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Author name: Sneha S

AI, GTM, RevOps

Unlocking Revenue Intelligence: Bridging Data Gaps with AI & GTM Strategies

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In this episode of the Revenue Lounge Podcast, host Randy Likas and guest Uday Sharma discuss the critical importance of data trust and hygiene in modern revenue operations. They explore how fragmented data can lead to poor decision-making and the necessity of building a centralized data system to enhance revenue intelligence. Uday emphasizes the role of analytics in shaping strategy rather than merely reporting metrics, and the conversation also delves into the implications of AI on data quality and governance. Uday shares insights on how to effectively advocate for funding data initiatives and the importance of changing organizational behavior to improve data practices.

GTM

PLG to Enterprise GTM: Playbook for Experimentation, Signal Discipline and AI

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PLG to Enterprise GTM: RevOps Playbook for Experimentation, Signal Discipline, and AI That Actually Works A conversation with Stephanie Couzin. Executive Summary Lucid’s GTM evolution is not a “PLG vs Sales” story. It’s an operating model story. In this episode, Stephanie Couzin (VP, GTM Strategy & Ops at Lucid) breaks down how modern revenue teams can scale experimentation, build enterprise sales muscle, and adopt AI without turning their tech stack into a Frankenmonster. Readers will learn: PLG → Enterprise is a signal shift: you move from optimizing for users to optimizing for accounts, buying committees, and expansion readiness. Experimentation only works if comp risk is managed: GTM tests touch variable pay, so pilots must be designed as true win-wins. Psychological safety is a growth lever: teams ship better ideas faster when people can speak up, fail fast, and share learnings without fear. Data is the cost of entry for GTM testing: if upper-funnel metrics and activity data are messy, your “experiments” turn into opinions. Standardize AI or suffer whack-a-mole: pick a core AI platform to reduce tool sprawl and enable repeatable adoption. Agentic vs Copilot AI are different games: agentic is replacement (parity + cost savings), copilot is augmentation (productivity + more customer time). Start with the use case, not the tool: the fastest path to value is defining the workflow problem first, then deciding buy/build. One priority beats twelve “priorities”: focus drives execution, and execution is the only feature that matters. Facebook Twitter Youtube The Hidden Shift: From “Users” to “Accounts” Early Lucid was deeply product-led, built on a mature self-serve engine. Then came the layering: Sales motion Segmentation Enterprise complexity Multi-threaded post-sales workflows Stephanie describes it as moving from user-centric growth to account-centric expansion. PLG gives you usage.Enterprise GTM demands orchestration. The real challenge isn’t adding sales reps.It’s building the infrastructure to know when, where, and why sales engagement should happen. Experimentation Built Lucid’s Sales Muscle Stephanie points to Lucid’s early experimentation with a Product Qualified Motion. Most PLG companies start with lead scoring at the user level. Lucid evolved it further: Not just which user is engaged But what is the account telling us? And who should we reach out to now? That shift is everything. “Where that really evolved over time was identifying at the account level, what are the signals we need to outreach at the right time with the right messaging.” — Stephanie Couzin That’s the marriage of: First-party product signals Third-party intent and context Segment-aware targeting Persona-aware outreach Stephanie calls it the “special sauce” of modern