startups, and how she became Statt's first and only marketing hire
• Building Statt's entire marketing foundation from zero: logo, brand, website, SEO, and lead generation
• Why the public policy, government affairs, and regulatory audience has historically been underserved by technology, and the apprehension that comes with it
• Repositioning Statt as a purpose-built AI company by studying how category leaders like Claude, Harvey, and Rogo present themselves
• Moving to a consultative model that frames Statt as a customized system woven into a customer's existing workflow and templates, rather than another tool to log into
• Automating "hearing intelligence," a task that used to mean sending an intern or analyst to physically sit in on hearings and take notes
• How Statt predicted a State of the Union address almost exactly, using its own policy dataset, and the brand attention that followed
• Turning an ongoing midterm election forecast report into Statt's single biggest source of marketing-qualified leads
• Statt's global data footprint, from deep US legislative coverage to EU, UK, South American, and Asian regulatory data
• Building internal, Statt-powered workflows, including one that tracks relevant DC-area policy events and another that monitors upcoming elections

GTM & Technology Adoption Lessons:

• Borrow positioning cues from outside your category. Cristina modeled Statt's "purpose-built AI company" positioning on recognizable AI brands like Claude, Harvey, and Rogo, rather than on legacy research and monitoring tools in her own space.

• Overcome adoption apprehension by reframing the tool as a system, not software. Presenting Statt as something built alongside the customer and integrated into their existing templates opened doors with both technical buyers and more cautious, leaner teams.

• Predictive content can outperform explanatory content. Statt's State of the Union prediction and ongoing midterm forecast report drove more traffic and leads than content that directly explains the product, by demonstrating data quality instead of describing it.

• A proof-of-concept report doesn't need to mention your product to generate pipeline. Roughly a fifth of Statt's marketing-generated deals trace back to a report that never explains what the company does.

• Keep AI output on-brand with a living internal tool. Statt built its own branded, memory-updated internal AI tool so that every AI-assisted asset reflects the company's latest messaging as it evolves.

• Use your own product internally before asking customers to trust it. Statt's marketing team runs on workflows built from its own platform, both to stay informed and to prove out the product's value firsthand.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

In this episode of Marketers of Technology, Andres sits down with Karolina Kelleher, Head of Marketing at Generis, the 25-year-old company behind CARA — an enterprise content and data management platform built for the world's most heavily regulated industries. Karolina walks through how a team of four supports customers across life sciences, government, and financial services, why CARA is engineered to never hallucinate an answer, and the trust-based process behind bringing in an outside ad agency that helped drive a 150% increase in inbound leads. She also shares a clear-eyed view of where AI belongs on a lean marketing team — as an assistant, not a teammate — and why her goal for the rest of the year is to start marketing AI itself, not just using it.

Topics Discussed:

• Karolina's path into marketing: from an English literature degree and a near-miss sales role to becoming Generis's first marketing hire
• What CARA is and how it evolved from a Documentum front-end into an independent, AI-enabled content and data management platform
• How CARA's AI framework is built for regulated industries: permission-aware, fully audited, and designed to never hallucinate an answer
• The mix of customers relying on CARA: primarily life sciences, plus government, financial services, and other highly regulated organizations
• Running marketing with a team of four: what Generis keeps in-house versus what it outsources
• The internal audit that led Generis to bring in an outside ad agency, and the trust-first vetting process behind that decision
• The 150% year-over-year increase in inbound leads that followed a broader push to reassess underperforming campaigns
• Karolina's rules for using AI as a marketing assistant: template-first prompting, tone-of-voice guardrails, and never skipping human review
• Why she avoids feeding finished, long-form content straight into AI for repurposing, and what she does instead
• Her goal for the rest of the year: moving from "using AI for marketing" to "marketing AI" itself, with campaigns built to run for years

GTM & Technology Adoption Lessons:

• Audit before you scale, not after. When ad spend crept up without matching results, Generis's team paused to ask a blunt question — are the ads bad, or are we bad at ads — before bringing in outside help.

