--- title: "Why AI + GTM Engineers Can't Replace RevOps" episode: 85 podcast: "The LeanScale Podcast" publisher: "LeanScale" guest: "Tessa Whittaker" guest_title: "Founder & RevOps Advisor (ex-ZoomInfo VP RevOps)" date_published: 2026-06-05 date_modified: 2026-07-22 duration: 00:41:01 word_count: 7502 topics: ["revenue-operations", "ai-in-gtm", "gtm-strategy", "enterprise-sales", "sales-enablement"] canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/ source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app" license: "Free to quote and cite with attribution to The LeanScale Podcast." --- # Why AI + GTM Engineers Can't Replace RevOps _Tessa Whittaker on the strategic layer AI can't automate, and leading enterprise AI transformation_ **Episode 85 · The LeanScale Podcast** Tessa Whittaker, Founder & RevOps Advisor (ex-ZoomInfo VP RevOps) · Hosted by Anthony Enrico Published June 5, 2026 · Updated July 22, 2026 · 00:41:01 Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/ **Topics:** Revenue Operations · AI in GTM · GTM Strategy · Enterprise & Public-Sector Sales · Sales Enablement ## Executive summary The board deck says AI plus a couple of go-to-market engineers just replaced the RevOps team. Tessa Whittaker — a UCLA grad who spent nearly a decade at Salesforce climbing from executive assistant to Senior Director of Strategy & Operations for Tableau's Global Enterprise business, then ran the ops org through ZoomInfo's move up-market as VP of Revenue Operations, and is now founder of a senior-operator RevOps agency already on contract with a Fortune 500 to lead an AI transformation across a 500-person operations organization — thinks that thesis breaks the moment it meets reality. In this conversation with LeanScale co-founder Anthony Enrico, she makes the contrarian case that GTM engineers can execute judgment; they can't manufacture it. Her sharpest frame is the why / what / how split. Executives (CEO, CRO) own the why — what market, what category, how we win. A highly technical GTM engineering layer is excellent at the how — enrich these records, build this automation, wire this ICP. What gets missed is the what: the VP-of-RevOps job of translating strategy into processes, business and operating design, and an architecture built for a 100-rep future rather than a 5-rep present. Her tell-tale anecdote: sitting with an AI startup's GTM engineer who, mid-build, asked her what ARR was. Cheap technical execution without the strategic layer, she argues, quietly ships over-engineered infrastructure that becomes tomorrow's bottleneck — a point Anthony echoes as 'playing business' instead of doing what the stage actually requires. The middle of the episode is a working operator's view of enterprise AI transformation. Tessa argues the hardest job in any ops org is prioritization: you must run the business — forecasting, pipeline, QBRs, territory and account planning, making sure people get paid — none of which can stop, while simultaneously transforming the business AI-first, often with the same headcount and a mandate to do it with fewer people. Real transformation, she insists, has to hit people, process, and technology together; you can't layer AI on top of messy processes and stale data structures and expect anything to change. That is why she is blunt that headcount will not fall on day one: reductions require rigorous operations, heavy standardization, and strong data architecture first. AI is not coming for your job today — but the operators who refuse to become AI-fluent are the ones who eventually get replaced. For practitioners, the playbook is concrete. Ruthlessly prioritize — three initiatives delivered at A-plus beat ten at C-minus the field can't absorb. Avoid 'AI slop' by working analog-first: write your own hypothesis and judgment down manually, then use AI to find examples, crunch data, and level it up. Own your own upskilling; don't wait for your company to enable you. Her tactical starting move: open a Google Sheet, list every recurring task by cadence and level of effort, mark manual vs. automated, and map where AI or agents fit — then take it to your manager. In an AI world, the gap between self-starters and everyone else is widening fast. The closing arc is career and market. Tessa's through-line — do the next job before you have it, and trust you'll figure it out — carried her from EA to founder. Her agency wedge: the market is full of technical fractional shops and big consultancies, but short on senior operators who have lived both enterprise and startup and can execute while navigating the politics. Who should listen: RevOps leaders wanting to capture the AI moment, founders deciding whether to hire strategists or engineers, and revenue executives resetting board expectations about what AI can and can't do. ## Key takeaways 1. **GTM engineers execute the 'how' — they can't replace the strategic 'what'** — The market narrative is that AI-first GTM engineers building end-to-end workflows make expensive, seasoned RevOps leaders unnecessary. Tessa's counter: the layer that disappears in that model is the person who takes high-level strategy and translates it into processes, requirements, and scalable design. You may need fewer requirement-gatherers, but the strategic architect becomes more important, not less. _Why it matters:_ Don't swap your strategic RevOps hire for a cheaper engineer and assume the judgment comes free. Pair a senior operator who owns the 'what' with a technical team that owns the 'how.' _For:_ Founders, RevOps Leaders, Revenue Executives 2. **Use the why / what / how split to staff a modern RevOps org** — Executives (CEO, CRO) own the why — market, category, how we win. GTM engineers are excellent at the how — enrichment, automation, ICP plumbing. The VP of RevOps owns the what: the processes, business strategy, operating design, and architecture that gets handed to engineering to make real. Collapsing the what into the how is where teams break. _Why it matters:_ Map every RevOps responsibility to why, what, or how, and make sure someone senior is unambiguously accountable for the 'what' before you scale the engineering layer. _For:_ RevOps Leaders, Revenue Executives, Founders 3. **The GTM-engineer-asked-what-ARR-was problem is real** — Working a few hours a month with an AI startup's GTM engineer, Tessa was walking through lead-to-opportunity and opportunity-to-contract design and the engineer stopped to ask what ARR meant. Brilliant technical execution with no business context ships infrastructure that looks fast but misses the implications of building for scale. _Why it matters:_ Technical fluency is not business fluency. Keep a strategist in the loop on design decisions so speed doesn't outrun judgment. _For:_ Founders, RevOps Leaders 4. **Over-engineered GTM infrastructure kills companies before they scale** — AI makes it trivially easy to build fancy bells and whistles, and over-customization becomes bottlenecks and breakage down the line. Anthony frames the failure mode as 'playing business' — building infrastructure that isn't stage-fit instead of doing what the current stage actually requires. Design for standardization and scale, not maximal customization. _Why it matters:_ Bias toward simplification and standardization. Architect for the 100-rep future so you don't rebuild everything the moment you hit hyper-growth. _For:_ Founders, RevOps Leaders 5. **RevOps talent is bifurcating — pick a lane** — Five to ten years ago RevOps was a generalist who was decent at business strategy and systems admin. Now the role is splitting: the deeply technical GTM-engineering lane, or the strategic decision-maker on the hook for the GTM infrastructure overall. In large enterprises the operators are strategic but not technical; in startups they had to be both. _Why it matters:_ Decide whether you're building (or becoming) the technical engineer or the strategic architect — and at enterprise scale, know that even strategic operators now must be AI-literate to stay effective. _For:_ RevOps Leaders, Revenue Executives 6. **Enterprise transformation must change people, process, and technology together** — You cannot layer AI onto existing processes and data structures and expect anything to change — it defaults back to what it was. Real transformation re-architects the go-to-market AI-first (lead-to-opportunity, quote-to-cash, handoffs) while simultaneously changing how the operational teams themselves work, think, and are structured. _Why it matters:_ Scope AI transformation across all three legs. If you only automate external workflows without re-architecting the ops org, the change won't stick. _For:_ Revenue Executives, RevOps Leaders, Founders 7. **Prioritization is the single hardest job in any ops org** — Every operator must run the business — forecasting, pipeline, QBRs, territory and account planning, paying people — none of which can ever stop — while also being handed a mandate to transform the business AI-first, often with the same headcount that lacks the new skills, and pressure to do it with fewer people. _Why it matters:_ Treat prioritization as the core competency, not a side task. Running and transforming the business at once with fixed headcount is, in Tessa's words, quite literally impossible without help. _For:_ RevOps Leaders, Revenue Executives 8. **AI will shrink RevOps — but only after prerequisites are met** — Tessa expects RevOps orgs to shrink over time (enablement, sales-ops business partners, PMOs, basic analytics, some sysadmin ratios). But headcount can't fall on day one. Reductions require rigorous operations, heavy standardization, and strong data architecture first — because agents can't fix a process where every seller operates differently, and can't reason over data that isn't structured. _Why it matters:_ Invest in standardization and data architecture before you bank on