lead scoring. The Account Expansion Checklist (Lucid’s Internal Recipe) Stephanie hints at what many GTM teams lack: an internal definition of “ready.” Lucid has an internal checklist: “A set of account signals where we say: this account is locked and ready to expand.” If the checklist isn’t met, Lucid doesn’t stop. They reverse-engineer value: You use Slack? You use documentation tools? You have workflows Lucid integrates into? Then Lucid doesn’t sell harder. They sell smarter: “This is how you get more value.” Cross-Functional Alignment: Experimentation Without Chaos Testing in GTM is not like testing in product. Because in revenue… someone’s commission is always involved. Stephanie puts it bluntly: “Testing something new will impact someone’s variable compensation.” — Stephanie Couzin So experimentation requires designing for buy-in. Lucid ran a coverage pilot by: Using lower-risk accounts Making rep trades fair Ensuring nobody felt punished for participating A GTM experiment only works if it’s a win-win. Otherwise reps sandbag, ignore, or resist. And your “pilot” becomes theater. Psychological Safety Is the Real GTM Scaling Lever Stephanie goes deep here, referencing Google’s Project Aristotle – Google studied what makes teams high-performing. The #1 factor wasn’t IQ.It wasn’t experience.It wasn’t process. It was: psychological safety. “They could speak up without fear of consequence. They felt freedom to fail fast.” — Stephanie Couzin Stephanie teaches this internally at Lucid. And she’s clear: This isn’t about politeness.It’s about intentional leadership. Practical mechanisms Lucid uses: Weekly project show-and-tells “Smart Fridays” where reps share plays Normalizing learnings over perfection The GTM Psychological Safety Loop Safe to speak → More ideas → Faster experiments → Better learning → Higher trust → Stronger execution Data Discipline: The Unsexy Requirement for GTM Experiments Stephanie delivers one of the hardest truths in RevOps: Lower funnel data is solid. Upper funnel data is… suspicious “As you move further up funnel… fewer eyes are on those metrics.” — Stephanie Couzin Testing requires: Full-funnel accuracy Integrated activity capture Shared KPI definitions Governance (tagging, segmentation discipline) AI in GTM: Standardize or Suffer Stephanie’s AI governance advice is refreshingly simple: “Standardize on something. Otherwise you’re in whack-a-mole.” Lucid standardized on Google Gemini. Not because it solves every revenue use case. But because: It reduces fragmentation It sparks shared experimentation It creates repeatable workflows It prevents reps from duct-taping random tools together The goal is not AI everywhere. The goal is AI that fits into systems. The Most Important GTM AI Rule: Start With Use Cases, Not Tools Stephanie nails this: “What use case are you trying to solve, not what tool do you want?” Because shiny object syndrome is real. Most orgs buy AI like toddlers choosing cereal: “Ooh, the box is shiny.” Lucid forces the opposite: Define workflow pain first.Then evaluate tooling. AI Use Case Intake Form What GTM workflow breaks today? What manual effort exists? What does “better” look like? Is this assistive or agentic? Can current stack solve 80% already? What data sensitivity is involved? What adoption friction will occur? Agentic AI vs Copilot AI: Stop Confusing the Two Agentic AI Replaces human work Measured in cost savings Goal is parity (“do no harm”) Copilot / Assistive AI Enhances workflows Measured in productivity and customer time Harder to quantify, but more transformative “Most AI we adopt for revenue teams is assistive. It makes teams more productive.” That’s where the real gains are: Better prep Faster follow-up Less manual CRM work More customer-facing time The GTM Takeaway Stephanie Couzin’s playbook isn’t about

