the gtm engineer

Michel Lieben, founder and CEO of ColdIQ — a GTM agency that scaled to multi-million ARR — talks about weaving LinkedIn content, ads, and outbound into a coordinated GTM flywheel. He explains warming accounts with pre-send ads, repeatable tech-stack content formats, hooks that prove credibility, and using Cursor plus Claude Code to turn campaign learnings into operational systems.

Listen on Spotify Oran Akron is the VP of AI GTM at monday.com, where he leads a new internal team focused on rebuilding go-to-market processes with AI agents. Oran joined monday.com in 2018 when the company had ~100 employees and just five sellers. Since then, he’s helped scale the revenue org to over 1,000 people across sales, customer success, marketing, and partnerships while growing the RevOps team from one to more than 80. About six months ago, after conversations with monday.com’s new CRO about the future of GTM, Oran decided to step away from RevOps and incubate something new. His team operates like a startup inside Monday, moving fast and shipping agents that are already handling the majority of inbound leads, expanding into new languages and markets, powering outbound research, and surfacing product usage signals that trigger sales engagement.In this podcast, we discuss:* Why Oran decided that AI was going to fundamentally change go-to-market and why he started a new team inside Monday dedicated to rebuilding GTM processes from scratch with AI* Why improving the top of the sales funnel was the first problem Oran’s new team tackled and how AI agents now handle inbound qualification and meeting booking across multiple languages* How Oran’s team built an outbound agent that automates account research and generates personalized recommendations for reps, while preserving their autonomy over how to use it* The importance of monitoring AI agents in production and how Oran set up dashboards, failure tracking, and observability to scale with confidence* What Oran learned about change management, internal branding, and gamification to get a 3,000-person org to embrace AI agentsEpisode highlights:* After seven years of building and leading RevOps, Oran felt that if he had to build Monday’s sales org today, he would do it much differently. With support from Monday’s CRO, they agreed to have Oran step away from RevOps and incubate a new team within Monday focused on rebuilding GTM processes from scratch with AI.* Oran’s new team started with inbound lead handling. The team tested against cohorts of their best-performing human reps, scaling only after results were equal or better. The results matched or exceeded top rep performance, and Oran’s AI now handles the majority of inbound leads, conducts real conversations that run five minutes or longer, qualifies prospects, and books meetings directly on AE calendars.* They built an outbound agent to listen to everything on the web, from podcasts to financial reports to LinkedIn activity, and synthesize that research into personalized account plans. The agent then recommends which department to target, why to start there, and how to position Monday’s offering based on what’s happening at the account. Importantly, reps retain full autonomy over how they use the output, as Oran doesn’t believe in forcing a playbook on them.* Oran treats his agents like an engineering system that needs to be closely monitored. His advice for others implementing AI agents in GTM is to start by tracking the same business performance metrics used for human reps, then layer on engineering-style observability to catch where things break* Oran explains that change management was the biggest organizational challenge he faced in his new role. The most effective approach combined top-down communication about the future of AI in go-to-market, gradual rollouts rather than all-at-once deployments, and a deliberate effort to make the agents feel exciting rather than threatening.Where to find Oran:* LinkedInTranscript details:(00:00) Intro(03:06) Oran’s monday.com journey(08:06) Why Oran pivoted to focus on AI in GTM(11:07) Prioritizing top of funnel wins to build trust in implementing AI across the org(14:19) How Amanda, the inbound agent, qualifies leads and books meetings in two minutes instead of 24 hours(15:42) Expanding into new language markets without hiring local teams(18:42) The AI ecosystem behind the agents and why Amanda was rebuilt four times in six months(20:40) Monitoring, guardrails, and treating AI agents like an engineering system(24:22) The next most impactful use cases beyond inbound(24:39) The outbound agent that researches accounts across the web and builds personalized account plans(26:35) Product usage signals and identifying intent in real time to trigger sales engagement(27:36) Giving reps autonomy over the AI output rather than forcing a single playbook(29:26) How the team serves information through the CRM and monday.com’s own AI tools(30:38) Change management, over-communicating the strategy from the top, and other strategies to drive adoption(35:29) How AI changes what’s required from salespeople and why relationship building becomes more important(40:18) Why moving fast and launching before perfection is a worthwhile tradeoff(43:29) Reinventing PLG by understanding user intent in real time instead of distributing leads 24 hours later(45:46) Favorite tools, growth campaign, and wrap-upFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Diana Marcela Gonzalez, Senior GTM Strategist who builds Allbound plays with HubSpot and data; Brendan Tolleson, RevPartners co-founder focused on RevOps and revenue-driving go-to-market. They dig into Allbound’s three pillars: data intelligence, brand gravity, and assisted prospecting. They cover website de-anonymization, signal-based list building, lifecycle stages and lead scoring, contextual multi-channel outreach, and how to keep CRM data fresh.