AI-driven headcount savings. The org that automates on top of chaos gets chaos faster. _For:_ Revenue Executives, RevOps Leaders, Founders 9. **AI isn't coming for your job today — but complacency is** — Tessa's message to her own team: I can't replace you with a bunch of agents today. But if you don't take personal responsibility to become AI-fluent and understand how AI reshapes your role, the company will eventually look for people who think that way. Learning to work yourself partway out of the old job is how you elevate into a bigger one. _Why it matters:_ Frame AI fluency as job security, not job threat. The operators who lean in early get more opportunity; the ones who wait get selected out. _For:_ RevOps Leaders, Revenue Executives 10. **Ruthlessly prioritize — three initiatives at A+ beat ten at C-** — AI lets an operator move faster, but the field's ability to absorb change is the real constraint. Forcing ten changes at once often means none get adopted. Delivering three things that genuinely move the needle with strong change management beats a pile of half-baked initiatives — the answer isn't 10x the output, it's do the work on your plate meaningfully better. _Why it matters:_ Use your AI speed to raise quality and ROI per initiative, not raw volume. Account for level of effort and change absorption, not just how fast you can build. _For:_ RevOps Leaders, Revenue Executives 11. **Work analog-first to avoid AI slop** — AI doesn't have great judgment. The highest-quality workflow starts in analog mode: write your own hypothesis, plan, and creative thinking manually, then use AI to find examples, metaphors, and crunch data you couldn't. Skipping the human hypothesis produces slop you can spot instantly — the moment someone asks a question, there's no foundation to stand on. _Why it matters:_ Adopt a hypothesis-driven approach: judgment first, AI as the amplifier. AI-slopping a deliverable is a career-limiting move the second it's interrogated. _For:_ RevOps Leaders, Revenue Executives, Founders 12. **Own your AI upskilling — don't wait for your employer** — Believing it's your organization's job to upskill you is the worst way to think about the moment. It's your responsibility to educate yourself and get hands-on. Tessa's zero-to-five AI maturity curve gives operators a way to place themselves and a concrete self-audit to climb it — starting with a simple spreadsheet of their own recurring work. _Why it matters:_ Take the initiative yourself, then bring your findings to your manager. In this window, a few hours of self-driven exploration puts you far ahead of peers who say they don't have time. _For:_ RevOps Leaders 13. **Do the next job before you have it** — Tessa's career through-line — EA at Salesforce to Senior Director at Tableau to VP RevOps at ZoomInfo to founder — ran on acting a level above her title: playing chief of staff as an EA, fixing broken comp plans, owning the sales operating rhythm no one else was running. You don't get promoted to the next level unless you're already doing that job. _Why it matters:_ Stop waiting to be told what to do. Deliver value beyond your scope now; the promotion becomes the obvious formality. _For:_ RevOps Leaders, Revenue Executives 14. **The market gap is the senior operator who can also execute** — There are plenty of technical fractional RevOps shops and plenty of big consultancies with a roadmap. What's scarce is a person who has operated in both large enterprise and startups, is both strategic and hands-on, and can navigate the political complexity of a large enterprise while actually executing the transformation. The rate of change in GTM tech makes an outside partner nearly mandatory. _Why it matters:_ When choosing a transformation partner, weight lived operator experience plus execution ability over pure technical implementation or slide-deck consulting. _For:_ Revenue Executives, Founders ## Frameworks ### The Why / What / How Framework (03:25) **Definition:** A three-layer split of GTM ownership: executives (CEO, CRO) own the WHY (market, category, how we win); the VP of RevOps owns the WHAT (processes, business and operating strategy, scalable design); GTM engineers own the HOW (enrichment, automation, ICP plumbing, execution). Tessa and Anthony use this to explain why AI-first GTM engineers don't replace strategic RevOps. Engineers are excellent at the how, but collapsing the strategic 'what' into the technical 'how' is exactly where over-engineered, non-scalable infrastructure gets built. ### The RevOps Talent Bifurcation (06:38) **Definition:** The RevOps role is splitting from a generalist (decent at business and systems admin) into two lanes: the deeply technical GTM-engineering lane, and the strategic decision-maker accountable for the GTM infrastructure overall. Historically operators were more technical at small companies and more strategic at large ones. Now even strategic enterprise operators must become AI-literate — the emerging gap is enterprise-scale operators who are highly operational but not technical, in a world that demands both. ### People, Process, Technology (the Core Threes) (11:00) **Definition:** Effective AI transformation must change all three legs at once — people (how teams work and are structured), process (re-architected end-to-end), and technology (AI-first infrastructure and data) — not just automate external workflows. If you AI-transform the go-to-market's processes but don't change how the operational teams operate, think, and are organized, the change isn't supported and defaults back to what it was. Transformation has to happen at every point across people, process, and tech. ### Run the Business vs. Transform the Business (13:24) **Definition:** The central tension for a RevOps leader: keep running the non-stop operating machine (forecasting, pipeline, QBRs, territory and account planning, comp) while simultaneously leading an AI-first transformation — usually with the same headcount and a mandate to use fewer people. The headcount that can run the business often lacks the skills to transform it, and the priorities never pause. Doing both alone, Tessa argues, is 'quite literally impossible' — which is precisely where an outside partner creates leverage. ### The AI Maturity Curve (0 to 5) (25:24) **Definition:** Tessa's methodology scores an operator's or org's AI adoption from 0 to 5 — where 0 or 1 is basic use (asking questions, rewriting an email) and higher levels reach standardized workflows and autonomous agents. The curve gives operators a way to locate themselves honestly and a direction to climb. Most enterprise operators sit far left; moving right requires hands-on practice, standardized processes, and clean data architecture as the foundation for agents. ### The Eisenhower Matrix for Operator Prioritization (20:43) **Definition:** Sort work by urgency and importance: do the highly-important-and-urgent first, delegate the urgent-but-low-importance, and protect time for the highly-important-but-not-urgent — always weighing level of effort per initiative. Anthony's favorite prioritization tool. The highest-leverage quadrant is the important work nobody is banging on your door about — where the best operators move the needle most — but level of effort must gate what makes the top of the list. ### Analog-First, Hypothesis-Driven AI Workflow (23:33) **Definition:** Start in 'analog mode' — write your own thoughts, plan, and hypothesis manually using your own judgment — then use AI to find examples, metaphors, and crunch data to back it up and level it up. Because AI lacks great judgment, leading with your own POV produces the highest-quality output and avoids 'AI slop' — deliverables that collapse the instant someone asks a question because there's no underlying foundation. ### The AI Self-Audit Exercise (26:06) **Definition:** A tactical first step for any operator: open a Google Sheet, list the core tasks you do daily, weekly, monthly, and quarterly, mark the level of effort and whether each is manual or automated, then map where AI or an agent could help — and how peers in your role are doing it. The exercise turns 'I should use AI' into a concrete plan you can bring to your manager. A few hours of it puts a junior or mid-level operator far ahead of peers who claim they don't have time. ## Quotes _Speakers inferred from an undiarized transcript — verify before attributing._ > "She's come out the other side with a thesis the rest of the market isn't quite ready for: that go-to-market engineers can't replace the experienced RevOps strategists, no matter what the board deck says." > > — Anthony Enrico, The LeanScale Podcast Ep. 85 (00:47) > "The layer that's missing from that is that RevOps person who can take that high-level strategy and translate it down to the tactical." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (02:45) > "The go-to-market engineering layer is extremely capable of handling the how. What I think gets really missed is the what." > > — Anthony Enrico, The LeanScale Podcast Ep. 85 (03:25) > "I was having that conversation with a go-to-market engineer, and at one point he asked me what ARR was." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (05:17) > "I have seen time and time again over-engineered GTM infrastructures. At a certain point, you're just playing business and you're not doing what you really need to be doing at that stage." > > — Anthony Enrico, The LeanScale Podcast Ep. 85 (05:58) > "You can't layer AI and expect anything to change on your existing processes, your existing data structures. You need to think about it from a re-architecture perspective, end-to-end." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (11:00) > "The single hardest thing for any operator that's running inside an organization is prioritization." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (12:18) > "How do I run the business while transforming the business while all the priorities are gonna stay the same? That is what people are up against right now." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (13:24) > "What the board or the LT is asking them to do by themselves, with everything they already have to deliver, is quite literally impossible." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (13:59) > "In order for there to be a reduction in headcount, there has to be rigorous operations, a lot of standardization, and incredible data architecture. It can't just replace people." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (16:49) > "AI is not coming for your jobs today. I'm not gonna replace you with a bunch of agents. But if you don't take the responsibility to learn AI and become AI fluent, we're gonna start having to look for people who will think that way." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (17:35) > "The area where people tend to have a tough time focusing is where it's highly important, but nobody's banging on your door to get it done. That's where the best work happens." > > — Anthony Enrico, The LeanScale Podcast Ep. 85 (21:24) > "Delivering three things really meaningfully that are going to move the needle and have a high ROI is still more important than delivering 10 things that may or may not be adopted." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (20:43) > "AI doesn't have great judgment. Start in analog mode — write your thoughts out manually, put your plan out manually — and then use it as a tool to layer in. That's gonna give you the highest quality results." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (23:33) > "You can tell when somebody puts together a presentation that they didn't really do — they just AI-slopped it together — because the second you start asking questions, they don't have any foundation to stand on." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (24:12) > "I'm going to do the next job that I want before I get it, and I'm gonna act the next level before I am — because I'm not gonna get there if I'm not already doing that job." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (32:21) > "Really, as an operator, the only time you get to be truly 100% honest is in your exit interview. That's the job." > > — Tessa Whittaker, The LeanScale Podcast Ep. 85 (39:26) ## Practical advice by role ### RevOps Leaders - Own the 'what' explicitly: be the person accountable for translating executive strategy into processes, business/operating design, and architecture built for a 100-rep future — then hand the 'how' to a technical team. - Ruthlessly prioritize. Deliver three needle-moving initiatives at A+ with strong change management rather than ten at C-minus the field can't absorb; weigh level of effort per initiative, not just build speed. - Work analog-first: write your own hypothesis and judgment down manually, then use AI to find examples, crunch data, and level it up. Never AI-slop a deliverable you'll have to defend. - Own your AI upskilling. Open a Google Sheet, catalog your recurring tasks by cadence and level of effort, mark manual vs. automated, map where agents fit, see how peers do it, then bring it to your manager. - Act the next level before you have the title. Deliver value beyond your scope now so the promotion becomes an obvious formality. ### Founders - Don't assume a cheap GTM engineer replaces a seasoned RevOps leader — you'll get fast execution without the strategic judgment, and it breaks at scale (the engineer who asks what ARR is). - Resist over-engineering. AI makes fancy bells and whistles trivial to build, but over-customization becomes bottlenecks; bias toward standardization and stage-fit design. - If you buy an AI transformation, weight lived enterprise-and-startup operator experience plus real execution ability over pure technical implementation or slide-deck consulting. ### Revenue Executives - Reset board expectations: AI won't shrink RevOps on day one. Reductions require rigorous operations, standardization, and strong data architecture first — invest there before banking savings. - Scope transformation across people, process, and technology together. Automating external workflows without re-architecting the ops org lets everything default back to how it was. - Protect the 'run the business' machine while transforming it — recognize that doing both with fixed headcount is impractical without a dedicated partner or added capacity. - Make AI fluency a stated expectation for the ops org; frame it as elevation, not threat, so top performers lean in instead of freezing. ## AI takeaways **Thesis:** AI plus a couple of GTM engineers does not replace the strategic RevOps layer — it can only execute its judgment. AI is a leverage tool that amplifies senior operators, but it demands standardized process, clean data architecture, and human judgment before it can safely reduce headcount or produce anything better than slop. - **Engineers do the how, not the what** — GTM engineers are excellent at execution — enrichment, automation, ICP plumbing — but can't manufacture the business strategy and scalable design a senior operator provides. The tell: an engineer who can build the workflow but asks what ARR is. - **Headcount cuts have prerequisites** — RevOps will shrink over time (enablement, sales-ops partners, PMOs, basic analytics), but only after rigorous operations, heavy standardization, and strong data architecture — agents can't fix processes where every seller works differently or reason over unstructured data. - **AI lifts your ceiling, not just your speed** — Anthony's experience with Claude Code: he works more, not less, because things that were once too costly to attempt are now doable — AI raises the ceiling of possible output and creates work the team didn't think was possible. - **Change absorption is the real limit** — Even if an operator can do 20 things faster, the field can't absorb that pace. Ruthless prioritization — three initiatives at A+ over ten at C- — beats raw volume; the answer isn't 10x output, it's higher-quality output. - **Analog-first beats AI slop** — AI lacks judgment. Write your own hypothesis and plan manually, then let AI find examples and crunch data. AI-slopped work collapses the instant it's questioned — a career-limiting move. - **Upskilling is on you** — It's the operator's own responsibility — not the employer's — to become AI-fluent. A few hours of a structured self-audit puts a junior or mid-level operator far ahead of peers who say they lack time. **Agent & automation ideas** - A pipeline/forecast 'chief of staff' agent that sits at the right hand of a sales leader at scale — taking centralized data sets, customizing them per leader, and running the reviews and handoffs that sales-ops business partners do manually today. - Just-in-time enablement systems that generate and deliver training content on demand, shrinking the always-on enablement content and delivery burden. - A self-audit agent that ingests an operator's catalog of recurring tasks (cadence, level of effort, manual vs. automated) and proposes where AI or agents can take over, ranked by time saved. - An AI-first re-architecture assistant that maps lead-to-opportunity, opportunity-to-contract, and quote-to-cash and flags where existing process or data structure blocks automation. ## Operations takeaways ### Revenue operations - **Own the 'what'.** The durable RevOps job is translating executive strategy into processes, operating design, and scalable architecture — the layer between the why (executives) and the how (GTM engineers). - **Pick a lane.** As the role bifurcates into technical GTM-engineering and strategic decision-making, choose deliberately — and at enterprise scale, become AI-literate regardless of lane. - **Prioritization is the craft.** Running the business (forecast, pipeline, QBRs, planning, comp) never stops; transforming it is layered on top with the same headcount. Prioritization, not raw effort, is what makes it possible. - **Standardize before you automate.** Agents can't fix a process where every seller operates differently. Rigorous operations, standardization, and data architecture are prerequisites to any AI-driven headcount reduction. - **Don't over-engineer.** AI makes over-customization easy and cheap, and it becomes tomorrow's bottleneck. Design for standardization and the 100-rep future, not maximal bells and whistles. - **Take initiative now.