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Lonny Sternberg has spent 13+ years building scalable revenue engines, from zero-to-one startups and a NASDAQ listing to VC- and PE-backed companies at $350M+ scale, and now leads RevOps at Veriforce. He joins Janis to lay out his operating thesis: in the age of AI, clarity beats speed, and clarity comes from a loop of governance, context, systems and rhythm. Together they dig into shared definitions, context capture that doesn't depend on reps updating the CRM, and why change management still decides whether any of it sticks. Lonny closes with his unpopular opinion on why RevOps must be in the room before the decision, not after the cleanup. We cover: Why clarity, not focus or time, is the new bottleneck for RevOpsThe loop system: governance, context, systems and rhythmGovernance as shared definitions, not rules: ARR, ACV, SQL-to-SQO conversion and who owns which numberWhy agents and MCP layers either accelerate a thoughtful process or accelerate a disasterWhen everyone becomes a data analyst with Claude and brings their own metrics to the meetingBuilding the context layer: CRM, first- and third-party data and governance combinedAutomating context capture, because reps shouldn't have to update the CRMSurfacing the right context for the right person: call prep, live calls and objection handlingMeeting ICs where they already work: Slack, Teams, Zoom and emailWhy MEDDIC-style methodologies fail when they feel like extra data entry for repsChange management and enablement that empowers instead of disciplinesRhythm and cadence: backing into the end state instead of ticking weekly checkboxesUnpopular opinion: RevOps belongs in the decision early, not in the cleanup afterwardsLonny Sternberg: https://www.linkedin.com/in/lonnysternberg/ Veriforce: https://veriforce.com/ Janis Zech: https://www.linkedin.com/in/janiszech/ Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Chapters: (00:00:00) Intro (00:01:41) Lonny Sternberg's path through RevOps (00:04:54) Clarity and the loop system (00:06:01) Governance, context, systems, rhythm (00:09:39) Shared definitions and metrics (00:11:30) Agents accelerate clarity or chaos (00:14:48) Building the context layer (00:18:00) Surfacing context by role (00:21:38) Meeting reps where they work (00:23:10) The MEDDIC data-entry trap (00:24:30) Change management and enablement (00:26:03) Rhythm and cadence (00:29:56) Unpopular opinion: RevOps seat at the table (00:31:53) Is the function shifting?

Ravi Parimi has spent 20+ years in revenue operations and GTM strategy, from AWS, VMware and Cisco to Zendesk and ServiceTitan, plus a stint as executive in residence advising a16z portfolio companies. He joins Janis and Philipp to explain why the agent-first mindset breaks in production and why workflows, not agents, are the unit of design. Together they dig into human-agent handoffs, permission scopes and guardrails, and why RevOps has to run agents like a product after launch. Ravi closes with the unpopular opinion that most teams badly underestimate the operating model change ahead. We cover: - Why an agent-first mindset works for experimenting but fails in production - Workflow as the unit of design: inputs, outputs, people and systems - Lead routing as the test case: account disputes, strategic accounts and other edge cases - Why deterministic rules matter if you want agents without hallucinations - Treating agents as digital workers in the talent mix: what agents do best vs. humans - Autonomy with a scope, like a driver's license - How agent speed changes your org design and the humans in the loop - Running agents like a product: roadmap, workflow v1/v2/v3, benchmarks and release cycles - Agent ops: owning speed, quality, cost and latency after launch - Why agents need sandboxes, testing and security layers before production - Why modern revenue organizations will run as systems of agents and humans - Why solution and field engineering leaders could be tomorrow's CROs - Why judgment and taste become the premium skill when agents give the answers Ravi Parimi: https://www.linkedin.com/in/rparimi/  Janis Zech: https://www.linkedin.com/in/janiszech/  Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/  WeFlow: https://www.weflow.ai/  Community: https://www.weflow.ai/community  RevOps Letter: https://www.weflow.ai/revops-letter  Chapters: (00:03) Ravi Parimi's path through RevOps (02:59) What's wrong with agent-first (06:54) Jobs to be done and lead routing (09:56) Why the workflow mindset is missing (13:54) Agents as digital workers (15:44) Autonomy, scope and permissions (17:13) Rethinking workflows and org design (18:58) Running agents like a product (21:11) Testing, sandboxes and guardrails (24:13) The future of RevOps and the CRO (28:48) Field engineers as future CROs (32:01) Judgment, taste and revenue architecture (34:26) Unpopular opinion: operating model