Jaleh Rezaei, Co‑founder and CEO of Mutiny, built personalization at Gusto and now leads an agentic AI company for go‑to‑market teams. She discusses how AI turns marketers into full‑stack GTM athletes. Conversations cover scaling one‑to‑one personalization, agents generating on‑brand sales assets, reducing sales‑marketing friction, and making the buying experience a key brand differentiator.

Dave Birckhead, a MarTech and growth systems leader who led marketing technology at Spotify and ezCater, talks about rebuilding scalable stacks and taking an AI sabbatical to build AI-native GTM systems. He discusses why Spotify built custom messaging, automating global creative production from months to days, prototyping cross-functional AI agents, and the production challenges of moving prototypes into reliable, monitored systems.

Listen on Spotify Umar Farooq Adam leads global GTM innovation and engineering at Hitachi Vantara, a subsidiary of Hitachi Limited. Starting his career in the Middle East working for a small B2B SaaS startup, Umar then moved to an ISV selling to defense sectors in the US and Europe. Afterwards, he spent time at Microsoft working on customer lifecycle management, renewal operations, and solution design, giving him a strong sense of each component in GTM. After Microsoft, Umar joined Hitachi Vantara as an inside sales rep covering APAC territories. While in the role, he noticed data quality and workflow problems firsthand, and started fixing them within his own territory. His work’s impact got attention from leadership, and he was able to get sign-off on running a 12-month POC that aimed to prove out whether the fixes he implemented could scale globally. By the end of the project, leadership created a dedicated role for him to lead GTM engineering globally.In this podcast, we discuss:* How Umar went from an individual sales rep to leading global GTM engineering at a 10,000-person company* Why getting leadership buy-in early is key when building out a GTM engineering function across a large org* What Umar focused on and deprioritized during the project that aimed to prove out the value of a GTM engineering function* How to think about friction in separate terms that resonate with sales, marketing, and operations leadership* Why data collection’s impact is minimized without activationEpisode highlights:* Umar’s path in Hitachi started with fixing problems in his own territory. Data quality was a mess because CRM records weren’t updating when things like mergers and acquisitions happened. As a result, he set up alerts using LinkedIn Sales Navigator and ZoomInfo to track company news, then manually raised requests with the data team to fix account hierarchies. He also built a manual waterfall enrichment process in Excel, pulling contacts from the CRM first, then ZoomInfo, then Lusha. Those fixes made enough of an impact that they were noticed by leadership and kicked off the POC to prove out implementing programmatic changes globally.* During the POC, Umar deliberately focused only on data quality and workflow automation. By keeping his scope tight and working on high likelihood of success projects, he managed to move the needle in both areas. Data quality improved by at least 60% through waterfall enrichment in Clay. Workflow automation sped up tasks like account research, identifying what products accounts were in the market for, and crafting relevant messaging.* Umar explains that one takeaway from the POC was the importance of aligning with regional leadership. In global organizations, people trust their local leaders more than global top-down initiatives. Once he started working closely with regional leaders, everything moved faster. They knew where the friction was, what was realistic to implement for their teams, and what didn’t work in their markets.* In order to achieve cross-functional alignment, Umar learned to speak each org’s language. For sales, Umar aligned on sales plays and targets. For marketing, he aligned on campaigns and ROI. For operations, he layered on top of their existing work rather than competing with it. The key was showing value that would resonate with each stakeholder rather than pitching a tool.* Umar calls out that the teams that win won’t necessarily be the ones who accumulate the most data, but the ones who can activate the data they collect across systems. He sees GTM moving from automating repetitive tasks to automating decision-making and triggering next best actions based on real-time account intelligence.Where to find Umar:* LinkedIn Transcript details:(00:00) Intro(02:44) Umar’s background(04:52) Starting as a sales rep and noticing data quality problems firsthand(07:50) The specific problems Umar solved in his own territory before the POC(08:50) How success in his own territory led to the global POC(10:13) What happened during the 12-month POC(12:19) Why these problems hadn’t been solved before(13:50) Navigating cross-functional stakeholders and politics at a 10,000+ person company(15:26) Challenges during the POC(16:49) Why Umar focused only on high-likelihood wins and avoided experimentation(18:19) How prompting and workflow design varied across APAC and EMEA(19:44) Advice for implementing GTM change at large organizations(23:02) How to build the skills and intuition for GTM engineering(25:28) Regional differences in