** This is a rare window for RevOps to lead the AI transformation. Self-starters who audit their own work and bring a plan will pull decisively ahead of peers who wait. ## Metrics mentioned | Value | Metric | Context | | --- | --- | --- | | 500-person ops org | Enterprise AI transformation mandate | Tessa's Fortune 500 contract: lead an AI transformation across a 500-person operations organization. | | ~10 years, joined at 24 | Salesforce tenure | From executive assistant in San Francisco to Senior Director of Strategy & Operations for Tableau's Global Enterprise business. | | 0–5 scale | AI maturity curve | Tessa's methodology scores AI adoption from 0 (basic prompts) to 5 (autonomous agents); most enterprise operators sit far left. | | 5 reps → 100 reps | Design horizon | Architect processes for the 100-rep future, not the 5-rep present; over-customization forces expensive rebuilds in hyper-growth. | | 3 at A+ > 10 at C- | Prioritization payoff | Deliver three needle-moving initiatives excellently rather than ten mediocre ones the field can't absorb. | | 90 → ~100 days | Departure notice | Asked to stay an extended period when she gave notice to found her agency — a signal of her standing as an operator. | ## Entities mentioned - **Tableau** (company) — Tessa was Senior Director of Strategy & Operations for Tableau's Global Enterprise business (a Salesforce company) — her enterprise-scale operator crucible. · https://leanscale-knowledge-hub.netlify.app/company/tableau/ - **ZoomInfo** (company) — Tessa was VP of Revenue Operations at ZoomInfo, running the ops org that drove the company's move up-market. · https://leanscale-knowledge-hub.netlify.app/company/zoominfo/ - **Pavilion** (company) — Tessa is a Pavilion Miami Co-Chapter Head and was named a Pavilion RevOps Leader to Watch. · https://leanscale-knowledge-hub.netlify.app/company/pavilion/ - **GTMfund** (company) — Tessa is a GTMfund LP, referenced in her intro credentials. · https://leanscale-knowledge-hub.netlify.app/company/gtmfund/ - **Tessa Whittaker** (person, guest) — Founder of a senior-operator RevOps agency; ex-VP RevOps at ZoomInfo and Senior Director at Salesforce/Tableau, leading enterprise AI transformation. · https://leanscale-knowledge-hub.netlify.app/guest/tessa-whittaker/ - **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/ - **Salesforce** (tool, CRM) — Where Tessa spent nearly a decade, rising from executive assistant to Senior Director for Tableau's Global Enterprise; also the archetypal CRM/system-of-record RevOps must architect for scale rather than over-customize. - **Claude Code** (tool, AI Dev Tool) — Referenced as 'cloud code' and 'Claude' — the agentic AI tooling LeanScale adopted for itself and customers, and which Tessa urges operators to get hands-on with to climb the AI maturity curve. - **Google Sheets** (tool, Productivity / Spreadsheet) — Tessa's tactical starting point: open a Google Sheet and catalog every recurring task by cadence and level of effort, then map where AI and agents can help. ## FAQ **Q: Can AI and GTM engineers replace a RevOps team?** A: No. Tessa Whittaker argues GTM engineers can execute the strategic layer's judgment but can't create it. AI-first engineers excel at the 'how' — enrichment, automation, ICP plumbing — but you still need a senior operator to own the 'what': the processes, business strategy, and scalable design. You may need fewer requirement-gatherers, but the strategic architect becomes more important, not less. **Q: What is the why / what / how framework in RevOps?** A: It's a three-layer split of go-to-market ownership. Executives (CEO, CRO) own the WHY — market, category, and how the company wins. The VP of RevOps owns the WHAT — processes, business and operating strategy, and architecture built for scale. GTM engineers own the HOW — the technical execution. Teams break when they collapse the strategic 'what' into the technical 'how' and skip the operator layer. **Q: Will AI actually reduce RevOps headcount?** A: Over time, yes — Tessa expects areas like enablement, sales-ops business partners, PMOs, and basic analytics to shrink. But not on day one. Headcount reductions require rigorous operations, heavy standardization, and strong data architecture first, because agents can't fix a process where every seller works differently or reason over data that isn't structured. AI can't reduce headcount by simply being introduced. **Q: What has to be true before AI can automate ops work?** A: Three things: standardized processes (so an agent has a consistent workflow to operate on), clean and well-structured data architecture (so AI can access reliable data and reporting), and operational rigor across the org. Without those, layering AI onto existing processes changes nothing — it defaults back to how the work was done before. Transformation must hit people, process, and technology together. **Q: What is 'AI slop' and how do you avoid it?** A: AI slop is low-quality work produced by handing a task straight to AI without your own thinking — it collapses the moment someone asks a question because there's no underlying foundation. Avoid it by working analog-first: write your own hypothesis, plan, and judgment manually, then use AI to find examples, add data, and level it up. Judgment first, AI as amplifier, produces the highest-quality result. **Q: How should a junior or mid-level RevOps person start using AI?** A: Own your upskilling — don't wait for your employer. Open a Google Sheet and list your recurring tasks by cadence (daily, weekly, monthly, quarterly), noting level of effort and whether each is manual or automated. Map where AI or an agent could help, look at how peers in your role do it, and bring the plan to your manager. A few hours of this puts you far ahead of peers who claim they don't have time. **Q: Why does over-engineered GTM infrastructure hurt startups?** A: Because AI makes it cheap and fast to build fancy, over-customized systems that aren't stage-fit — what Anthony calls 'playing business.' Over-customization becomes bottlenecks and breakage as you grow, forcing expensive rebuilds during hyper-growth. The fix is to standardize and simplify, and to architect for the 100-rep future rather than maximizing bells and whistles for the 5-rep present. ## Timeline - **00:00** — Cold open + intro - **02:00** — The contrarian thesis: why GTM engineers can't replace RevOps - **04:25** — The why / what / how framework - **07:30** — How RevOps talent is bifurcating: strategic vs. technical lanes - **11:20** — The real bottleneck behind enterprise AI transformation - **13:50** — Running the business while transforming the business - **16:00** — Will AI actually reduce RevOps headcount? - **22:00** — Tessa's pushback: ruthlessly prioritize, don't just do more - **26:20** — How to use AI without producing AI slop - **28:10** — Practical advice for RevOps practitioners - **33:15** — EA to founder: the career through-line - **38:00** — The agency thesis and the market gap ## Related episodes - **Ep. 95: Why AI Means More RevOps Hires, Not Fewer** (Jimmy O'Halloran, New Relic) — The sibling counter-argument: AI is a productivity multiplier that should grow the operating layer, not gut it — the flip side of Tessa's 'engineers can't replace strategists.' · https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/ - **Ep. 88: Why AI Won't Close Your Biggest Deals** (Michael Kiernan, Nextdoor) — A CRO's take on the practical limits of AI in enterprise selling — pairs with Tessa's signal-vs-slop, human-judgment view. · https://leanscale-knowledge-hub.netlify.app/podcast/michael-kiernan-nextdoor-ai-wont-close-deals/ - **Ep. 92: Outbound AI Agents** (Mica, Ample Market) — Where AI agents genuinely take over GTM work — a concrete look at the 'how' layer Tessa says engineers and agents excel at. · https://leanscale-knowledge-hub.netlify.app/podcast/mica-ample-market-outbound-agents/ - **Ep. 89: From CEO to CRO** (Alex Wakefield, AcuityMD) — A leadership career-arc conversation that rhymes with Tessa's EA-to-founder through-line and 'do the next job before you have it.' · https://leanscale-knowledge-hub.netlify.app/podcast/alex-wakefield-acuitymd-ceo-to-cro/ - **Ep. 86: The Best CROs Don't Come From Sales** (Jerry Brooner) — On non-linear GTM leadership paths and the operator's edge — a companion to Tessa's career and strategic-operator thesis. · https://leanscale-knowledge-hub.netlify.app/podcast/jerry-brooner-best-cros-dont-come-from-sales/ ## Full transcript _Machine-transcribed and not diarized; speaker attribution is inferred._ _Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/transcript.md_ ### 00:00 — Cold open + intro **[0:00]** (logo whooshes) Joining me today is Tessa Whittaker, founder of a brand new senior operator RevOps agency that is already on contract with a Fortune 500 company to lead an AI transformation of their 500 person operations organization. A UCLA grad who pivoted from legal recruiting into tech, Tessa spent nearly a decade at Salesforce, moving from executive assistant in San Francisco to senior director of strategy and operations for Tableau's global enterprise. And then went to Zoom info as vice president of revenue operations, where she ran the ops org driving the company's move up market. She is a pavilion Miami co-chapter head, a GTM fund LP, **[0:47]** and was named a pavilion RevOps leader to watch. One of the few operators who has lived inside the enterprise transition at two iconic GTM companies. She has come out the other side with a thesis the rest of the market is not quite ready for. That go-to-market engineers can't replace the experienced RevOps strategists, no matter what the board deck says. In this conversation, we get into the contrarian view, the career arc that took her from EA to founder, and the very honest reality of what it feels like to go from operator to entrepreneur. - Amazing, thank you, Anthony, so much for having me and thank you for the great intro. **[1:29]** - Thank you so much for being here. So excited to dive in. You have a vast wealth of experience, really excited to go through it. And I think one of the most interesting things you said in our pre-call prep that there is a perception in executive leadership that AI plus go-to-market engineers replaces the historical RevOps team. Where do you see that thesis breaking down in actual practice? - Such a great question. And I think you hear that theme coming up so much that the future of RevOps is going to continue to change, where you won't have as many RevOps people and instead you'll have these go-to-market engineers ### 02:00 — The contrarian thesis: why GTM engineers can't replace RevOps **[2:06]** that are super AI first, building out these AI workflows, and you won't need these traditional RevOps people that you had before. And I think you even hear that or see that theme coming up in startups, smaller companies, that instead of going out and saying, oh, we need to go hire this expensive RevOps leader, we're just going to hire go-to-market engineers that can architect our end-to-end workflows using AI and build really fast. And that'll be great and that'll suffice. And we don't need to spend money on that, maybe more seasoned or experienced operator. I think that, I don't know if you've seen