How Nektar helps AI Hypergrowth companies move even faster
AI

How Nektar helps AI Hypergrowth companies move even faster

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How Nektar Helps AI Hypergrowth Companies Move Even Faster Artificial Intelligence 10 min Fast-moving AI companies are having a moment. Every week a new AI-native startup crosses $100M ARR in what feels like record time. Accel’s 2025 Globalscape report shows a “new breed of AI-native applications” hitting scale much faster than previous generations of SaaS, with some reaching $100M ARR in just a few years. That velocity is backed by unprecedented capital. Prominent AI companies like Cursor, Writer, Groq and Fireworks are raising huge rounds, hiring at triple-digit growth rates, and building products that spread virally from individual builders into the world’s largest enterprises. AI application categories like developer tools, finance, cybersecurity and vertical AI each attracted multiple billions of dollars in 2025 funding alone. Nektar sits right in the middle of this wave. Over the past year, we’ve partnered with some of the fastest-growing AI companies in the US – including Writer, Cursor, Groq, Chainguard and Fireworks  to help them turn raw go-to-market activity into clean, structured, AI-ready data they can actually execute on. This blog looks at why AI companies grow differently, what that does to their GTM data, and how Nektar helps them grow even faster. The new AI growth curve: Speed, Efficiency and Youth Funding and company maturity Accel’s data makes one thing clear: AI is no longer a niche category. It’s the new centre of gravity for software investing. Total EU/US/IL cloud & AI funding (excluding models) has climbed into the ~$180B+ range annually, with 2025 setting fresh records.   AI model funding is heavily concentrated in the US, but on the application side, EU/IL funding now represents roughly two-thirds of US levels, showing how global this wave has become. The winners look very different from the last SaaS cycle: over 65% of the Accel US & Europe AI 100 are 0–3 years old, and US winners skew especially young at 2.4 years on average. Put simply: AI companies are raising big, hiring fast, and still figuring out their GTM motion on the fly. Bottom-up adoption and insane efficiency AI-native tools are spreading from the bottom up: Developers using AI coding assistants jumped from 36% in 2023 to 90% in 2025 – in just two years. Tools like AI IDEs, agents and copilots are hitting milestones such as “$100M ARR in 8 months” and “10x YoY growth,” according to Accel’s case studies of leading AI-native apps. This isn’t just fast growth – it’s efficient growth. Accel estimates that leading AI applications now generate 3–10x more ARR per employee than prior generations of SaaS companies. But that speed and efficiency create a GTM paradox: You can scale product adoption and revenue incredibly fast. But your GTM data, process and tooling often lag badly behind. The hidden tax of hypergrowth: messy GTM data Most fast-growing AI companies share a few traits: They sell into large, multi-person buying committees (Fortune 500, Global 2000, high-growth tech). They run hybrid motions – PLG bottoms-up adoption plus enterprise sales, often with heavy founder-led or executive-led outbound. Their GTM stack is complex and evolving: Salesforce + Gong + Snowflake + ABM + sequencing tools, changing every few quarters. They are young – which means processes, definitions and data hygiene were rarely “designed,” they just happened. That shows up in four chronic problems: Invisible buying groups Activity sits at the account or activity object level, not tied to which humans are actually influencing a deal. Contact roles are incomplete, incorrect, or simply not used. Multi-threading that’s impossible to measure Leadership wants reps and CSMs to multi-thread. But nobody can answer basic questions like: “How many net new stakeholders did this SDR actually bring in?” “Which deals progressed because we pulled in the economic buyer early?” Broken marketing attribution for enterprise deals First-touch and last-touch models collapse when there are 10–20 stakeholders, dozens of events and campaigns, and long sales cycles. “Marketing sourced” covers only a small fraction of reality. No shared view of the customer journey Pre-pipeline engagement, in-pipeline meetings, onboarding, success reviews, expansion conversations – they live in different systems owned by different teams. This is exactly the gap Nektar is built to fill. Nektar as the data backbone for AI GTM At its core, Nektar is a revenue data platform that: Harvests metadata from communication tools (email, calendar, meetings, sequences). Cleans and transforms that data. Writes it into Salesforce against the right opportunities, accounts, contacts and leads. Automatically creates and updates Opportunity Contact Roles (OCRs) with accurate personas (economic buyer, champion, influencer, etc.). Generates revenue signals that help teams act – from “missing exec sponsor” to “multi-threading risk” to “QBR overdue.” Writer is a great illustration of how fast-moving AI companies use this foundation Writer: building an AI-ready GTM engine on top of Nektar Writer is an enterprise AI platform selling into Fortune 500 and Global 2000 organizations. Their GTM complexity is huge: multi-persona deals, long cycles, and a mix of PLG, partner, and enterprise motions. One activity capture layer for Sales, CS and Marketing Writer started with Nektar in sales, then expanded to sales engineering, customer success and now marketing. Nektar: Captures emails, meetings and other activities from tools like Gmail and calendar. Associates them correctly with accounts, opportunities and contacts in Salesforce. Backfills historical data by “travelling back in time” across past emails and calendars, so data isn’t limited to post-implementation activity. Creates missing contacts and writes them into Salesforce as OCRs with mapped personas. Compared with their previous setup (Gong plus internal workarounds), Writer’s RevOps leaders called out that Nektar simply does a better job of capturing and correctly associating activities, especially in complex account structures with multiple open opportunities. This gives Writer a single, reliable activity dataset they can push into their warehouse (GCP) and model in Omni for analytics – a critical enabler for AI-driven GTM. Making multi-threading measurable (and compensable) Writer wants SDRs and AEs to multi-thread aggressively – and they want to pay them for doing it. The problem: Nektar was so good

