RevOps Growth Approaches

In 2025, the RevTech noise is deafening. Everyone’s pushing new tools and AI promises. But the real question for MOps pros isn’t what’s new—it’s what’s working? A few trends we’re watching closely: Integration is now table stakes. It’s not “can it connect?” anymore—it’s how seamlessly. If your tools need spreadsheets to bridge the gaps, that’s a liability, not a solution. AI is only as smart as your systems. Early wins are coming from AI-powered enrichment, deduplication, and lead routing—but they only work when your data is clean and structured. No amount of AI fixes a broken foundation. Ownership is shifting back to MOps. More teams are pulling RevTech admin responsibilities out of IT and sales ops and returning them to where they belong—inside the go-to-market motion. Metrics are finally evolving. Pipeline impact has replaced MQLs. Teams are tracking time-to-pipeline, stack ROI, and attribution confidence. If your stack can’t support these, it might be time to rethink the setup. These shifts aren’t about chasing trends. They’re about tightening alignment and building for scale. Let’s talk about it. Join the conversation inside the Marketing Ops Community. #RevOps #MarketingOps #Martech #GTM #RevenueTech #MOPro #TechStackStrategy

As SaaS companies scale, operational complexity multiplies. The key question then: Is your marketing and sales machinery keeping pace? I've been watching the RevOps space evolve from marketing curiosity to business necessity. What is RevOps in a nutshell? • Centralised systems like CRM & revenue intelligence tools eliminating data silo. • Shared KPIs between marketing, sales & customer success. • A focus on end-to-end visibility across the full customer journey • Process automation and standardisation across departments • Proactive identification of growth opportunities & streamlined analytics to spot revenue leaks early • Tech stack alignment and integration The operational gains are material. What's interesting is how RevOps transforms existing resources. Companies with mature RevOps functions are 2.3x more likely to exceed profit goals. BCG research shows RevOps adopters achieve 36% more revenue growth. LinkedIn has roughly 9 million active marketers but only 9,000 RevOps specialists. Still nascent; investing early in this function can prove a competitive advantage in newly forming categories. Companies report 30% reductions in go-to-market expenses and 10-20% increases in sales productivity through automated workflows. New tech fragmentation amplifies the need for strategic alignment between marketing, sales, and customer success. But it also seems to be serving as the solution to the complexity it's creating. Gartner predicts 75% of high-growth companies will deploy a RevOps model by 2025. Then again some of their predictions are on the ambitious side - many businesses lagging behind due to an overwhelm of challenges on multiple fronts. A16z's data suggests that as organisations mature, Account Executive to RevOps ratios should scale from 5:1 to 10:1. This reflects the increasing importance of operational efficiency as complexity grows. Deloitte Digital found orgs leveraging RevOps are 1.9x less likely to struggle with pipeline/forecast challenges. Early adopters of RevOps are clearly hot on new tech - Deloitte notes that RevOps-driven companies are 2x more likely to deploy generative AI for personalised customer interactions and predictive analytics. For SaaS businesses with ambitious targets, RevOps is becoming essential for scaling. Think of it as a multiplier of all the other activity in your GTM engine. If you're struggling with pipeline visibility, attribution challenges, or operational friction between your customer-facing teams, perhaps it's time to look at RevOps.

I've had some version of the same conversation about 20 times in the last few weeks across the AI founder dinners John Collison and I host, CFO roundtables, and a run of 1:1s with AI leaders. Here's what keeps coming up: 🚀 The pace is accelerating and it feels structurally different – planning cycles are collapsing or altogether disappearing. Last year felt fast. This year feels like a step change. Patrick Collison said we may look back on this as “the first quarter of the singularity” and that really resonates. Across the board: faster revenue ramps, compressed build cycles, and decisions happening in days, not weeks. One symptom (and arguably root cause?): when model capabilities shift every few months, long-term roadmaps don’t hold. Many teams are operating in short, iterative cycles with more flexible resourcing and a higher tolerance for rework. Finance leaders in particular are rethinking how to plan headcount, token, and infrastructure spend when all prior assumptions change so frequently. 🚂 Leadership hiring is the primary bottleneck. In multiple recent CEO conversations, leadership hiring was the #1 priority. CRO is the most in-demand role, with People leaders close behind to help teams scale without breaking. For people hiring these roles, differentiation is key – the best CROs I know are getting multiple reachouts a week and are weighing the pros and cons of various companies. ✍ Enterprise sales is being pulled forward Companies are building PLG and SLG motions almost simultaneously. Even highly technical, developer-first teams are hiring sales earlier. We’re seeing new team structures emerge with1:1 (and even 2:1) Solutions Engineers to AEs, reflecting how technical these products are and how much the sales process has changed. I imagine the next wave of problems for companies to solve will be building up strong Rev Ops functions to handle the pace of evolution across sales targets, AE quotas, and team evolutions. 💲 Pricing is still unsolved and increasingly strategic The shift toward usage-based or hybrid models continues – we see that 68% of the fastest growing AI companies are doing hybrid or usage based pricing – seats and standard subscriptions are going away. That said, the single playbook has not emerged. The core tension: balancing simplicity and predictability for customers with the reality of usage-driven costs. We’re seeing more active experimentation,especially as enterprise sales motions ramp. 🛡️ Fraud is evolving as quickly as the products Every fast-growing AI company I've talked to is seeing new attack vectors—free trial abuse, agent workflow exploits, patterns that didn't exist 12 months ago. The old playbooks don't hold. AI is changing how companies are building and operating, and the playbook is written in real time. Curious what others are seeing!