that from your side, but I think that, in my opinion, **[2:45]** that the layer that is missing from that is obviously that that RevOps person that can take that high-level strategy and then translate it down to the tactical and that maybe you don't need as many RevOps people that you had before to go gather the requirements, build the user stories, translate that into technical requirements and make those changes themselves. Maybe many of those people turn into a few of those people and your teams are looking a lot smaller going forward, but having someone who really understands that business strategy and translation and can architect that end-to-end, I think is incredibly important. - I fully agree. **[3:25]** And the way I like to think about it, there's the why, the what, and the how. And I think the go-to-market engineering layer is extremely capable of handling the how. When you tell them, hey, I need to get this ICP, I need to enrich these records, I need to flow this automation, you want a highly technical team that can handle the how. What I think gets really missed though is the what. So the CRO, the CEO, they're gonna set the why. Why are we in a particular market? Why are we setting up our business this way? What category are we competing in and how are we gonna win? That's all on that executive level. I think that VP of RevOps role though, **[4:06]** translates all that the way you said into the what. What are the processes? What's the business strategy? What's the operation strategy? What's the design in the what? And do I have a GTM engineering team that I can hand that off to turn into reality? - 100%. I don't know if you've ever gathered requirements from non-technical leadership or sat in a meeting with your CRO or COO or CEO and been, why can't we just do it this way? And there's so many implications to how you're designing and how you do it to be built for scale, that someone that is just really good at that execution layer, they're not necessarily going to be able to catch. ### 04:25 — The why / what / how framework **[4:46]** And I was actually working with an AI startup that had to go to market engineer and I was just doing a few hours a month where I was coming in and they were architecting their lead to opportunity, their opportunity to contract and going through like why we had to build all the different implications and why things needed to be built the way that they were gonna be built and how you wanna think through, okay, how does this work? Not just for a sales team of five people, but for a sales team of a hundred people down the line and how thinking through scale and not over customization can help when you think about hyper growth **[5:17]** and not having to go back and redo everything down the line. And I remember in that same conversation having and having that conversation with a go to market engineer, at one point he asked me what ARR was. And so I think that there is this gap where companies think, okay, again, I'm gonna go out and just get an inexpensive, really good AI engineer and then I don't need to have the strategic layer. And I think that will end up breaking things down the line. And I think the other thing is that there is the folks that have been there or done that before understand, I think that there's an opportunity to continue to standardize and simplify for scale **[5:58]** and that over customization or building out all of these fancy bells and whistles for lack of a better word that you can through AI very quickly can actually just end up being a lot of bottlenecks in your processes or things that are gonna break down the line. - I have seen time and time again, over engineered GTM infrastructures. It's not stage fit at a certain point, you're just playing business and you're not doing what you really need to be doing at that stage. And of course, like invest in processes that can scale, but sometimes it is a bit over engineered. And one, I'm curious your take on, I feel five years ago, 10 years ago, **[6:38]** RevOps tended to be a group of generalists. They were pretty good at the business side, business strategy, pretty good at the systems, but a lot of the tools were more like being a systems admin, not necessarily needing to bring to the engineer level. And I see this bifurcation of, you have to start to pick a lane. Are you gonna be the very technical RevOps person and go down that GTM engineering lane? Or do you wanna be the strategic person who's really on the hook for decision-making of the GTM infrastructure overall? - Yeah, it's funny. There has been such a transition in the role. I think it goes back to the taking a thousand foot view. **[7:22]** If you're a RevOps person at a startup versus a mid-sized company versus a large enterprise, you're obviously a different profile altogether, right? So if you go up into the enterprise, the folks that are in operations are very strategic, very operational, very programmatic, are very good at communication and getting buy-in and leading transformation projects at scale, but they actually don't know how to do the work, right? Like they don't actually know how to be very technical. There's completely separate siloed teams that are technical for them. And then you have, I'd say mid-market or even down to startups, SMB, ### 07:30 — How RevOps talent is bifurcating: strategic vs. technical lanes **[7:58]** your RevOps person was always more of a generalist where they had to be both operational and then both really technical. And so I do think some of that still is similar in the sense that people, I would say historically it was very much like you're much more technical at a smaller company. And then as that company gets bigger, you can be an operator who's not as technical. I think now what I'm starting to see is that it's really hard if you're in the large or the high end of the mid-market, or let's say in the large enterprise where you have entire organizations that are incredibly operational, but they're not technical. **[8:34]** But in order for them to completely transform and to keep up and to continue to scale in the age of AI, they need to be AI literate and need to understand AI workflows. And they need to understand how not only do they change the way that they're working, but what they need to do from an AI perspective to re-architect everything that they're doing. And so I think the gap that's emerging is, okay, before you're in the enterprise, you don't need to be that technical at all 'cause you hand that off, to being like actually to be an effective operations person, you can no longer not be technical or at very minimum understand AI and AI implications **[9:13]** and what AI workflows look like, because it's incredibly hard to do your job if you don't have that knowledge or understand it. And so you have all these large enterprises now that have hundreds and hundreds of operators, and this is across the board, who they aren't necessarily AI literate, but they need to be. And they don't have the opportunity at that size or scale to go, okay, we need to go ahead and replace all their talent because even if they wanted to, we're not gonna go out and find people that have large enterprise experience that also have that AI experience that you're looking for, but basic AI enablement isn't necessarily gonna change **[9:48]** or transform your entire organization either. So what are they gonna do? - Yeah, and I really feel like this is a moment for RevOps to really capture because it's the team and function positioned so well to lead the AI transformation. I'm curious, you've seen this at an enterprise level, you're helping companies through AI transformation right now. What is this process like? What are people trying to accomplish? What tends to block them? And what are the real opportunities that people are missing right now? - Yeah, we think at the end of the day, the most simplest thing that needs to happen is if you think about ops organizations **[10:25]** serve their stakeholders, they're serving the field, they're serving the go-to-market. And what needs to happen in the go-to-market right now is re-architecture from all of their processes. Basically, how do we re-architect from an AI first lens? So how do we look at our lead to opportunity to contract, quote to cash, all of our handoffs or end-to-end customer journey and make sure that we're thinking about this in an AI first way. And operational teams need to not only start to think through, okay, what are all the things that we can be doing across the board and not just point and click solutions, because you can't layer AI and expect anything to change **[11:00]** on your existing processes, your existing data structures. You need to think about that from a re-architecture perspective end-to-end. But these ops teams are looking at that, and that's what they need to deliver. And so that isn't just, okay, we're gonna run a project, a sales transformation project, and we're gonna document every single process and then figure out what needs to change. Everything needs to change across people, process and technology to go back to the simple core threes of how do we transform how we're operating to be able to accelerate down the AI maturity curve and serve the field while we do it. ### 11:20 — The real bottleneck behind enterprise AI transformation **[11:38]** Because if you go out and you do an AI transformation of processes and workflows in the go-to-market and you're not actually changing how your operational teams are operating or thinking or working or re-architecting, then anything you do externally won't be supported consistently to change. It'll just default back to what it was before. And so the AI transformation doesn't need to just happen in the actual architecture. It has to happen in every point across the people, process and tech. - Where do you think the biggest