Janis Zech, CEO and founder of WeFlow, joins Kelly Lewis and Tiffany Jones on The Revenue Insiders to explain why AI in go-to-market fails without a clean data foundation. He walks through automated activity capture, AI meeting prep and coaching scorecards on MEDDIC/MEDDPICC, and why accurate forecasts depend on deal intelligence. The hosts push back on critical thinking, business context and vibe coding. Janis leaves listeners with one question for annual planning: are you AI-first or problem-first? We cover: - The three problems behind weak AI results in go-to-market: siloed data, non-selling time and unpredictable quota attainment - Why manual data entry doesn't work and what to automate instead - Design principles for data capture: integrate the signal systems, map everything back to the CRM, stay headless - Why activity has to be mapped to opportunities, not just accounts - AI meeting prep that cuts 15 minutes to 5 and lives in the calendar, Slack or email - Coaching scorecards that auto-populate MEDDIC/MEDDPICC from every email, call and deal room - Automation vs. human in the loop: why reps still need to understand the framework - Why enablement and critical thinking matter more once AI does the data entry - From system of record to system of intelligence to engagement orchestration - Why AI forecasting failed without data pipelines, and how to give CROs multiple forecast numbers - Feeding AI your business context: personas, industries, buying cycles - Mining transcripts and emails for the real close-lost reasons - Why vibe-coding your own GTM stack is slower and costlier than it looks Original episode: https://www.therevenueinsiders.com/podcast/episode/39359e05/ai-first-or-problem-first-the-revops-question-for-2027  Kelly Lewis: https://www.linkedin.com/in/kellylewis418  Tiffany Jones: https://www.linkedin.com/in/tiffanyelizabethjones The Revenue Insiders: https://www.therevenueinsiders.com  Janis Zech: https://www.linkedin.com/in/janiszech  Philipp Stelzer: https://www.linkedin.com/in/philippstelzer  WeFlow: https://www.weflow.ai  Community: https://www.weflow.ai/community  RevOps Letter: https://www.weflow.ai/revops-letter  Chapters: (00:01) Janis's path to WeFlow (03:43) Three problems in go-to-market (05:48) What's different from Gong and Clari (07:08) Design principles for data capture (12:55) AI coaching on MEDDPICC (15:40) Automation vs. human in the loop (19:45) Enablement in an AI world (23:34) System of intelligence (24:52) Forecasting needs deal intelligence (26:48) Feeding AI your business context (30:58) Mining transcripts for real answers (32:14) Vibe coding vs. long-term partnership (35:07) AI-first or problem-first?

Avtar Varma, who just started as VP RevOps at Chainguard after building RevOps functions from Series A startups to Stripe, lays out three stages of enterprise AI adoption: token maxing, centralizing within functions, and a company-wide AI ops team. He explains why usage leaderboards are a vanity metric and what to reward instead. Together with Janis and Philipp, he debates where an AI ops function should sit, who owns the data infrastructure, and why go-to-market is the best place to prove ROI. He closes with an unpopular take on go-to-market engineers. We cover: - Stage one: token maxing, and why "give everyone every AI tool" makes ROI hard to prove - Why AI usage leaderboards are a vanity metric - Hackathons vs. leaderboards: rewarding the best ideas, not the biggest spend - Meeting prep as a simple, high-impact use case, and why AI has to live in the calendar - Stage two: centralizing skills, templates and agents inside each function - Using usage data to coach below-average adopters instead of rewarding top spenders - Stage three: a central AI ops team with a liaison for each function - Where AI ops should sit: COO, CIO or RevOps, and who owns data infrastructure - Saving cost with open-source models where best-in-class models aren't needed - Why the data warehouse is becoming the layer agents run on - Whether RevOps can grow into BizOps or AI ops - Why go-to-market is the best place to start proving AI ROI - Unpopular opinion: go-to-market engineers should be actual engineers Avtar Varma: https://www.linkedin.com/in/avtar-varma Chainguard: https://www.chainguard.dev/ Janis Zech: https://www.linkedin.com/in/janiszech/ Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Chapters: (00:01) Avtar Varma joins from Chainguard (01:58) Three stages of AI adoption (05:54) Is token maxing necessary? (07:33) Leaderboards as a vanity metric (10:33) Hackathons and meeting prep (14:46) Incentives that actually work (16:48) Coaching with usage data (18:10) Stage three: central AI ops (21:07) Who owns data and infrastructure (22:17) Cost, open-source models, security (24:46) Can RevOps become AI ops? (27:46) Why GTM is the place to start (28:31) Unpopular opinion: GTM engineers