We're entering a new era of 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (RevOps) where the game-changing focal point of go-to-market (GTM) strategy will no longer be about brute force—but precision, process, and continuous improvement. Let me paint you a picture of how yesterday’s practices are evolving into tomorrow’s winning playbooks. ↳ 𝗚𝗿𝗼𝘄𝘁𝗵 𝗮𝘁 𝗮𝗹𝗹 𝗰𝗼𝘀𝘁𝘀: Throw money and people at the problem. ↳ 𝗦𝗶𝗹𝗼𝗲𝗱 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁𝘀: Sales, Marketing, and CS operating on different planets. ↳ 𝗜𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗵𝗲𝗿𝗼𝗶𝗰𝘀: Growth driven by your best reps, not by the system. ↳ 𝗢𝗽𝗶𝗻𝗶𝗼𝗻-𝗯𝗮𝘀𝗲𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: Leadership trusted gut feel more than data. ↳ 𝗟𝗶𝗻𝗲𝗮𝗿 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴: Solve one problem at a time without considering downstream impact. Did it work? Sure, until it didn’t. Tomorrow’s Playbook: ↳ 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗴𝗿𝗼𝘄𝘁𝗵: Efficiency > Hustle. Measured outcomes matter more than vanity metrics. ↳ 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗚𝗧𝗠 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: Revenue teams act as a single, aligned unit. ↳ 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲: Revenue factories replace the rollercoaster of heroic effort. ↳ 𝗗𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: From gut feel to rigorous diagnostics and benchmarks. ↳ 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴: Solve for 𝘦𝘯𝘵𝘪𝘳𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴 to create compounding returns. The winners will be those who don’t just "train their people" but who 𝗯𝘂𝗶𝗹𝗱 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀 powered by scientific principles, 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, and relentless iteration. The chart above says it all: open-loop systems (people-dependent) dissipate knowledge over time, while 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻, 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 accumulate it. This is RevOps’ time to shine. What Changed? ↳ Businesses can no longer afford endless cost bloat in GTM. ↳ AI is transforming what “good” looks like in data analysis, process execution, and learning systems. ↳ Leaders want repeatable systems, not unreliable heroics. What do you do? ↳ 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘆𝗼𝘂𝗿 𝗚𝗧𝗠 𝗴𝗮𝗽𝘀: Analyze KPIs, processes, customer journeys, and call data to pinpoint inefficiencies. ↳ 𝗕𝘂𝗶𝗹𝗱 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝘀𝘆𝘀𝘁𝗲𝗺𝘀: Use AI to continuously improve processes, playbooks, and skills. ↳ 𝗧𝗵𝗶𝗻𝗸 𝘀𝘆𝘀𝘁𝗲𝗺𝘀-𝗳𝗶𝗿𝘀𝘁: Optimize not just individuals, but the revenue engine as a whole. 🌶️ take:  While we’re trimming the fat across Sales, Marketing, and CS teams, RevOps will 𝘀𝗰𝗮𝗹𝗲 𝘂𝗽. Why? Because they’re building the GTM machine—and in the new AI era, that’s the only way to win. The age of opinion-based GTM is over. Sustainable growth will come from 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗚𝗧𝗠 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝘀, 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗹𝗼𝘀𝗲𝗱 𝗹𝗼𝗼𝗽𝘀, 𝗮𝗻𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. The RevOps teams that get this right will lead their companies to dominate. Are you building a RevOps-led revenue factory, or still stuck in yesterday’s playbook? Time to level up.