bottleneck is right now? Because I know a lot of teams, they have the desire to transform their GTM organization, **[12:18]** to implement AI in a meaningful way. What tends to be the biggest blocker? - My gosh, I would say it's this would be my answer to any question that anyone has ever asked me about RevOps ever. They're like, what's the biggest blocker to do this thing? I think the single hardest thing for any operator that is running inside an organization is prioritization. Meaning, all right, you've got your set amount of headcount and you have all these things that you need to do to run the business that cannot stop. How you're forecasting, how you're measuring the pipeline, how you're reporting to your board, **[12:52]** how you're running QBRs, how are you doing territory planning, how you're doing account planning, making sure people that are getting paid, all of those things, right? That is never gonna stop. So you have everything that you need to deliver and then you have set headcount that are focused on delivering those things. And the headcount that you have can run the business and work on all of those things, but maybe don't necessarily have different skill sets that you would need them to transform. And then you're getting this mandate down that you're saying, okay, on top of everything that you're doing, you should be doing it with less people because AI, **[13:24]** why do you need as many people as before? And you need to run this transformational project where you're thinking about how you're transforming the go-to-market AI first at the same time as you're running the business. So how does a RevOps leader navigate with their current headcount that probably don't necessarily have the skills that they have? How do I run the business while transforming the business while all the priorities are gonna stay the same? And I think that is what people are up against right now and why there is a big opportunity for people like you and I, for companies like yours and mine, to say, okay, actually, how do we go in ### 13:50 — Running the business while transforming the business **[13:59]** and help these organizations? Because what the board or what the LT is asking them to do by themselves with everything they already have to deliver is quite literally impossible. Yeah, and I think expectations, what they expect this transformation to bring. And one of the things you brought up, I'm curious if we could double-click on it. When you implement this, are you finding that you need less people after an AI implementation? That's a great question. I think that ultimately, revenue operations organizations will shrink. Now, that depends obviously on your size to begin with and what is in your remit or what you're focusing on. **[14:39]** I think if you're an organization, for example, I'm gonna get some slack for this, but if you have enablement, for example, I think that enablement is an organization that will shrink over time. I think there'll be just-in-time enablement. You can build through systems. You're gonna be able to have less enablement with people that are creating content or delivering content or needing to be in trainings, for example. I think it's a natural area that you'll see shrink. I think your traditional sales operations, business partners, folks that are sitting at the right hand of sales that are acting like a pipeline forecasting chief of staff, **[15:19]** like person who is taking the data sets that are maybe coming out of centralized team and customizing them and helping those leaders. I think you won't need as many because a lot of those things can be automated through agents to help what they sit at the right hand of a sales leader at scale. Of course, they don't go away entirely, but I don't think you need as many of those. And I would say that across the board. I think that the folks that are, I'd say just purely system admins, I think you still need system admins, but I think you don't need the same ratios as you needed before. And ultimately, maybe some of those folks that are doing more manual tasks ### 16:00 — Will AI actually reduce RevOps headcount? **[16:03]** or organizational jobs or have skills like PMOs, for example, or maybe folks that are doing more basic analytics, et cetera. I think those ultimately over time, that those roles be less than more. But at the same time, to get to that point, it's not just, okay, we're gonna bring AI into our organization and all of a sudden, you can reduce head count. Because there's so much from an infrastructure perspective that you need in place in order to have the AI to work efficiently or even autonomously to get there. So for example, if you don't have good data architecture and you're not able to layer on AI that can give you access to data and reporting **[16:49]** or agents that makes that really a lot easier, then you're not gonna be able to get there. So actually, investment needs to be made into the data architecture in order to get to a point where AI can start reducing head count. Another example would be you need to have incredible operational rigor around what your processes are and standardization in order to be able to layer agents on that are doing some of the things that ops people do that are due to customization. So sure, you could have an agent that can help with forecasting and pipeline management and those cadences and those reviews and those handoffs. But if every single salesperson or leader **[17:35]** in your organization operates differently, then AI can't come in and fix that. So in order for there to be a reduction in head count, there has to be rigorous operations, a lot of standardization and incredible data architecture in order to get there. It can't just replace people. And it's funny, I used to say this to my last organization all the time, AI is not coming for your jobs today. I'm not gonna be able to replace you with a bunch of agents. But if you don't take the responsibility on yourself in partnership with me, which I'm gonna help you get there to learn AI and to become AI fluent and understand how that is going to help you do your job **[18:13]** and the organization operate better, then we're gonna start having to look for people that will think that way. So if you're listening to this and you're an operator and you're like, oh my God, I'm not gonna have a job tomorrow, that's absolutely not true. It's just really understanding how you and your role would use AI and continue. It's like a way to work yourself out of the job. And it's not that you're gonna have all these agents and all of a sudden you're not gonna have a role and you won't have a place anymore in the organization. The folks that do that are actually going to elevate and I think have more opportunity down the line. **[18:44]** - Yeah, I think that's been an interesting experience I've had maybe over the last six months or so, we really here at LeanScale heavily adopted cloud code. We're implementing it for customers. We're building custom agents, custom workflows. And what I found really interesting is I tend to be working more because there were certain things that I wouldn't even consider doing because I knew the barrier of getting it done would be too high. But now I have access to these tools where I can get it done today if I want to. And I think what a person who really equips themselves and learns how to leverage AI in a meaningful way, **[19:23]** you will see that the ceiling of your possible output just also lifts. So it's not just about getting the work done that's on your plate today. It can create more work that your team probably didn't even think was possible before. - Okay, so one other way to look at that, that's like a slight pushback. And so I'd love to hear how you feel about it. But I think that especially as an operator, the best thing you can do is ruthlessly prioritize. So what are the things that I can focus on that are gonna move the needle the most? And knowing that even if I can do 20 things faster and more, my stakeholders or the field, their ability to change or absorb or shift, **[20:06]** can they match the rate of change or output that I can now do as an operator who's really good at Claude, who can deliver much faster? Because at least from what I've seen is sometimes when you force so much change at once, the field can't even keep up with that, right? And of course this is broader and maybe what you're working on or what you're delivering more of and maybe it's just being able to serve more clients, right? But I think the one thing is just like an operator who's sitting in a business is just because you can move so much faster. Delivering three things really meaningfully that are going to move the needle **[20:43]** and actually make a true business impact and have a high ROI is still more important than delivering 10 things that may or may not be adopted or have good change management that won't change much at all. - I agree and two things to that. One, when you are prioritizing, I like the Eisenhower matrix, that's my favorite prioritization tool. So understanding which quadrant to put things in and putting it against urgency and level of importance, things that are highly important and highly urgent, get those things done first, things that are really urgent, low importance, delegate if you can. And the area where people tend to have a tough time focusing **[21:24]** is where it's highly important, but nobody's banging on your door to get it done. And I think that's where some of the best work happens and where people move the needle the most. Now, when you're prioritizing, you always also have to account for level of effort per initiative. So if there's a high level of effort that may prevent you from getting it done in a certain timeline, you may not put that in your top priority because you may not be able to meaningfully