GTM execution across APAC, EMEA, and Americas(28:13) Future trends in GTM(30:34) How GTM engineering varies by org size(31:40) Favorite tools, growth hack, and wrap upFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyGabe Larsen is CRO at Signals, a company selling cloud employees, AI teammates that do full jobs rather than single tasks. Gabe spent 2 years doing door to door sales in Germany before beginning his career in consulting and investment banking in New York and the Middle East. After that, he settled into SaaS where he joined InsideSales in 2013 when they were at $5M in ARR and helped them grow to north of $100M. The company was a pioneer in sales acceleration, building power dialers and training programs that helped companies shift from field sales to high velocity inside sales. After InsideSales, Gabe joined Kustomer, a Zendesk competitor, at $10M ARR. They grew quickly and got acquired by Meta for >$1B. He spent two years at Meta before getting spun out during their year of cuts in the name of efficiency. During that time, he saw how companies were being pushed to do more with less, but still relying on the old playbook of adding tools and adding people. That's what led him to reconnect with Dave Elkington, the founder of InsideSales, to start Signals.In this podcast, we discuss:* What the shift from field sales to inside sales taught Gabe about how industries evolve and why he’s betting cloud employees will drive the next shift toward autonomous organizations* Some of the early marketing and sales playbooks that helped InsideSales go from $5 to $100M* What separates AI agents from the cloud employees that Gabe and his team are building* The win-loss cloud employee that’s already replaced expensive consulting firms* How expectation setting with users gets them comfortable talking with AI employees* Why framing them as cloud employees instead of AI agents changes how you collaborate with themEpisode highlights:* Gabe distinguishes cloud employees from AI agents in three ways. 1). Cloud employees are autonomous rather than deterministic. They have access to tools and decide when to use them, rather than following strict if X then Y workflows. 2). They do full jobs rather than single tasks. For instance, an SDR cloud employee will handle anywhere between 20 to 300 tasks from setting up calls to running calls to sending gift cards after the call. 3). Cloud employees operate across multiple channels - phone, email, SMS, Slack, LinkedIn, and chat. A customer can chat with the cloud employee, and if they call 30 minutes later, it remembers the earlier conversation.* Gabe explains that he runs weekly coaching sessions with four cloud employees that report to him. He reviews their output and gives feedback on things like email personalization and report building. By framing them as teammates rather than tools, it changes how organizations interact with them. Instead of setting up a workflow and forgetting about it, teams invest in improving the cloud employee over time, which compounds into better output and enables companies to do more with less.* Gabe explains that the Signals win-loss cloud employee runs the entire deal analysis and reporting workflow better than the consulting firms he used to pay 25x to do the same thing. When a deal is lost, the cloud employee reaches out across email/phone/text to get an interview scheduled. Once the interview is scheduled, the cloud employee runs it (or can hand it off to a human), sends the interviewee a gift card, then ultimately writes up a report and shares it in Slack. After the report has been shared, sales leaders have a back and forth conversation with the cloud employee to dig deeper into findings across the many interviews it’s done.* Gabe explains that implementing customer-facing AI personas was initially a challenge, and when Signals deployed a customer service cloud employee at a Fortune 500 company, they saw high hang-up rates. The fix was having the cloud employee open with honesty: “I’m an AI persona. I can get you to a human in five to ten minutes, but if you challenge me, you might be surprised.” That framing led customers to be more open to trying the AI experience. Many returned with positive feedback, saying the AI handled their questions better than junior reps would have.* Gabe argues that SaaS tools are becoming limited because they’re building AI agents trapped inside their own platforms. Gong builds agents that only work in Gong. Apollo builds agents that only work in Apollo. He compares it to Siri, which can only access Apple apps and is locked out of everything else. Cloud employees are different because they’re cross-platform by design, just like real employees that move between Salesforce, Apollo, Gong, and Slack throughout their day. As a result, Gabe sees a shift coming where companies move from renting siloed tools, to hiring AI teammates that operate across their entire stack and compound over time.Where to find Gabe:* LinkedIn* SignalsTranscript details:(00:00) Intro and Gabe’s background(06:04) What Signals does and the concept of cloud employees(09:32) The history of InsideSales.com and the shift from outside to inside sales(14:13) How InsideSales changed the way that modern selling works(15:50) Assembly line sales and the formalization of the SDR role(18:26) Category creation and