It looks like we’re quietly recreating the “growth at all costs” era. But this time around, it’s disguised as AI. Coming out of the 2020s, the SaaS industry went through a much-needed reckoning. We all started paying closer attention to the Rule of 40, durable growth, and scalable business models. But with the explosion of AI startups and complex usage-based pricing, it feels like we’re sprinting right back into the fire. Here’s the fundamental shift: traditional SaaS enjoyed 80% to 90% gross profit margins because software scaled beautifully. You wrote the code once, deployed it, and sold it a million times. AI is different. Every single time a user interacts with a product, there’s compute attached. For the first time in a while, software companies are dealing with a heavy Cost of Goods Sold. As a result, AI companies are operating at 40% profit margins (or worse) as they race to add revenue and market share, burning cash on compute costs to do it. This creates a visibility problem. The issue isn’t just the shrinking margin itself, it’s the disconnect in the data. Today, costs live in one system, and billing lives in another. To be honest, no one’s bridged this gap so far. But as long as that disconnect exists, figuring out your true margins, profitable customers, and sustainable pricing models is going to be a challenge. RevOps teams are under massive pressure to iterate on usage-based and hybrid pricing models to keep up with AI demand. But when billing is disconnected from actual cost data, companies risk scaling a loss leader without realizing it. This gap inevitably hits finance and the boardroom. CFOs need investor-grade financial data to guide their companies. Without a single source of truth, board meetings stop being strategic and devolve into debates over data accuracy. So where do you stand? Do you actually know your true margins at the customer level? Can you tie usage directly to cost and revenue in real time? Or are you scaling faster than your visibility? What are you doing right now to close that gap? #AI #SaaS #PricingStrategy #UsageBasedPricing #RevOps #CFO #FinOps #UnitEconomics #GrossMargin #B2B #Startups #Growth

I gave a talk on #TheScienceOfScaling at #SXSW. We covered the full framework - how to know if you're ready to scale, how fast to go, and how your go-to-market system needs to change at each stage of the journey. My last slide was one of the most important. I talked about what I believe to be the implications of AI on the framework. Three things I believe are changing: 1) Specialization is reversing. The hyper-specialized GTM model of the last two decades, SDR, AE, CSM, AM, each owning a narrow slice of the customer journey, was designed for a world where humans were the primary unit of scale. AI doesn't need that architecture. We're already watching it happen in engineering. 2) Revenue organizations flatten with more reliance on RevOps The manager ratio is shifting dramatically. Ratios that sit around 7:1 today will move closer to 20:1. And as the team gets smaller and more AI-leveraged, the most important person in your GTM org shifts from the CRO (human manager) to the SVP of RevOps (process/tech/data manager). 3) Two metrics will tell you how you're doing by the end of 2026. Selling time (the percentage of a rep's time actually spent in front of buyers) should be approaching 75%. Manager ratio should be trending toward 15:1. The result will be a 2X PPR by end of year. Watch the full talk on YT: https://buff.ly/uzfQoyw

𝐆𝐓𝐌 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 𝐖𝐡𝐲 𝐑𝐞𝐯𝐎𝐩𝐬 𝐈𝐬 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐭𝐨 𝐁𝐫𝐞𝐚𝐤 The CRM is clean. The dashboards are live. The forecast is held together. And none of it is enough anymore. Here's what's actually happening: RevOps job postings dropped 19% last year. "GTM Engineer" roles went from near zero to 1,400+ open positions. This isn't a title debate. It's a structural shift. For a decade, RevOps scaled SaaS by bringing order to chaos. That era isn't ending, it's being absorbed. AI now enriches leads, drafts outreach, flags churn risk, and suggests next actions. The work that defined us is becoming table stakes. And yet, fewer than 10% of GTM teams report real ROI from AI. Not because the tools aren't there. Because the "systems thinking" isn't. That's the real gap. I keep seeing the same three tensions in every org: 🔴 The strategic trap: RevOps leaders who know they can operate at a higher level, but can't escape the reporting queue long enough to get there 🟡 The execution gap: GTM leaders whose revenue engine is too manual, too slow, too noisy — but can't quite bridge to what's next 🟢 The builder itch: Operators who don't just want to use AI. They want to build something-composable, intelligent, and compounding over time The best way I can describe this shift (at least for now) is GTM Engineering. Not a rebrand, and not hype. A different center of gravity: ⚙️ Maintaining tools → designing systems 🧠 "What report do you need?" → "What should happen automatically?" 🔁 Ad hoc automation → composable workflows and agents that improve over time RevOps runs the business. GTM Engineering builds the revenue machine. Both matter. But they are not the same job, and the orgs that figure out the difference first will pull ahead fast. Here's what makes this moment harder than it looks: early-stage companies can build AI-native from the ground up. Companies at scale can't. They're navigating layered systems, legacy complexity, and org inertia that doesn't move quickly. Which means this transformation isn't just about tools. It's about rethinking how the entire GTM engine is designed - across the full customer lifecycle, from first signal to renewal. That's the part most of the RevOps-vs.-GTM-Engineering debate completely misses. This is the start of a short series on GTM Transformation: what's breaking, what's evolving, and how teams can actually move forward. GTM Engineering is one thread. Unified lifecycle design is another. So is the question of who owns what when AI is doing half the work. One question before I write the next piece: What's the biggest friction in your GTM motion right now? 🔴 People & change management 🟡 Strategy & business outcomes 🟢 Translating strategy into workflows & systems Drop it below — I'll shape what I write next around what I hear.