resource against it. So I think it can, I agree with you. I don't think it's push out more. I think it's do the things that are on your plate better. There's maybe an even higher level ### 22:00 — Tessa's pushback: ruthlessly prioritize, don't just do more **[22:05]** to achieve the outcome that you're working on than you could before. So yeah, I don't think you should bombard people with, oh, it's 10X the initiatives. Maybe let's add one more initiative, but instead of doing a C minus job on all these initiatives, let's take it to A plus on everyone. - Yeah, I like that. I think AI has a great ability to help you deliver better work or more polished work or slightly faster than maybe you would have done before. I will say though, that I have tested quite a few times going through and having, let's say taking input like interviews or documentation or my notes or surveys and pulling it in with context and saying, **[22:50]** okay, based on all of these things, what is your output? What is your recommendation? Where do you think the priorities are? And then looking at those compared to what I've come up with, right, from my experience, and then having, of course, the AI back that up with examples and details and why that is true. Those outputs are completely different. And so I still think it's so interesting, and it goes back to even that conversation about the go-to-market engineer, is that AI can take all of those things and help people that are more junior deliver, but it's still not with maybe the judgment or the expertise that come from experience **[23:33]** that you would have if you've been there and done that before as a senior operator. AI doesn't have great judgment, and I think practically, so if anybody's listening to this going through, okay, what's your workflow of leveraging AI? I think what we have found is when you start in, I'll call it analog mode, write your thoughts out manually, put your plan out manually, and then leverage it the way you've mentioned. Have it find the examples, metaphors, data, crunch more data than you could, but you already have your hypothesis. Take the classic hypothesis-driven approach to anything you're doing. Start with your own judgment, **[24:12]** your own creative thinking and ideas, and then use it as a tool to layer in. That's gonna give you the highest quality results. I think you can tell when somebody puts together a presentation that they didn't really do, they just AI slopped it together, because the second you start asking questions, they don't have any foundation to stand up. So I wouldn't recommend doing that. I think that's probably a career-limiting move, but you should be able to take what an output is that you had before, and then just take it to that next level with AI, and let it propel you. - Absolutely, I couldn't agree more. - I'm curious, for those who are sitting in RevOps **[24:47]** with you, I think you need to really take initiative, take advantage of the moment, dive in, and it's not going anywhere. So if you stand still, you're gonna miss it. Practically speaking, what is some, if I'm sitting in RevOps, what's something I should be doing today? Where do I learn, what do I start doing? What tools should I start using? What advice do you have somebody sitting in one of those junior mid-level roles that might miss the AI train? - Great question. I think there's two ways to start and to look at it. I think waiting for your organization to enable you or believing that it's your organization's job **[25:24]** to upskill you and uplevel you and make you an expert, I think that's the absolute worst way to think about it. It is purely yours and your responsibility alone to educate yourself and to understand it. And so the best way to do that obviously is to get hands on. I think if you are on, I always think of the AI maturity curve that I built in my methodology from zero to five. So if you're a zero or a one and you've just done things very basic of maybe asking questions or helping rewrite an email or whatnot, that's as far left or basic as you can be right now. And so it's really just sitting down and I'm gonna get so tactical and simplify. **[26:06]** If I were a senior analyst or a manager and I'm sitting here listening to this, I would open up a Google Sheet and I would write down what are the core things that I do for my job that are repeating, that are daily, weekly, monthly, and quarterly, and what's the level of effort? And is it manual or is it automated? And I would go through and start understanding how I could, one, personally use AI to start doing these things more efficiently. And if something takes you X amount of time, how do I save time doing that? And then I would start thinking, okay, even if I don't know how to do it yet, what could be a way that I could do this ### 26:20 — How to use AI without producing AI slop **[26:51]** with an agent down the line? And then I would go out and say, what are other people doing that have my same job that have these 10 things that they deliver? How are they doing it? What are they using it in the market? And are they building or are they buying it? And so it's just starting with just you through your lens, how could I be operating different with AI with the things that I'm doing? And I guarantee you, if you're a senior analyst or a manager and you go through that exercise and you go to your manager and you go, this is what I've been doing. This is how I'm looking at it. This is how I'm trying to explore these things. **[27:26]** This is how far I was able to get on my own. Is this something or how can I work with you to help me be able to get to a point where we can actually make this much more further down that maturity curve and get to a point where we have some autonomous agents or things that we're using with AI for these things? And can you help me get there? But just taking that first step is so important, right? And then I think what's really interesting about that is then having that really strong perspective or POV of not going, oh my gosh, if I do this and I show I work 50 hours a week, I can do these in 25. Don't limit yourself to that belief. **[28:01]** Then you go, okay, what is the list below that? That if I wasn't spending 50 hours a week working on this other stuff that I think I could do for the business, that would have a big ROI. And not having that mindset and thinking that way and then being hands-on and working with Claude and really starting to understand how these things work, you will already doing that exercise for three or four hours. Be so much further ahead than most operators or junior folks that aren't AI enthusiasts that you will stand out and really have an opportunity for growth within your organization. But if there's just, you can just going, just doing a little bit extra effort ### 28:10 — Practical advice for RevOps practitioners **[28:39]** to show your curiosity, to show the initiative, to show your starting to think about it and start doing it yourself will make you stand out so much more than so many people that are doing nothing and saying they don't have enough time. - I think that's a tale as old as time. The people who take initiative tend to start leading and getting further and further along in their career. And I think this moment, that dynamic between different profiles of professionals is exasperated by AI. The top performers, the people who take initiative, the people who are self-learners, the people who don't have those limiting beliefs and are really looking for opportunities **[29:16]** to give back as much as they can. The gap between their performance and somebody who's not leveraging AI is just widening so much. - 100%. - And I think those are just some core values that should be instilled in any professional. But I think right now it's exponentially making a difference for people. - I think so too. I think it's such an opportunity and I mentor quite a few people and the advice I give them, especially in larger organizations, it's just you have this window to stand out and the future of operations is changing. Stand out, take it. - And I think this is something I really respect about your career arc. **[29:54]** It didn't take AI for you to push through your story and accomplish what you've accomplished. I'm curious if you have the time you spent at Salesforce, the time you spent at Zoom Info, plus launching your own agency, moving into entrepreneurship. All of these are very big, bold, brave moves. Is there a through line with your story that has empowered you to keep making those big moves? - Oh my gosh, that's a great question, a through line. In the early days, and you mentioned it, so I started as an executive assistant at Salesforce. I graduated school, I thought I was gonna go to law school. I ended up working in a law firm **[30:29]** and then got very lucky story for another time that I got a job at Salesforce as an executive assistant. And when I took that job, talk about the absolute, I think really best intro to tech job ever because from day one of being 24 years old or however old I was when I started at Salesforce, I'm sitting in the QBRs, I'm sitting in the executive leadership calls, I'm sitting in forecasts. So I really understood how the business was running and understanding how you need to communicate with executives and beyond all else, just get things done. And I think my ability to just execute and get things done and not getting something done was never an option **[31:10]** has really stayed with me through my career. But every single one of those jobs, and I think I went back going back to my tactical advice of just start, don't wait for someone to tell you what to do, start thinking bigger is what I did from the beginning. And so when I was in EA, I started acting as the chief of staff. And then I remember I was six months in and the comp plans