Colin Gerber, VP RevOps & Strategy at Socure, returns for part two to unpack how his team forecasts revenue in a fully consumption-based business. He walks through the solution readiness document that substantiates every booking, the "BAR" (booked ARR realization) process that tracks ramp against reality, and why RevOps should own the forecasting system but not its accuracy. Plus: the comp structures that keep AEs and CSMs honest, and the two mistakes that blow the biggest holes in a consumption forecast. We cover: - How Socure prices ~35 modules by API-call consumption, not seats - Giving customers real-time visibility into what they're actually spending - The solution readiness document that turns a sales pitch into a forecastable number - Why AEs still call a bookings number — and when it gets challenged - Booking conservatively for pre-launch startups vs. mature, high-volume customers - Modeling seasonality into ramp schedules (BNPL, gaming, tax season) - Using live trials to close the gap between closed-won and go-live - The monthly BAR review with FP&A - Salesforce, JIRA, NetSuite and Tableau: the four-source forecasting stack - Booking discipline vs. realization — why bookings still matter in consumption pricing - Who actually owns the ramp: AEs, CSMs, and post-sales solution consultants - How 24-month payout schedules shape AE behavior on ramping accounts - The two mistakes that blow up a consumption-based forecast Colin Gerber: https://www.linkedin.com/in/colinsgerber/ Socure: https://www.socure.com/ Janis Zech: https://www.linkedin.com/in/janiszech/ Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Chapters: (00:01) Colin Gerber returns from Socure (02:37) How Socure prices consumption (06:08) Real-time visibility into spend (09:07) Why consumption pricing is spreading (10:49) Forecasting sessions & the solution readiness doc (15:07) Contract terms and seasonality (17:41) Live trials close the go-live gap (20:05) Ramp realization and revenue recognition (21:55) The four-source forecasting stack (25:05) Booking discipline vs. realization (29:14) Who owns the ramp (34:41) Two mistakes that wreck a forecast (39:07) RevOps shouldn't own forecast accuracy

Nicole Bradshaw spent six months as interim global sales leader at Eventbrite — and discovered how much she didn't know about her own job. She joins Janis to explain why RevOps teams know exactly what good selling looks like but never apply it to themselves, and why an order-taking RevOps function is the one most easily automated away. We cover: What changed when Nicole became her own stakeholder Why execs only want your top two problems, not all of them Balancing business impact against time to impact Using OKRs as a vehicle, not a solution Matching operating cadence to stakeholder level Strong opinions held loosely — showing up with a point of view Why order-taking RevOps is the easiest role to automate Bridging strategy and execution as RevOps' unique position Multi-threading inside your own company Picking the right champion for the right initiative Objection handling: anticipate, prepare, bring data Making people's lives easier before you need favors Framing every ask as a win-win Nicole Bradshaw on LinkedIn: https://www.linkedin.com/in/nkbradshaw/ PandaDoc: https://www.pandadoc.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Resource recommendation: Harvard Business Review — Nicole's habit of blocking 30 minutes every other week for professional development reading Chapters: (00:00) Intro & welcome to Nicole (00:37) Nicole's path into RevOps (03:08) Six months leading sales (04:43) Becoming your own stakeholder (07:34) Impact vs. time to impact (09:28) OKRs and operating cadence (13:08) Showing up with agency (17:19) AI-proofing the RevOps role (18:26) Bridging strategy and execution (22:56) Multi-threading and champions