Nektar.ai vs People.ai: A Buyer's Guide
Buyer's Guide

Nektar vs Backstory (formerly People.ai): A Buyer’s Guide

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2026 Guide for Enterprise GTM Teams Seeking Backstory (People.ai) Alternatives Buyer’s Guide 11 min Jan 27, 2026 Updated: August 13, 2026 A quick note before you read further: People.ai rebranded to Backstory in April 2026, repositioning as a “Revenue Answers Platform” with a conversational AI layer on top of its existing activity-capture technology. Same underlying company and core capture technology, new name and pitch. This guide refers to it as Backstory throughout, noting the People.ai history where it’s directly relevant (its Gartner recognition, for instance, was announced under the People.ai name). Introduction: Two Different Approaches to the Same Problem Both Backstory and Nektar operate in the revenue data capture category, helping enterprises automatically capture GTM activity and enrich their CRM using AI. However, they solve fundamentally different problems for different buyers. Backstory (formerly People.ai) is an established revenue intelligence platform with strong analytics capabilities, recognition as a Visionary in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration (awarded under the People.ai name, prior to the April 2026 rebrand), and a mature suite of tools including ClosePlan, account planning, and leadership dashboards, now layered with a conversational query interface as part of its Backstory repositioning. Nektar is an advanced data-first GTM telemetry solution focused on delivering clean, accurate, AI-ready CRM data directly into standard Salesforce objects, designed specifically for enterprises that want to power their existing BI stacks rather than adopt another analytics platform. This guide is intended for GTM leaders, RevOps leaders, Sales Operations teams, and Data teams evaluating both solutions. It draws on direct enterprise evaluation feedback, product analysis, and independent research to help you determine which solution fits your specific needs. Get our latest insights into your inbox Who This Guide Is For This comparison is most relevant if your organization: Already operates a mature BI stack (Databricks, Snowflake, Looker, Tableau) Has dedicated RevOps or SalesOps teams building custom analytics Prioritizes CRM data accuracy over out-of-the-box dashboards Needs granular control over what data syncs to Salesforce Requires specific detail around internal and external participation or meeting attendance intelligence (not just invitee data) If your priority is comprehensive analytics UI, pre-built dashboards, and account planning tools, Backstory may be the stronger fit for your organization. But if you’re looking to solve the data problem at its core without the additional enablement effort of new training, Nektar is a better bet. This guide focuses on scenarios where data infrastructure is the primary buying criterion. The Core Difference: Analytics-First vs Data-First The fundamental difference between these platforms comes down to philosophy, and the April 2026 rebrand sharpened rather than changed this distinction: Backstory is built around the premise that revenue teams need better analytics and insights delivered through their platform, now explicitly reframed around conversational, natural-language answers rather than dashboards alone. Data capture exists to power those answers, scorecards, and AI-driven recommendations. Nektar is built around the premise that enterprises already have analytics tools they trust. What they lack is clean, accurate, complete, unified rep activity data in the CRM to feed those tools. Nektar focuses on being the best possible data layer, not an additional