research-based marketing at InsideSales.com(20:38) Why Utah became a hub for sales talent(24:32) How Signals landed on the cloud employee framing(28:20) The four areas where cloud employees are deployed and how they differ from AI agents(34:32) Deep dive on the win-loss cloud employee(41:20) How to introduce customer-facing AI to prevent skepticism in audiences(46:48) Other cloud employee use cases(49:37) The revenue advisor cloud employee and ask-me-anything for sales teams(52:34) Weekly coaching sessions with cloud employees and how to think about training them(56:00) Gabe’s view on build vs. buy, why he thinks SaaS tools are dead and that the future is AI teammates(01:00:43) Favorite underrated tool, growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyEoin Clancy is the VP of Growth at AirOps, a company that helps brands get found in LLMs like ChatGPT, Claude, and Perplexity. Before joining AirOps, Eoin ran growth and marketing at Telnyx, where he began as a growth engineer. He spent a year using AirOps at Telnyx to automate inbound SDR work, enabling him to move 8 inbound SDRs to outbound. After that, he was sold on the product, and 18 months ago joined the AirOps team. When Eoin joined, AirOps had fifteen employees and was doing less than $1.5M in revenue. Since then, they've nearly 10x'd headcount and well over 10'x’d revenue.In this podcast, we discuss:* The new role of content engineering and why AirOps believes it will thrive in the new world of search* Why generic AI-generated content doesn’t work anymore, and what actually differentiates content that ranks in LLMs* How to make AI-generated content look like it was written by your team* How content teams like Ramp and Carta use high velocity experimentation to separate themselves from competitors* The case for investing in documentation and support guides* How AirOps uses their own product to speed up and increase the impact of their webinar contentEpisode highlights:* 18 months ago, AI slop worked, and teams could pump out pages at scale while watching website visits climb. The old SEO playbook was to look at the top three results for a target keyword, see what sections and questions they covered, and release pages that copied or rephrased it. AI slop enabled that playbook at 10x scale. However, Google and LLMs have adapted and now reward unique insights that add to the conversation, rather than rehash what’s already out there, effectively killing the benefits of generating loads of AI slop content pages. Eoin explains that Reddit performs so well in AI search because many comments offer new takeaways.* The two biggest changes to content in the age of AI are that: (1) it is easier to create more content than ever, and so the bar for speed (of net new and refreshed content) to keep up with the market has significantly increased, and (2) LLMs process content and rank differently than Google, making certain tactics (like offsite content) more important than before.* In order to make AI content look like the team that wrote it, Eoin suggests feeding the AI internal context before having it write. If the goal is for AI to write like engineers talk, give it access to engineering Slack channels and standups, so the words and phrases in those places make their way into content. He also explains that building effective content workflows requires serious time and investment that is well worthwhile to speed up overall content creation and refresh.* Eoin points to Ramp and Carta as examples of content teams that succeed by moving fast. Ramp has pushed the boundary of offsite content on Reddit because they ship so fast that they’re able to learn from rapid experimentation. On the other hand, Carta’s content team is now able to ship content three times faster than before, enabling them to go to market to new audiences and industries quickly.* Eoin advocates for investing in documentation for the sake of AI search distribution. Docs are often the last thing to get updated and the first thing to go stale, but they’re rich with context, well structured, and contain the most nuance about a product. When someone asks ChatGPT for a tool that does X and integrates with HubSpot, docs are what surface. Eoin gives Salesforce as an example of a company that understands this. Most of their sitemap is how-tos and community content, and that’s what ranks first in AI search.* Because AI search prioritizes fresh, up-to-date insights, AirOps regularly refreshes their existing content in order to keep it up to date. After recording a webinar, they use the transcript to auto-update existing related articles with new takeaways. This enables same-day turnarounds, so after a webinar is recorded in the morning, their content library is refreshed by day’s end.