Dan Jiao, VP RevOps at Aircall, has now moved two companies from a global functional model to regional GMs — at Signifyd and again at Aircall. He joins Janis to explain what actually changes when four business units each own their P&L, and why RevOps becomes the glue that keeps one company from quietly becoming five. We cover: Global functional vs. regional GM model — and what stays centralizedThe revenue scale where a regional split starts to make senseRunning RevOps as a product org: the global go-to-market operating systemWho owns segmentation vs. who co-designs territoriesEmbedding regional business partners as de facto chiefs of staffOne global scoring model, multiple regional calibrationsWhy capacity ratios differ market by marketRevOps as the neutral referee in budget and headcount debatesHolding one global forecasting standard across four business unitsBuyer-verified exit criteria instead of rep-filled fieldsSharing best practices when there's no global head of salesDan Jiao on LinkedIn: https://www.linkedin.com/in/danjiao/ Aircall: https://aircall.io Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/WeFlow: https://www.weflow.ai/Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Book recommendation: Switch: How to Change Things When Change Is Hard by Chip & Dan Heath Chapters: (00:00) Intro & welcome to Dan(02:33) Dan's path from sales to RevOps(04:46) Global functional vs. regional GM(08:12) When a regional split makes sense(11:18) Who owns segmentation and territories(14:04) The hybrid model and regional business partners(17:43) Account prioritization and scoring(20:49) Capacity planning across regions(25:53) RevOps at the budget table(27:52) One global forecasting standard(30:31) Buyer-verified exit criteria(32:43) Sharing best practices without a global sales lead(34:50) Book recommendation & close

Mollie Bodensteiner, VP RevOps at ZoomInfo and returning guest, joins Janis to break down how AI is reshaping RevOps roles, team structures, and career paths. RevOps used to report on the business — now it builds and governs the systems that run it. Mollie shares what the go-to-market engineer role actually is, why career ladders are turning into portfolios, and why she stopped doing case studies in hiring. We cover: From reactive help desk to building and governing the systems that run the business AI sprawl: why "give everyone access" now creates a governance problem What a go-to-market engineer really is — beyond the LinkedIn hype Agent ops: monitoring drift, QA, and cost like DevOps monitors uptime Why it's a skill set, not a new job title The analyst shift from firefighting to proactive intelligence Who should own AI: centralized, federated, or center of excellence Flatter orgs, wider spans, and horizontal layers replacing functional silos Career paths becoming portfolios instead of ladders Why Mollie killed case studies in her hiring process Skill vs. will — advice for early-career operators Mollie Bodensteiner on LinkedIn: https://www.linkedin.com/in/molliebodensteiner/ ZoomInfo: https://www.zoominfo.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Book recommendation: The Accountable Organization by John Marchica Chapters: (00:00) Intro & welcome back to Mollie (03:26) How RevOps jobs have changed (05:30) AI sprawl and the governance problem (08:43) The go-to-market engineer role (12:06) Agent ops and the digital workforce (16:25) What happens to the analyst (20:30) Who owns AI in the org? (24:19) Flatter teams, wider spans (28:20) Making RevOps impact measurable (31:43) Why case studies are dead in hiring (34:11) Skill vs. will: advice for operators (37:07) Book recommendation & close

Colin Gerber, VP RevOps & Strategy at Socure, breaks down 17 years of CPQ evolution — from Zuora's billing-wrapper days to today's consumption-ready, platform-agnostic tools. He shares how Socure pressure-tested its pricing in Excel for a year before building anything, and how a new entitlements object now ties CRM, CPQ, and billing into one source of truth. We cover: CPQ's three eras: Zuora → Salesforce CPQ → platform-agnostic tools like DealHub Pressure-testing pricing in Excel before implementing CPQ Socure's shift to à la carte, consumption-based pricing across 35 modules Common CPQ mistakes: over-complication and workflow bloat Scaling SKUs across ~170 countries RevOps' role: guardrails, deal desk, pre-deal margin modeling Using AI to auto-generate Solution Readiness Documents A bi-directional entitlements object unifying CRM, CPQ & billing Automated entitlement capture for PLG motions A preview of consumption-based forecasting Colin Gerber on LinkedIn: https://www.linkedin.com/in/colinsgerber/ Socure: https://www.socure.com Weflow: https://www.weflow.ai/ RevOps Chat Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Janis on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer/ Book recommendation: Team of Teams: New Rules of Engagement for a Complex World by General Stanley McChrystalChapters: (00:00) Intro & welcome to Colin (01:51) Colin's background (04:23) The three phases of CPQ history (10:00) CPQ challenges with consumption-based pricing (14:12) Common CPQ implementation mistakes (19:03) The role of RevOps in CPQ (21:19) Using AI in CPQ (25:21) Where's the system of truth? (32:26) Direct vs. self-service/PLG (34:44) Teaser: consumption-based forecasting (36:44) Book recommendation & close