interface to learn. Neither approach is inherently superior; they serve different organizational needs. The question is which approach matches your GTM infrastructure strategy, and whether a conversational interface actually solves your problem or just adds a new way to ask a question the underlying data still can’t fully answer. Considering an Alternative to Backstory? See how Nektar delivers 90%+ attribution accuracy directly into your Salesforce, without the Backstory price tag. Check Side-by-side Feature Comparison Why Enterprises Evaluate Backstory Alternatives Based on conversations with enterprise buyers evaluating both platforms, several consistent themes emerge: Existing Analytics Investment Many large enterprises have already invested significantly in Databricks, Snowflake, Looker, or Tableau. Their internal ops teams build custom dashboards tailored to their specific sales motions. For these organizations, adopting another analytics platform, conversational interface or not, creates redundancy rather than value. They want the underlying data, not another UI. Salesforce Integration Model Backstory (like People.ai before it) uses a managed package approach that creates custom objects in Salesforce. While this provides rich functionality within Backstory’s own ecosystem, some enterprises report challenges including: Additional automation required to map data into standard Salesforce fields Complexity when using captured data in existing workflows or forecasting Duplicate participant records requiring cleanup Nektar writes directly to standard Salesforce objects (Events, Tasks, Contacts), which can simplify integration with existing processes but may offer less specialized functionality. Meeting Attendance Requirements A significant differentiator for some buyers is meeting attendance intelligence. Backstory’s meeting data typically relies on calendar invites and recorded calls via conversation-intelligence platform integrations. Nektar captures both invitees and actual attendees, along with meeting status (completed, cancelled, no-show, under 10 minutes), without requiring recording. For organizations focused on coaching, churn analysis, or executive involvement tracking, this distinction can be decisive, and it isn’t something a conversational query layer on top of the same underlying capture gap actually solves. Data Volume Control Some enterprises express concern about data volume and Salesforce storage costs. Nektar offers granular sync controls that let administrators define which activities to capture, which contacts to create, and what thresholds to apply. Backstory’s capture approach may generate higher data volumes, which can be beneficial for analytics but challenging for storage-conscious organizations. Category Nektar.ai Backstory (formerly People.ai) Salesforce data model Standard objects (Events, Tasks, Contacts) Managed package with custom objects Opportunity Matching AI/ML graph-based, self-learning Rule-based, configurable Meeting Attendance Invitees + actual attendees + meeting status Primarly invitee-based, recorded calls via CI partners Meeting Intelligence Source Direct Zoom/Teams integration (no recording required) CI platform integration (Gong, Zoom IQ, Webex) Engagement Scoring Customizable, writes to Salesforce fields Pre-defined, displayed in analytics UI Multi-user Attribution All internal users + external contacts Primarily organizer-focused Noise Control Granular sync rules and filters Comprehensive capture approach Analytics & Dashboards Minimal (Data-focused) Comprehensive built-in analytics Account Planning Not a primary focus Strong (ClosePlan, org charts) Data Portability Standard objects, no lock-in Managed package migration required Best for Data-first