were messed up and I was pulling in the compensation teams and the strategy teams and bringing everyone together trying to solve this problem, which wasn't within the scope of an EA. And then starting to manage the sales calendar between product marketing and enablement and marketing **[31:43]** because there was no program manager at the time that was thinking about this large sales organization and how we run the operating rhythm and make sure that we're driving focus for sales. There's always things that I was doing that were beyond my scope. And so it was never, I'm gonna do a really good job at my job. Of course I was gonna do that, that was a given, but what is beyond and above my job where I can stand out and add value and I'm gonna do that too. And I'm going to do the next job that I want before I get it and I'm gonna act the next level that I am before I am because I'm not gonna get as a manager to senior manager **[32:21]** or senior manager to director if I'm not already doing the job of that next level. And that was always my mindset of how I did things. And I think the second thing that I've always believed is that I don't need to know perfectly everything about the next job that I'm gonna have in order to go for it, for things they say no. And the best thing is that I get the job and I don't know how to do it and I figure it out. And even going into entrepreneurship, I knew I wanted to take the leap. I was in a great position at my last organization, so much so that when I gave notice, I was asked to stay an additional 90 days, **[33:00]** which I think ended up being a hundred days, I don't know. It was a long extended amount of time, but I knew that I wanted to go out and I saw such a huge opportunity from an agency perspective, but I've never been a consultant before. And what's funny is I thought at first, I was like, oh my gosh, how am I gonna show up against these consultants? And then I realized actually that I'm not trying to be the typical consultant. I'm actually gonna pull from my experience and design something that I think is, it would make sense in a gap in the market right now. But it was just always this belief that no matter what, ### 33:15 — EA to founder: the career through-line **[33:32]** I'm gonna work really hard, I'm gonna go above and beyond. And if I don't know how to do something, I'm gonna figure it out. And that I think is the main consistent thread through that time. And here we are now, which is I'm trying to figure things out. And Anthony, it's so funny. I think it was like, I don't know, two weeks ago when we talked and I was just having a day. And it was the first time we were ever on the phone and I was so stressed out. And I was like, I'm just gonna go into all my fears that I'm having right now and be super vulnerable. And it was great. And we connected quite quickly through that, **[34:03]** but it doesn't mean of course that you don't have days like that where it's imposter syndrome and you're really stressed out, but defaulting or falling back on that belief that I know that no matter what happens, I'll figure it out is just, I think what I've built my entire career on top of. - I think that's so powerful having that quiet confidence that keeps pushing you through. And if you're anything like me, you'll have more of those days to come, which I'm certain of. But I think that's really important for people to hear. And the way you phrased it, if you wanna get promoted, you wanna start moving up in your career, **[34:41]** start doing the job before you're asked. And then it's a pretty obvious answer. So when somebody is ready to make the move, then it'll happen. I would love if you could share your thesis on where you think there's a gap in the services market, where you think there's a gap with internal capabilities. And I think it's incredible bringing your experience and know-how and also how up to date you're keeping with all things AI and everything in that ecosystem. What's the thesis and the wedge that you're bringing to the market? - Sure. I think that there are a lot of RevOps agencies that are out there right now. **[35:21]** You see it if you're on LinkedIn or you're talking to anyone, there are so many people that are starting to do the fractional work. I think there's a lot of those firms that I've seen. And of course I haven't seen them all. And I'm sure there's plenty that are outside of that as well. But it's folks that are really good at the technology or have become AI experts or really understand different systems and can do implementations or maybe migrations. And so there is a large pool of that. And I think that they have predominantly worked in maybe startups or smaller tech companies, or maybe in some cases, the smaller side of the mid market. **[36:03]** And then I think there are, there's obviously the big consulting firms as well that can come in and have great consulting experience and run a more formalized project and come in and give you that roadmap, et cetera. But what I'm finding, and there's less of, of course it exists, but less of out there is a person or people that have operated both in large enterprise and in smaller companies who are both very strategic and operational and hands-on that can go in and help from a consulting perspective, larger organizations, try to navigate these transformations. Because there's one thing to come in and say, okay, here are all the things that you need to do **[36:48]** to modernize and optimize and actually transition and re-architect in the age of AI to truly be an AI first organization. But there's a lot of complexities in how you get things done and how things need to be done in the large enterprise. And so I believe in my bet is that by having operators go in to run these more strategic projects that can both say, I understand how large enterprise works, I understand the transformation that you need to go through, I can help navigate the very political environment that most of these large enterprises are sitting in, but I also understand the tactical on how to do this **[37:29]** and how to execute, and I'm an expert within myself, I can help you full circle navigate this, is where I think there's an opportunity in the market right now. - I think there's huge opportunity. And one of the main drivers is the rate of change that we're seeing in go-to-market tech, AI, it's very difficult to be embedded as an operator in one company and be fully up to speed on the entire ecosystem. Because you're gonna do one of these implementations one time, maybe it's one project a year, that's kind of defined that way, where an agency, the nature of the business, you have to be doing that multiple times concurrently, ### 38:00 — The agency thesis and the market gap **[38:13]** running it from multiple organizations, it's your whole business to stay up to speed and keep up with that velocity. And I think it's a very difficult thing for in-house teams to just do on their own, I think they need a partner to walk them through it. - They do, and it's just, even if they wanted to, it's this concept we talked about, which is like transform the business versus run the business, run the business, keeps people so occupied. And so again, I think having someone come in who is focused on that, but has also been there and done that before in a similar job, who can relate and understand, and is not just approaching it **[38:50]** as a broader, larger consulting project, I think that there's gonna be a lot of need for that and the future, and I'm excited to fill that gap. - Yeah, I used to work for a chief strategy officer, and we would hire quite a few consultants to do different types of engagements. And he always said, there's three reasons to bring them on. One, you just don't have enough bandwidth, you need helping hands to get the job done. Two, they have some level of expertise, and you wanna tap into their domain knowledge and take advantage of that. And the third and most common reason is we simply need a third party to tell us what we need to do for political reasons. **[39:26]** And it sounds so much better coming from someone who's completely objective, they're not working for a promotion inside the company or defending a department or anything like that. It's just, these are the market dynamics, this is what's going on, this is what you should do. - Yeah, really as an operator, the only time you get to be truly 100% honest is in your exit interview. That's the job. - That's exactly right. Tessa, I just wanna thank you so much for bringing your experience, your knowledge, your perspective to the conversation. It's so cool to see your career arc through Salesforce, Zoom Info, launching your own agency. **[40:00]** I'm fully aligned with your thesis. I think there's going to be a big wave of companies coming in to meet those needs, just by the sheer velocity of the category and how it's moving. And I really hope anyone who's sitting in a RevOps role, listen to what you shared intently about how to capture the moment, take advantage of this time and leverage it to accelerate their career because there's so much opportunity. And you and I both know there's so many people who are not going to take advantage of it, but this is an incredible window. Get busy with cloud code, start diving into YouTube channels and learning how to do things, network with people, **[40:41]** find what they're doing and what's working and start pushing the limits of what you're doing inside your own organization. So Tessa, thank you so much. I appreciate the time and I can't wait to see what you do with your business and follow your growth. - Amazing, thank you so much for having me and always down to have these conversations on or offline. --- _LeanScale Podcast Knowledge Hub. Free to quote and cite with attribution to The LeanScale Podcast (https://www.leanscale.team)._