• Vet partners on trust, not just capability. Generis interviewed multiple ad agencies and chose based on which one it actually trusted with a specialized, compliance-heavy industry — a decision that preceded the 150% jump in inbound leads.

• Treat AI as an assistant, never a team member. Karolina draws a hard line: AI can execute tasks, but it can't be handed ownership of a channel or a decision.

• Prompt with your own material, not a blank ask. Feeding AI a previously written, on-brand example — not just bullet notes — is what keeps its output from reading like generic AI copy.

• Use AI to compress research time, not to skip verification. Fast-tracking an unfamiliar sub-vertical is a legitimate use case, as long as a human on the team signs off before anything goes out.

• Don't repurpose finished assets through AI. Dumping a completed ebook into a prompt for "LinkedIn slides" just regurgitates the same ideas; better to ask AI for new angles and build from there.

• The next opportunity is marketing AI itself, not just using it. For unglamorous, regulated categories like content and data management, the differentiator is making the AI story itself compelling and durable.

// Sponsors: Front Lines — Silicon Valley's leading Podcast Production Studio. We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. Mention you are a listener and get a 10% discount. www.FrontLines.io/Podcast-as-a-Service

FrontierGTM helps AI infrastructure, developer platform, and agent companies turn deeply technical products into clear market narratives, stronger customer traction, and sustainable growth.

In a recent episode of Marketers of Technology, we sat down with Ryan Pollock, VP Marketing of FrontierGTM, to learn why he thinks building an AI product is no longer the hard part, and how he applies the software free-trial playbook to his own consulting time.

Topics Discussed:

• How a career through Oracle, Google Cloud, DigitalOcean, and Together AI led Ryan to found his own go-to-market consultancy
• Why Ryan believes building has become relatively trivial while go-to-market has become the harder problem
• Who Ryan considers his ideal customer: companies building AI infrastructure, developer platforms, and autonomous or specialized agents
• The "FrontierGTM Rush" offer that gives selected companies free hours of consulting and AI credit before any paid engagement
• Why Ryan built a deliberately bold "gold rush" brand identity for a highly technical consultancy
• Why Ryan currently spends nothing on paid advertising and is limited by his own capacity, not demand
• Ryan's argument that AI productivity gains should push labor markets toward far more fractional employment

GTM & Technology Adoption Lessons:

• Treat a free trial of yourself as the product, not just the service: Ryan's core funnel mechanic is "FrontierGTM Rush," an application where selected companies get up to three hours of his time and five hundred dollars in AI credit at no cost, explicitly borrowing the software free-trial model and applying it to a fractional consulting engagement.

• Bet that go-to-market, not building, is now the harder problem: Ryan said pre-trained models have made building relatively trivial for AI infrastructure and agent companies, which is why he positions FrontierGTM specifically around the go-to-market side rather than product development.

• Prove a technical category doesn't have to look boring: Ryan built a deliberately bold, visually distinctive "gold rush" brand identity for a company serving deeply technical AI infrastructure clients, arguing that even the most technical categories have room for creative, memorable marketing, and cites Apple as proof that technology and creativity aren't in tension.

• Grow through network and appearances before spending on any paid channel: Ryan said he currently spends nothing on advertising, relying instead on his professional network, direct inbound interest, and appearances like this podcast, because his own capacity, not demand, is the current constraint on how many clients he can take on.

• Advocate for the business model you're personally betting on: Ryan argues that AI-driven productivity gains should be pushing labor markets toward far more fractional and flexible engagements, and positions his own willingness to work fractionally, ahead of how most tech job postings are still structured, as part of what he's selling.

• Package pricing around outcome-sized engagements, not fixed retainers: After the free trial period, Ryan structures paid engagements flexibly, from as little as ten hours to as long as ten weeks, matching the scope of the engagement to what a client actually needs rather than a standard retainer.

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