There's a role emerging in B2B that doesn't have a clean job title yet. Some are calling it the GTM Engineer (shoutout Clay). Some Growth Operations. Others see it as the next evolution of the Revenue Operations leader. Whatever you call it, the need is the same: someone who can take an organization from using AI as a glorified search engine to running AI-forward systems with real governance and adoption from CEO to IC. And the talent shortage is massive. We've seen more and more PE firms mandating AI implementation across their portfolios. But who takes the mandate and turns it into a functioning system? Not a data analyst. Not an innovation lead. An operator who can architect AI into the way the business actually runs. The challenge is that most AI initiatives never get past the experimentation stage. Every department runs its own experiments and nobody is thinking about how it all connects to the data warehouse, or governance, or adoption across the org. PwC's research on AI in PE puts it clearly: the binding constraint isn't the technology, but whether people know how to use AI in their specific jobs, have permission to learn, and whether the system rewards the change. Solving that is an operational challenge, and it requires someone who lives at the intersection of strategy and execution. This is where the RevOps function has a structural advantage that the market is starting to recognize. RevOps professionals think natively in four pillars -- data, process, tools, and people -- which is exactly the framework you need to implement AI in a way that sticks. They understand the business end-to-end. They sit at the intersection of sales, marketing, and customer success. They think in systems, not silos. They straddle the tactical and the strategic. The GTM Engineer trend is related but distinct. It's a tactical, execution-heavy role who orchestrates AI tooling so one SDR can do the work of five. That's valuable, especially at earlier-stage companies. But the role the market is missing operates at a different altitude. It's someone who can connect AI to your data infrastructure, redesign processes around it, drive adoption across teams, and put governance in place to avoid a compliance nightmare. That's less "engineer" and more "strategic architect." From what I'm seeing, AI implementation isn't a technology initiative with an IT owner. It's an operational transformation that needs a RevOps-minded architect at the center. Curious what others are seeing. Who is owning AI implementation internally at your company? What do you call this role and where does it sit inside the org? #AI #RevOps #GTMEngineer

🚨 Your RevOps stack is too rigid. Scott Brinker’s “systems of context” vs. “systems of truth” framework isn’t just a martech evolution; it’s a fundamental shift in how RevOps will structure and operate their tech stacks. For years, revenue operations has been caught between systems of record (CRM, CDP, DW) and systems of engagement (SEP, MAP, BI tools). But AI agents are now pushing us toward a new paradigm: Systems of truth → The single source of reliable data in your org; the backbone that ensures data integrity across teams. Systems of context → While systems of truth ensure correctness, they don’t activate or interpret data dynamically; instead, they adapt dynamically to different jobs to be done. Examples in action ✅ CRM + CEP → AI-powered sales & customer engagement A customer-facing AI agent pulls first-party data from a CRM (truth); enriched by a CDP (context); and orchestrated by a CEP (like Outreach or Braze) to trigger perfectly timed outreach. ✅ MAP + AI → Hyper-personalized nurture flows A MAP (like Marketo or HubSpot) dynamically adjusts email nurture sequences based on AI-driven engagement scoring; refining audience segmentation in real time to optimize conversion. ✅ CEP + AI chatbots → Proactive customer experience & expansion A CEP (like Intercom or Drift) listens to in-app user behavior; triggering AI-powered chat interactions that surface support, expansion, or upsell opportunities exactly when needed. What does this mean for RevOps teams? ✔️ Data needs a governance model. If systems of truth aren’t properly structured, systems of context will fail. ✔️ AI-driven orchestration is the future. AI agents will not just analyze data but automate and trigger workflows. ✔️ GTM tech stacks must be designed for agility. Static tools won’t cut it; flexible, composable architectures will. RevOps isn’t just about managing tools anymore; it’s about architecting systems that adapt dynamically AND proactively to every GTM motion.