AI, Customer Success

Transforming Customer Success in the Age of AI

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Rebuilding Customer Success for the AI Era: Lessons from a VP of Customer Success A conversation with Chad Gorman. Executive Summary This article examines how customer success leaders should rethink AI adoption, using insights from an in-depth conversation with Chad Gorman, VP of Customer Solutions and Success (North America) at LivePerson, on the Revenue Lounge Podcast hosted by Randy Likas. Rather than focusing on automation or AI features, Gorman argues that AI-ready customer success is fundamentally about visibility, data discipline, and relationship intelligence. Readers will learn: Why most AI initiatives in customer success fail before deployment  How unifying fragmented CS data is a prerequisite for AI or automation What an effective early warning system looks like  Why engagement and relationship depth are stronger leading indicators than product usage alone How AI can expose relationship “white space” across complex buying committees Where buy vs build decisions actually differ across enterprise and mid-market segments How to embed AI into CSM workflows over-relying on automation Which metrics matter when measuring AI’s impact on retention, risk, and productivity Why the real promise of AI in customer success is reclaimed time for strategic customer work Facebook Twitter Youtube From Call Centers to Customer Outcomes Gorman’s perspective is shaped by an unusual career arc. He started in contact center operations, moved into IT at DirecTV, and then crossed over to the vendor side after a colleague recruited him to Splunk. “I didn’t even know what a CSM was,” he admits. “But once I saw how customer success could be built as a scalable engine, I was hooked.” — Chad Gorman From Splunk, he went on to lead global cloud customer success at VMware, before joining LivePerson, where he now oversees customer success and professional services across North America. That mix of operator, builder, and enterprise leader shows up in how he thinks about AI. Practical. Outcome-driven. Skeptical of hype. ​​AI Adoption Fails Before Deployment Most AI initiatives stumble long before a model is ever deployed. According to Gorman, the real friction points show up earlier in the buying and approval cycle. The Hidden Gates to AI Adoption Governance reviews and AI councils Legal, compliance, and security documentation Industry-specific scrutiny, especially in financial services Undefined success metrics “You can sell software all day long. But if you are not there to shepherd customers through governance, compliance, and approval gates, adoption will stall.” — Chad Gorman Customer Success Has Become Revenue Insurance In volatile markets, customer success is no longer a post-sale support function. It is a revenue protection layer. That shift forces CS leaders to answer harder questions: Where is risk building right now? Which accounts look healthy but are quietly disengaging? Where is expansion hiding in plain sight? The answer, Gorman says, is an early warning system built on stitched data. https://youtu.be/sDdV747jBJA?si=fVE8O2bqTcf2yTeN The Anatomy of an Early Warning System Gorman is blunt about the prerequisite. “Data is non-negotiable. Full stop.” — Chad Gorman Before AI enters the picture, organizations must understand what their book of business actually looks like. Engagement Is the Most Underrated Risk Signal Product usage is table stakes. Engagement is the differentiator. Gorman emphasizes that many churn events are preceded not by usage decline, but by relationship decay. “If engagement drops and you do not notice, you end up ghosted and surprised later.” — Chad Gorman What Engagement Actually Means Engagement is not email volume or meeting counts alone. It is relationship depth across the buying group. Who shows up to meetings? Who stopped showing up? Which roles are missing entirely? Who influences decisions but never engages directly? This is where Gorman believes AI has its most immediate impact. Relationship Intelligence: Where Art Meets Science Gorman describes relationship intelligence as the intersection of human judgment and system-derived insight. “We think we know our accounts. AI shows us the white space we missed.” — Chad Gorman AI-Assisted Relationship Mapping AI can analyze: Calendar data and meeting attendance Email and collaboration patterns Role changes and stakeholder turnover Sentiment from meeting notes and transcripts At LivePerson, Gorman’s team increasingly relies on workspace-level intelligence using Google Gemini to surface patterns across meetings, documents, and communications. You can literally ask, ‘Who used to attend and no longer does?’ and get an answer.” — Chad Gorman Buy vs Build Is No Longer Binary Enterprise customers increasingly want flexibility. Some bring their own LLMs. Others rely on vendor-provided AI. Most land somewhere in between. Enterprise: Build and bring your own models Upper mid-market: Hybrid Down-market: Out-of-the-box AI The common denominator remains the same: clean, structured, accessible data. Embedding AI Into CSM Workflows Even the best insights fail if CSMs do not trust them. Gorman stresses three adoption levers: Data transparency Always link insights back to source systems. Prescriptive guidance Do not just flag risk. Recommend next steps. Respect experienceAI should augment gut instinct, not override it. Measuring AI Impact in Customer Success AI success is not measured by novelty. It is measured by outcomes. What’s Next: Agentic AI and Time Reclaimed The next wave, according to Gorman, is not better summaries. It is execution. The thing CSMs hate most is admin. AI agents that actually do the work change everything.” — Chad Gorman Examples include: Auto-generated QBRs with live data Scheduled reporting without manual pulls Automated follow-ups and task execution The payoff is not speed. It is reclaimed time for strategic customer work. Leadership Lessons From the Field When asked what advice he would give his younger self, Gorman’s answer is simple. “Know your book. Be curious. Admit what you do not know.”” — Chad Gorman Growth mindset Curiosity within and beyond the “box” Meticulous organization Executive presence Tight partnership with the AE  Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources

Andy Mowat
GTM, RevOps

Future of RevOps: GTM Systems, Hiring Tactics and Career Strategies

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A RevOps Playbook on the GTM Power, Careers and Hiring Strategies A conversation with Andy Mowat Executive Summary Andy Mowat has navigated the go-to-market journey from every angle—entrepreneur, operator at four tech unicorns (Box, Culture Amp, Carta), and now founder of Whispered. In this conversation, Andy shares hard-earned lessons on what separates strategic RevOps leaders from tactical executors, why the GTM tech stack is dying, how to take control of executive interviews, and why most people are dangerously cheap about investing in their careers. This isn’t theory. It’s a playbook built from someone who’s been the “wrong person for the job” four times and figured out how to win anyway. Readers will learn: RevOps is evolving from an execution function into a strategic GTM decision engine. The best RevOps leaders earn trust by forcing trade-offs, not by saying yes to everything. Legacy GTM stacks are breaking. Data fluency and AI-ready systems are becoming mandatory. GTM engineers are emerging under RevOps to automate execution and scale insight. Most executive roles are never posted. Senior hiring happens through networks and backchannels. Hiring favors builders who can get into the weeds, not just managers of managers. Strong candidates take control of interviews and show how they think, not just what they’ve done. Career leverage now comes from networks, reputation, and visible thinking, not applications. Facebook Twitter Youtube From Accidental RevOps to GTM Architect: Andy’s Career Arc Andy did not plan to end up in revenue operations. He stumbled into it the way many of the best RevOps leaders do. Early in his tech career, he joined Upwork. There was no CRM. So he built one. There was no outbound engine. So he figured out how to send a million emails. There was no formal RevOps function. So he became it. This pattern repeated. At Box, post-IPO, he took over post-sales operations, then marketing ops. At Culture Amp, he helped scale revenue from roughly $5M to $150M. At Carta, he entered during another inflection point, surrounded by leaders who understood that GTM decisions compound quickly, for better or worse. Across these roles, Andy learned something that most operators learn too late. RevOps is not a service desk. It is the economic engine room. The Real Difference between Tactical & Strategic RevOps Most RevOps leaders think their job is to execute requests efficiently. Andy believes that is how RevOps loses credibility. The inflection point in his thinking came at Box, when the company’s CCO told him “he’s not getting headcount unless the business gives it to him”. Instead of asking for budget, Andy began forcing trade-offs. He showed Sales, Support, and Customer Success how RevOps leverage could outperform incremental hiring. When leaders realized that one RevOps hire could unlock more growth than two frontline hires, budget appeared quickly. Andy’s rule is simple. If RevOps says yes to everything, it is not strategic.If RevOps forces prioritization conversations with executives, it is. “The wrong answer is ‘We got it.’The right answer is ‘Here’s the priority order I see. If we disagree, let’s take it to the CRO.” — Andy Mowat Where RevOps is Actually Headed Andy does not believe today’s GTM stack survives the next five years. He is tracking more than a dozen “CRM 2.0” challengers. His core criticism of legacy tools is structural, not cosmetic. Current GTM systems have: Clunky user experiences Data models not designed for AI Endless bolt-ons that fragment signal quality The Non-Negotiable Skills a RevOps pro Must Have Data literacy. Know what ETL and DBT actually do. Tight partnership with product and data teams. Embedded analytics and BI inside RevOps. Automation ownership, not tool babysitting. He also pushes back on the myth of the “GTM Engineer” as a shortcut. There is no shortcut. But there is a new function emerging. “GTM Engineers should live under RevOps.Their job is to automate the business and innovate for the reps.” — Andy Mowat https://youtu.be/H3CesaCmKWA What is Whispered? Whispered did not start as a company. It started as a survival mechanism. After a failed startup, Andy found himself asking a question many senior leaders never admit out loud.“Will anyone Hire me again?” At senior levels: Roles are rarely posted. Recruiters control access. Company quality is opaque. Networks go stale quietly. “Your next role won’t be posted. It’ll be whispered.” — Andy Mowat Whispered is designed for VP+ GTM leaders who are curious but cautious. It combines: Career playbooks Company backstory intelligence Unposted role discovery Network swarming across 300,000+ first-degree connections A private community that trades signal, not hype People join for roles. They stay for the network. Strategies for Hiring Senior Executives Through Whispered Hiring, Andy has interviewed dozens of CEOs, CROs, and CMOs about how they evaluate senior talent. Several patterns repeat. 1. Back-Channels Are the Highest Signal Everyone uses them. Everyone admits it. The best advice he heard recently: “Back-channel before you fall in love with a candidate.” 2. Builders Are in Demand Even at senior levels, companies want leaders who can still get into the weeds.AI has increased this expectation, not reduced it. 3. Rigid Thinkers Lose Andy calls it anti-rigidism, not ageism. Leaders who cannot adapt get filtered out quickly. 4. Slope Matters, but Only with Pattern Recognition High-growth companies love people who can outgrow their role. But leadership teams need both: Builders with slope Operators with scars Out perform your next Interview call Andy comments, treat your interview as a Sales call. Andy’s favorite interview opener is disarming. “I’m excited to meet you. What questions do you have?” Then he watches. Great candidates take control. Weak candidates wait to be prompted. Lazy questions kill momentum. Deep questions reveal how someone thinks. Interview Question Upgrade Instead of:“What’s your strategy?” Ask:“Here are three GTM constraints I see. Which one worries you most right now?” The goal is not to impress. It is to create signal. Personal Brand Without Becoming an Influencer Andy draws a line between thought leadership and performance. He writes because he cares and because writing clarifies thinking. It

RevOps

Building Revenue Engines that Scale: Lessons on Forecasting, Alignment and Multi-Product Complexity