Markus Jaensch, Head of RevOps at Aiven, joins Janis and Philipp to unpack what consumption-based pricing actually means for a RevOps team running a $100M+ ARR business. Aiven — the open-source data platform behind managed Kafka, Postgres, OpenSearch, and ClickHouse — recently crossed $100M ARR, and Markus walks through how the forecasting model, comp design, and territory setup have evolved over 4.5 years to deal with the fundamental problem of consumption: a "win" doesn't equal revenue, and the next 12 months can swing either direction. We cover: Why consumption ARR is structurally harder to forecast than SaaS bookingsWhy Aiven moved away from pure ARR forecasting and back to bookings + finance-modeled ARRThe three forecast buckets: new business, add-on, and commit contractsUsing MEDDPICC with mandatory mutual success plans and CRM score as a red-flag signalT-shirt sizing (S/M/L/XL) with solution architects to anchor opportunity sizeThe two non-negotiables to close-won at Aiven: valid payment method + 3 consecutive days of consumptionWhy RevOps owns the close-won gate — and the tradeoff of being a controlling functionThe Farmer/Hunter evolution: from split, to mixed, to dedicated Inside Sales + named-territory Field repsComp design: bookings as a sanity metric, ARR as the paid metric, with new-ARR ramp-to-sizeStack: Salesforce + data warehouse, and why Aiven switched from spot ARR to a 30-day averageBuilding the process around the customer first, not internal convenienceMarkus Jaensch on LinkedIn: https://www.linkedin.com/in/markus-jaensch/ Aiven: https://aiven.io Weflow: https://www.weflow.ai RevOps Chat Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revopsletter Janis on LinkedIn: https://www.linkedin.com/in/janiszech Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer Book recommendation: The Café on the Edge of the World by John Strelecky Chapters: (00:00:00) Intro & Welcome to Markus(00:02:14) Why Consumption ARR Is the Topic — and Aiven Crossing $100M(00:03:39) Why Consumption ARR Is So Hard to Forecast(00:08:30) Bookings vs. ARR Forecasting — and Aiven's Evolution(00:12:53) MEDDPICC, Mutual Success Plans & Deal Hygiene(00:16:21) T-Shirt Sizing with Solution Architects(00:18:01) The 3-Day Consumption Rule for Close-Won(00:22:28) Modeling the ARR Ramp After a Booking(00:26:27) The Farmer/Hunter Evolution & Inside Sales Motion(00:29:37) Comp Design: Bookings, ARR, and Ramp-to-Size(00:31:00) Stack: Salesforce, Data Warehouse & 30-Day Average ARR(00:34:34) Final Learnings on Consumption Forecasting(00:36:29) The Customer-First Principle(00:38:02) Book Recommendation & Close

In this episode of the RevOps Lab, Janis sits down with Andy Smidmore — a 15-year RevOps and SalesOps veteran whose résumé reads like a tour of high-growth West Coast SaaS (Confluent, Cloudera, Docker, most recently Ditto). Andy is also the author of a popular LinkedIn article series on RevOps fundamentals, including the piece that anchors today's conversation: Forecasting Is a Trust Problem Before a Math Problem. Janis and Andy unpack why the magic forecast number leadership wants is just the surface, why broken sales stages quietly leak forecast accuracy, and why mixing deal reviews into your forecast call destroys the very trust your reps need to be honest with you. We cover: Why forecasting is a trust problem first, a math problem second Why the "how much are you committing?" question on day one of a forecast call is the wrong starting point Building sales stages around the buyer's journey, not your selling process — and why misaligned stages are a leaking bucket The danger of performative deal progression and the false hope it creates for leadership Why deal reviews and forecast calls should be two separate motions "I'm not questioning you, I'm asking questions" — the subtle reframe that changes the whole room Crystal-clear definitions for pipeline vs. commit, kept short (5 bullets, not essays) What a true commit actually requires: technical sign-off, legal redlines, procurement