* Branded search terms are an underrated metric. Even as teams optimize for AI search, people will still Google a company name after discovering it in ChatGPT. If a competitor is running a conquesting campaign on that branded term, they’ll steal the click. Eoin recommends keeping an eye on and tracking branded search terms over time, to ensure all bases are covered.Where to find Eoin:* LinkedIn* AirOpsTranscript details:(00:00) Introduction to Eoin and overview of AirOps(03:34) Eoin’s background as a growth engineer at Telnyx and becoming an AirOps user before joining(5:03) AirOps’ growth trajectory(05:34) What VP of Growth means at AirOps, the builder enablement function and content engineering(8:58) How AirOps is helping their customers adapt to the new reality of content creation(11:00) How the modern content role differs from historical SEO roles(16:14) Why Eoin thinks content teams will grow, not shrink(20:52) How much should AI actually write, why AI slop content doesn’t work anymore, and where humans need to stay in the loop(26:47) How to measure whether your content is sufficiently human and value-add(32:06) The importance of content velocity(34:24) Underrated metrics in content(37:37) Why documentation is a goldmine for AI search visibility(41:16) How AirOps uses their own platform for webinar content workflows(46:34) Agentic browsers and how websites are going to change(47:29) Where search is headed(51:11) Why AirOps bet on webinars as a growth channel(54:03) Favorite underrated tool, growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyGaurav Vohra worked in consulting for 5 years, where he honed his ability to attack problems with a high degree of vigor, speed and urgency, before he joined Superhuman to run growth in 2015. At Superhuman, he ran growth, analytics, and growth product from the early days to tens of millions in ARR. During this time, Gaurav dove deep into the technical + analytical parts of growth, while also spending time on deep craft and artistry, which is clear to anyone who ever interacted with the Superhuman product. After a ~10-year run at Superhuman, Gaurav took a step back to become a startup advisor for businesses like Clay, Replit, and Wispr Flow. Subscribe for weekly updates on top GTM Engineering content, open roles & moreIn this podcast, we discuss:* The Grammarly acquisition of Superhuman and what about the Superhuman name invoked Grammarly taking it on* The two mission-critical characteristics Gaurav looks for in growth operators, how he tests for those skills, and what it took to build the skills himself* Balancing the analytical with artistic and creative side of growth and Gaurav’s reach vs. resonance framework* How to build very good taste to drive resonance in growth work* How growth skills building is changing (and not) in the age of AI * The future of growth vs. growth product vs product teams* The growth hack hall of fame move that Grammarly made during the Superhuman name changeEpisode highlights:* Superhuman was initially called Supercharged, but changed after the team thought it was too sports car oriented. Superhuman ended up being a brand name so broad and aspirational that Grammarly eventually took it on as the overall business name.* The two critical skills in successful growth operators are CPU and velocity. All other important and relevant skills can be derived from processing through large amounts of information to find solutions (CPU), and from doing so + iterating extremely quickly (velocity).* CPU and velocity are not innate, but require a consistent and concentrated effort. This comes from pushing to move faster and think from first principles to process information. If you get to the end of the day and your brain is tired, you know you’re pushing on the CPU and velocity muscles.* While people who process information a bit slowe but arrive at the right answer can be successful in growth, it becomes a hiring risk, especially in growth roles that require particularly high levels of velocity.* Content, products and ideas can fall in any of the four quadrants of resonance X reach. High reach = large distribution, and high resonance = deep influence and impact. The best products and growth ideas sit in the quadrant of high resonance and high reach.* Different sorts of roles within growth require different levels of artistry (and resonance). Generally speaking, the closer you get to touching product, the more important art becomes.* Building the skill of taste (which influences resonance) requires many reps of seeing what amazing looks like, testing ideas out in the world, paying attention to what is currently driving resonance + reach (can often measure by what is going viral), and by spending time understanding users.* As AI becomes more effective, the importance of technical skills appears to be reducing - with coding or analytics as prime short-term examples. It’s unclear how it will all shake out down the line, but having the deeper level of understanding can be useful on foundations work and/or to be in the top 1 or 0.1%.Where to find Gaurav:* LinkedIn* GauravVohra.comTranscript details:(00:00) Pod intro(03:45) Gaurav introduction and background(05:45) Superhuman’s name and the Grammarly acquisition(10:06) The two critical traits to being effective in growth - CPU and velocity(13:44) Building CPU and velocity muscles(20:57) How writing & analytical skills fit into the CPU / velocity framework(24:57) Whether smart, slower processors can be effective in growth(27:28) Balancing the left vs. right brain in growth and Gaurav’s resonance vs. reach framework(32:56) How to improve resonance and taste(38:06) How building growth skills is changing with AI(42:44) Whether PLG vs. SLG is changing with AI(44:31) How growth vs. product roles are evolving(47:00) Other parts of the growth landscape that are changing(49:53) AI pricing(51:43) Intense competition and startups as the new investment banks(56:03) Growth