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Building Revenue Engines that Scale: Lessons on Forecasting, Alignment and Multi-Product Complexity A conversation with Jeff Perry Executive Summary This article unpacks the operational blueprint behind scaling a revenue org from $15M to $500M+ ARR, while steering three parallel businesses at once. It’s distilled from an in-depth conversation with Jeff Perry, Chief Revenue Officer at Carta, who brings more than two decades of GTM leadership experience across Oracle, DocuSign, and Carta. Readers will learn: How Jeff’s career evolved through three distinct phases: growth, scale, and building What the “What’s Your Gut?” forecasting method reveals about improving accuracy Why cross-functional alignment is the real competitive advantage in revenue operations How to manage multi-product complexity with different ICPs and buying motions The characteristics that separate great sellers and managers from good ones Where AI fits (and doesn’t fit) in modern revenue operations   Facebook Twitter Youtube The Journey: Three Career Arcs That Shaped a Revenue Leader 1. Oracle: The Growth Phase Jeff started his career at Merrill Lynch depositing physical stock certificates—ironic given he now leads Carta, the company eliminating physical certificates. After a brief stint, he spent many years at Oracle where he learned: Sales fundamentals through structured training programs Leadership principles by observing great (and not-so-great) mentors Scale operations as Oracle grew from 30,000 to 120,000 employees Resources were abundant. Revenue operations, sales strategy, and training programs just happened. You operated inside a well-oiled machine. 2. DocuSign: The Scale Phase Jeff made what many considered a lateral or downward move: from leading 250+ AEs at Oracle to managing a 20-person SMB team at DocuSign. Why he did it: He needed to prove he could operate in a smaller, scrappier environment where: You’re hands-on with planning and execution Resources aren’t automatic You build the machine, not just run it Over four years, Jeff doubled his team size and took on additional verticals. This experience opened the door to Carta. 3. Carta: The Building Phase Jeff joined Carta in late 2018 when the company was at ~$15M ARR with 275 employees. Today: $500M+ ARR 1,800 employees Three distinct businesses operating under one roof “Sometimes you have to get one door opened up to lead to the next door. Oracle opened the DocuSign door. DocuSign opened the Carta door.” — Jeff Perry The Multi-Product Challenge: Managing Three Companies Inside One When Jeff joined Carta, it was a single-product cap table business. Today, it’s three distinct revenue engines. Venture-backed companies Venture Capital firms Private Equity The Growth Strategy: Classic Spreadsheet-to-Software Carta’s playbook: Identify a spreadsheet problem (cap tables, back office GL, ownership tracking) Build software to solve it Add adjacent products that create cross-sell and upsell opportunities “I can’t have one AE that sells cap tables to venture-backed C-Corps and fund administration to private equity firms. We’ve built teams within that align to the ICPs” — Jeff Perry Building Multi-Segment GTM Systems Without Chaos Each market Carta serves has its own logic: Startups care about cap tables, compensation benchmarks Venture firms care about GL automation, fund admin Private equity teams want scenario modeling and ownership accuracy The Challenge Different products have different: ICPs (leading to data segmentation issues) Sales cycles (transactional vs. enterprise) Buyer journeys Conversion benchmarks Example issue:A prospect reaches out to Jeff about their product. Jeff searches the CRM. The company isn’t there. Why? They were doing business under a different name. The data doesn’t connect. The RevOps Implication: As you add products with different ICPs, you can’t force unified systems. You need: Separate sales teams aligned to buyer personas Different quota structures Distinct sales motions (transactional vs. enterprise) The “Don’t Lose Alone” Philosophy: Why Lone Rangers Fail Jeff flips the classic advice: people say “Don’t try to be the hero.” He says don’t lose alone. Early-career reps want to be the savior who lands the big deal at the end of the quarter, makes a bunch of money, and gets recognized as the hero. Why this backfires: You limit your access to support and expertise Leadership lacks visibility to help you close You give yourself a lower probability of winning You don’t build career capital beyond the one deal The better approach: Involve your leadership team, product experts, and delivery teams early. “It does no good to be at the end of a quarter and say, I delivered this X hundred K deal and be the hero. You give yourself a better chance to win by involving the right people along the way.” — Jeff Perry Your future relationship builders are the SDRs learning your business today. If you automate away those roles, you lose the talent pipeline that becomes your future AEs and managers. Cross-Functional Alignment: The Secret Competitive Advantage Carta’s gone from 275 employees in 2018 to ~1800 today. Alignment usually decays with scale. But Jeff argues the opposite is possible, if leaders treat every metric like a shared asset. Marketing isn’t feeding leads to sales.Marketing and sales are feeding the same revenue engine. “Nicole, Carta’s CMO, doesn’t look at it as Jeff’s revenue number. She sees it as our revenue number.” — Jeff Perry Sales + Marketing: One Team, One Number The old model: Marketing owns lead generation Sales owns revenue Finger-pointing when numbers miss New model: Shared pipeline ownership Joint accountability for closed revenue Integrated planning across demand gen and sales capacity No classic sales-marketing friction Product isn’t building in a vacuum.Product is reacting to customers at lightning speed. He describes a moment at a Napa event: Prospects gave feedback at 11am.By evening the CPO confirmed engineers were already building on it. That’s alignment at operational speed. RevOps as the Connective Tissue Jeff’s perspective on where RevOps fits: It depends on company culture and structure, but: RevOps often has the clearest view of: The buyer journey Data flow across systems Change management needs Narrative resonance through metrics Many CEOs include RevOps in the small leadership group for exactly these reasons. The “What’s Your Gut?” Forecasting Method The problem with traditional forecasting is that most forecast calls

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