engaged, stakeholder alignment Why a consistent week-in, week-out forecast format builds rep trust over time Treating forecasting as a team sport — and why punishing reps swings them toward concealment, not honesty Links: Andy Smidmore on LinkedIn: https://uk.linkedin.com/in/andy-smidmore-3aab0556 Andy's article series on RevOps & forecasting (LinkedIn) Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letter Book recommendations: Disrupted by Dan Lyons The Girl with Seven Names by Hyeonseo Lee Chapters: (00:00) Intro & welcome to Andy (00:51) Andy's 15-year journey through Confluent, Cloudera, Docker, Ditto (03:08) Why forecasting is the topic — and the article that started it (04:03) Why every forecast framework is unique to its company (06:15) Intelligence tools haven't replaced the foundational framework (08:13) "How much are you committing?" — the surface-level forecast call (10:01) The top forecast process problems Andy keeps seeing (11:33) Sales stages built around the buyer journey, not the seller (12:24) The POC stage problem: what does "done" actually mean? (14:25) When poor stage definitions destroy trust between reps and leadership (15:43) Creating an open, honest forecast call environment (18:17) From gut-based reviews to data-backed conversations (20:22) Why deal reviews and forecast calls must be separate motions (23:25) "I'm not questioning you, I'm asking questions" (25:00) The cadence: clean pipeline Thursday, deal review Friday, forecast Monday (28:32) Building a clear forecasting methodology — pipeline vs. commit (31:34) What actually constitutes a commit deal (35:21) Closing thoughts: stages, enablement, cadence, culture (39:35) Book recommendations & close

In this host-only episode of the RevOps Lab, Janis and Philipp take stock of what three years of building a forecasting tool — and hundreds of conversations with sales leaders, RevOps teams, and CROs — have taught them about getting to a reliable, repeatable forecast number. They unpack why forecasting is a process (not a number), why AI only works on top of a clean data foundation, and how the best companies combine roll-up, dynamically weighted, and AI-predicted forecasts into a single operating cadence. We cover: Why forecasting accuracy is the output of a well-run sales org, not the input The operating cadence: weekly meetings, deal reviews, and stakeholder alignment that make forecasting work Building the data foundation: activity capture, multi-threading signals, conversation intelligence, and CRM autofill Why deal hygiene and shared qualification criteria (SPICED, MEDDIC) are non-negotiable before you forecast Splitting the forecast into new logo, expansion, and renewal — and why bookings ≠ consumption The three-pillar forecast: dynamically weighted + bottom-up roll-up + AI prediction (with corridors, not single numbers) How to run a roll-up motion: baseline vs. best case, rep forecast vs. independent manager forecast Why running roll-ups in spreadsheets breaks down at 50+ reps Calculating dynamic stage probabilities by rep tenure or team — and when it's worth doing Why AI predictions are only as good as the data foundation underneath them Links: Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.getweflow.com WeFlow RevOps resources: https://www.getweflow.com/revops Join the RevOps Chat Community: https://www.getweflow.com/community Subscribe to the RevOps Letter: https://www.getweflow.com/revops-letter Operating Cadence master deck: ping Janis or Philipp on LinkedIn to request Chapters: (00:00) Intro: Has AI fundamentally changed forecasting? (01:34) Why forecasting is a process, not a number (02:25) What an operating cadence actually looks like week-to-week (04:23) The data foundation: why CRM alone isn't the system of truth (06:53) Anchoring deal conversations on a shared qualification methodology (08:42) How AI adds an objective layer through automated capture and CRM autofill (09:29) Stage entry/exit criteria and deal signals for deal health (13:06) Why "comparable deals" matters as you scale past 50 reps (14:23) Forecasting as the end result of a well-functioning sales org (16:05) Splitting forecasts: new logo, expansion, renewal, bookings vs. consumption (17:22) The three-pillar forecast: dynamically weighted, roll-up, AI prediction (18:50) Roll-up forecasting: baseline vs. best case, rep vs. manager numbers (24:07) Anatomy of a roll-up: hierarchy, gap-to-quota, pipeline coverage, deal-by-deal (26:43) Why spreadsheets break down for roll-up forecasting at scale (28:07) AI prediction models: aggregate vs. deal-by-deal scoring (29:02) Dynamically weighted forecasts and rep-level stage probabilities (32:44) Why AI predictions only work on top of clean data foundations (34:27) Book recommendation & close