hack, favorite tool & conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyJen Igartua is the CEO and Co-Founder of Go Nimbly, a RevOps agency with 100 employees that works with companies like Intercom, Twilio, Zendesk, and Vanta. She started her career at Bluewolf, a major Salesforce partner later acquired by IBM. There, she developed an obsession with breaking down silos between sales and marketing after seeing firsthand how easily they become misaligned. That led her to start Go Nimbly, which now provides fractional RevOps services ranging from $20K/month engagements to six-figure enterprise contracts, plus a partnerships motion that has delivered over 600 Gong implementations in the past two years. What’s more, Jen also owns a board game company that recently got picked up by Walmart and Target.Subscribe for weekly updates on top GTM Engineering content, open roles & moreIn this podcast, we discuss:* The different stages of signal delivery systems and why most companies stall at stage one - Slack alerting* Why RevOps teams need roadmaps and to build strategically instead of just fighting fires* Why your first party product data is gold when building expansion and renewal plays* What caused such widespread tool sprawl, the “build everything” overcorrection and the optimal state of GTM tooling* Why the best GTM engineers will start to feel more like architects who obsess over data models and their downstream effects* The swim lanes between RevOps, Growth, GTM Engineering, and system teams at varying company sizes* What’s happening to marketing automation as tools like Clay absorb workflows like audience building and emailing capabilitiesEpisode highlights:* Intercom used first-party product data to build an expansion play that combined ticket volume increases, support team growth, and declining NPS scores. Instead of generic outreach, reps could lead with specific information about a customer that they themselves might not even know.* Jen explains that most companies start delivering product signals with Slack alerts, but adoption quickly trails off because it becomes noisy. A better long-term solution is building a custom object for signals in Salesforce. This enables teams to hone in on which signals actually convert into opportunities and prioritize accordingly.* Understaffed RevOps teams get stuck putting out fires instead of doing strategic work. The fix isn’t to ignore the fires. It’s to staff the team well enough to handle the day-to-day while still building toward a longer term vision. This enables RevOps to be strategic thinkers with thoughtful roadmaps.* Before 2022, there was a buying spree in tech that created tool bloat and shelf-ware. Now, there’s an overcorrection toward building everything in-house. Jen points out that while building in-house is great, it can create problems when the person who built it leaves or it’s not built for scale. Orgs are left with knowledge gaps and a bunch of half-documented workarounds across Clay, n8n, Salesforce flows, and Slack automations.* Jen shared that most companies aren’t choosing a single orchestration platform for their automations. Instead they’re building department by department in whatever tool seems slightly better for that use case. This creates nightmares like trying to find which automation is driving which field changes and nobody knowing if it’s coming from Clay, Zapier, Workato, n8n, or somewhere else.* The best GTM engineers will evolve into RevTech architects who understand the entire go-to-market stack, obsess over data architecture, and think about order of operations and downstream effects. Knowing when to use Clay versus Salesforce versus when to use a dedicated tool is one key that separates prototype builders from systems thinkers.* Marketing automation platforms like Marketo and Eloqua haven’t meaningfully innovated in 15 years. Meanwhile, marketers are naturally unbundling. Events run through Luma, newsletters live in Beehive or Substack, webinars happen on Sequel. Now, Clay is absorbing audiences and email. If all that data can flow through an orchestration tool straight to Salesforce, the only thing left for expensive marketing automation platforms is nurture streams.Where to find Jen:* LinkedIn* Go NimblyTranscript details:(00:00) Intro, Jen’s background, and founding Go Nimbly(05:19) Go Nimbly’s business model(06:16) Current trends in RevOps(09:04) Using product signals for expansion and renewal plays(11:48) The different stages of delivering signals to sellers(15:15) AI CRMs vs. Salesforce for mid-market and enterprise(16:50) Browser-based automation as an alternative to single pane of glass approaches(18:21) Signal prioritization(22:34) How to define RevOps(25:26) What separates high-performing RevOps teams from the rest(27:43) GTM engineering, AI Operations, and where they fit within GTM(35:14) The importance of systems teams and governance at larger companies(39:06) The evolution of tool sprawl(44:12) The fix to GTM tooling sprawl(45:36) How the best GTM engineers think like architects(49:31) Pushing back on complex requests(51:56) The GTM trends Jen is following, like marketing automation software(56:12) How running a board game company influenced Jen’s thinking and her RevFest conference(58:04) Jen’s favorite underrated tool, growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com