In this episode of the RevOps Lab, Janis and Philipp sit down with Robert Kaiser and Christoph Dünhuber from Tacto — the Sequoia- and Index-backed AI procurement platform powering 300+ industrial customers across the German Mittelstand. Robert leads revenue functions, Christoph leads RevOps. Together they unpack how Tacto scaled to 130 employees by making customer value the center of gravity across every revenue function — from BDR to renewals — and why "customer-centric" only works when it is operationalized, not just stated. We cover: Why customer value (not org design) is priority #1 in a scaling revenue org How webinars with 200–500 weekly attendees became Tacto's top-of-funnel engine The "Customer Internship" program: every new hire spends 2–3 days embedded with a customer Why marketing and outbound are converging — and how to make cold outreach feel warm Selling to dual personas: the procurement team (user) vs. the MD/CFO (economic buyer) How forward-deployed Customer Value Managers (ex-BCG, ex-Roland Berger) drive renewals and expansion Why 70–80% of new Tacto customers speak to an existing customer before signing Building a value-engineering motion that makes ROI tangible inside the sales cycle Links: Tacto: https://tacto.ai Robert Kaiser on LinkedIn: https://www.linkedin.com/in/-robert-kaiser/ Christoph Dünhuber on LinkedIn: https://www.linkedin.com/in/christophduenhuber/ Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.getweflow.com Join the RevOps Chat Community: https://www.getweflow.com/community Subscribe to the RevOps Letter: https://www.getweflow.com/revops-letter Book recommendation: The Qualified Sales Leader by John McMahon Chapters: (00:00) Intro & welcome (01:36) Meet Robert & Christoph — their paths at Tacto (03:13) What Tacto does and the $500M procurement opportunity (07:47) Why customer-centricity is a two-sided coin (users + economic buyers) (10:24) The Tacto customer journey: from webinars to account management (15:44) Org design for scale: renewals, upsells, expansion leads (17:00) Selling to procurement vs. selling to the managing director (19:43) How outbound stays customer-centric when brand and webinars lead (23:24) Customer Internships: every new hire embeds with a customer (28:00) Hiring for cultural fit and the "warm calling" academy (29:35) Referrals and why 70–80% of new customers speak to existing ones (31:10) Dual-persona strategy: power user vs. economic buyer (32:49) How a CX mindset reshaped Tacto's entire sales motion (35:21) Org design deep dive: SEs, Value Engineers, CVMs (37:39) Book recommendation & close

Eric Portugal Welsh, Head of RevOps at PlanetScale and returning guest on the RevOps Lab, joins Janis and Philipp to share how he went from leading a team of six to operating as a solo RevOps function — and how AI is making that possible. Eric walks through his full AI stack, his vibe coding journey, and the specific agents and workflows he's built to automate everything from pipeline reviews to stakeholder communication to onboarding himself at a new company. We cover: Going from a RevOps team of six to a team of one — and why that's the PlanetScale model The full AI stack: Claude, Cursor, Cowork, Gemini, Notion AI — and when to use which How Eric onboarded himself in days instead of weeks using Claude and Notion agents Vibe coding a full pipeline dashboard from a single Salesforce history report in 90 minutes Why PlanetScale's head of finance hasn't opened Excel in months — everything is vibe coded Building agents for weekly status updates, pipeline prep, and Slack-based Q&A A communication agent that mirrors the COO's tone and preempts his questions The Monday morning pipeline agent: auto-generated executive overviews before the 8:30 call Why VPs of sales and CROs are the heaviest AI users — not the reps How the CRM interaction model is shifting from reports and record pages to chat interfaces Practical vibe coding tips: build an acceptance criteria doc first, then prompt the build The scaling challenge: why open data works at 65 people but breaks at 500+ RevOps as the orchestration layer for AI-powered go-to-market workflows Eric Portugal Welsh on LinkedIn: https://www.linkedin.com/in/ericwelsh1/  PlanetScale: https://www.planetscale.com  Weflow: https://www.getweflow.com  RevOps Chat Community: https://getweflow.com/community  RevOps Letter: https://www.getweflow.com/revopsletter  Janis on LinkedIn: https://www.linkedin.com/in/janiszech  Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer Chapters:  (00:00:00) Introduction & Eric's Return to the Pod  (00:00:53) From Deputy to PlanetScale: RevOps Team of One  (00:03:45) The Full AI Stack: Claude, Cursor, Gemini, Notion AI  (00:05:05) Self-Onboarding with AI: Days Instead of Weeks  (00:08:40) Claude vs. Gemini vs. ChatGPT: What Works Best  (00:11:45) Vibe Coding a Pipeline Dashboard in 90 Minutes  (00:12:20) PlanetScale's Head of Finance: Zero Spreadsheets in Months  (00:13:25) Getting Started with Vibe Coding: Tips for RevOps  (00:17:55) Practical Tip: Build Acceptance Criteria Before Prompting  (00:19:15) More AI Use Cases: Cloning Yourself as a Team of One  (00:21:05) Weekly Status Agents & Notion-Based Slack Q&A Bots  (00:23:10) The Monday Morning Pipeline Agent  (00:24:20) Communication Agent: Matching the COO's Tone & Style  (00:25:45) Context: 65-Person Company, 85% Engineers, Open Data Culture  (00:27:10) Why This Will Come to Every RevOps Team — and the Scaling Challenges  (00:29:20) Agentic Content Creation for Outbound Sequences  (00:31:30) Why VPs of Sales Are the Heaviest AI Users  (00:33:45) The Future: Chat-Based CRM Interaction Over Reports and Record Pages  (00:36:00) RevOps as the AI Orchestration Layer  (00:37:05) Book Recommendation & Wrap-Up

Stephen Diorio, author of "Revenue Operations" and a go-to-market strategy veteran with decades of experience — from co-founding one of the first GTM strategy firms in 1993 to serving as a Gartner analyst — joins Janis and Philipp to unpack the math of growth. Stephen explains why the financial systems behind most B2B organizations fundamentally fail to measure what actually drives revenue, and how RevOps can close the gap between what the business spends on growth and what it actually gets back. We cover: Why traditional accounting treats all go-to-market spend as expense — and why that's fundamentally wrong Growth assets: databases, brands, digital channels, content — and why no one measures their value The bow tie model: what it captures, what it misses, and why the back end is where CFOs get scared Why a third of closed deals originate from actions taken 12–18 months earlier The 25 post-booking variables that can double or halve lifetime customer value Why "cost to sell" has 100 definitions across 100 companies — and none are right The dark funnel and LLM search: why 90% of the buying process is invisible Forward-looking vs. backward-looking signals: product telemetry, adoption, and renewal prediction The nine customer behavior changes that every growth investment ultimately drives Why documenting assumptions — not just measuring outcomes — is the key to continuous improvement Treatment models and customer experience: why no one can answer "what does a good CX cost?" Share of wallet and account penetration: the massive untapped opportunity most orgs can't quantify Stephen Diorio on LinkedIn: https://www.linkedin.com/in/stephen-diorio-1339011/ Revenue Operations (Book): https://www.amazon.com/Revenue-Operations-Stephen-Diorio/dp/1119871115  Weflow: https://www.getweflow.com  RevOps Chat Community: https://getweflow.com/community  RevOps Letter: https://www.getweflow.com/revopsletter  Janis on LinkedIn: https://www.linkedin.com/in/janiszech  Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer Chapters:  (00:00:00) Introduction  (00:00:32) Stephen's Background & Why He Wrote the RevOps Book  (00:02:45) The Math of Growth: Why It's One Big Equation  (00:03:25) Growth Assets: Brands, Data, Channels — and Why No One Values Them (00:05:10) Why Financial Accounting Fails Go-To-Market  (00:07:10) Cost to Sell: 100 Companies, 100 Definitions  (00:09:35) Walking Through the Bow Tie: Top of Funnel to Post-Sale  (00:11:40) Why We Overplay Conversion Rates and Underplay the Dark Funnel ( 00:13:00) The Back End of the Bow Tie: Where CFOs Get Scared  (00:15:10) Reliable Cash Flow as the North Star Metric  (00:16:00) Account Penetration & Share of Wallet: The Untapped Opportunity  (00:18:10) Treatment Models: What Does a Good Customer Experience Actually Cost?  (00:20:05) Reliable vs. Accurate Revenue: Why Sandbagging Kills Forecasting  (00:22:30) The Scientific Method: Document Assumptions, Then Test and Adjust  (00:26:35) Dark Funnel, LLM Search & Why 90% of Buying Is Invisible  (00:30:05) Forward-Looking Signals: Product Telemetry and Adoption Data  (00:32:05) The Nine Customer Behavior Changes That Every Growth Investment Drives  (00:35:25) Brand as an Asset: Touch Points, Awareness, and Future Option Value  (00:37:05) Book Recommendation: The Goal by Eli Goldratt