Eli Weiner

CFOs don't just report the results, they define how the business runs. As companies scale, the CFO becomes a central operator—owning the model, shaping GTM strategy, and driving alignment across teams. In a few conversation between John Rettig (BILL) and Rory O'Driscoll (Scale Venture Partners) a few takeaways stood out: - Financial leadership = GTM clarity. The baseline is being an objective scorekeeper. But the best CFOs are active partners in shaping pricing, headcount, and efficiency levers—not just tracking them. Strategy and finance aren’t separate lanes anymore. - Operational excellence is about alignment. Finance has a unique vantage point to connect the dots across sales, marketing, product, and ops. When metrics and models are consistent, execution gets tighter. - Trust is the foundation for real influence. Whether it’s planning tradeoffs or navigating tough calls, CFOs lead best when they’ve earned confidence from execs, boards, and teams before the pressure hits. More to come with a more thorough write up, but wanted to share some insights that resonated with our CFO community.

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Some Vertical SaaS companies are generating 𝘂𝗽 𝘁𝗼 𝟯𝟬% 𝗼𝗳 𝗻𝗲𝘄 𝗯𝗼𝗼𝗸𝗶𝗻𝗴𝘀 𝗳𝗿𝗼𝗺 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗲𝗾𝘂𝗶𝘁𝘆 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀. They figured out that PE firms can be a distribution channel if you build for it. That means products built for multi-entity environments, data that proves post-acquisition impact, pricing that scales through roll-ups, and teams designed to manage PE relationships over time. We break down how Vertical SaaS leaders engage PE firms across sales, marketing, product, pricing, and customer success in our 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗘𝗾𝘂𝗶𝘁𝘆 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸. The full checklist is in the comments. 👇

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RevOps has always been a data-driven function. From funnel mapping and CVR optimization to forecasting, territory design, renewal planning, quotas, and commissions; the list of data use cases is long. But here’s the irony: While RevOps spends enormous effort wrangling data from every source imaginable, the most foundational dataset —customer contracts— is often a complete mess. In recent conversations with RevOps leaders, I’ve seen a shift. A growing awareness of the operational debt caused by poor post-signature workflows. When contract data doesn’t flow properly into CRM and ERP systems, the downstream impact is brutal: Invoices go out with incorrect terms Renewals are missed due to wrong dates or terms Upsell opportunities are lost due to incomplete product data Quotas are set using inaccurate baselines Commission plans break Forecasts fall apart And that’s just the start. Historically, scaling the contract review process to fix this was operationally unmanageable; manual, error-prone, and too expensive. �� The good news? This is fixable. AI now makes it possible to automate contract data ingestion, validation, and system updates at scale. Smart RevOps leaders are leaning in, turning contract chaos into clean data, and reclaiming their role as the stewards of the single source truth. #RevOps #RevenueOperations #AI #OrderToCash #SalesOps #CRM #ContractIntelligence #ARR #DataQuality

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The agent stack is taking shape—but there's a missing layer nobody's talking about yet. Arvind Jain at Glean published a thoughtful breakdown of the emerging agent architecture yesterday: Models, Orchestration, Context, Agents, Interfaces, Security. One line stood out: "In past data eras, we only focused on recording the decisions, not understanding how those decisions were made." Glean's answer? Context graphs—infrastructure that captures the topology of work, how decisions flow through an organization, what agents touched and when. That's a massive step forward. But it exposes a deeper question: When AI agents make consequential decisions at scale—pricing exceptions, legal risk assessments, hiring calls, capital allocation—what happens to the reasoning behind those decisions? Context graphs capture how work flows. Agent traces capture what was executed. But why alternatives were rejected, what criteria mattered, what assumptions shaped the judgment—that doesn't live in workflows or execution logs. Six months later, when: Boards audit AI decisions Regulators ask "why did the system decide this?" Successor teams inherit agent fleets Patterns of failure emerge The reasoning isn't retrievable. It evaporated the moment the decision was made. This is the gap between decision execution (what Glean's orchestration layer handles) and decision reasoning (what institutional memory requires). The agent stack needs a reasoning preservation layer. Not explainability in real-time (models are getting better at that). Not audit logs of what happened (SaaS already does that). Not workflow documentation (context graphs capture that). Infrastructure that captures judgment at the commit boundary—so reasoning survives retrieval when the consequences play out months or years later. That's what we're building at Quantum Mosaic. Not to replace any layer of the agent stack—but to add the reasoning preservation layer that makes institutional memory possible when agents make thousands of consequential decisions daily. Because as Arvind Jain wrote: understanding how decisions were made requires more than capturing what was done. It requires preserving why. Here is the original post from Arvind Jain: https://lnkd.in/ga2Cy76T

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Kyle James makes a great observation about how GTM teams are approaching platforms like Gainsight today. What looks like hesitation is often discipline. Teams are taking time to pressure test workflows, data, and ownership before committing to a platform. That behavior does not diminish the value of Gainsight. In many cases, it increases it. When teams arrive with clearer intent and operational readiness, adoption improves and outcomes compound faster. The signal here is not about building instead of buying. It is about earning the right to scale with the platform once the foundation is actually in place. https://lnkd.in/emF9ZvCQ

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Your Cloud Infrastructure bill is the new headcount plan. For years, startup board meetings obsessed over hiring velocity to drive growth. Now we’re seeing a shift. CFOs are putting cloud spending in the spotlight. In the age of AI, the marginal cost of delivering value isn’t a new developer for a salesperson; it’s a token, a model call, or a GPU hour. That’s why we backed Matt Biringer and Yassine Açoine at North.cloud. Their AI-powered financial system helps high-growth startups and Fortune 500 companies alike manage cloud costs with the same visibility and rigor they bring to development, hiring, and GTM. • No FinOps headcount required • Massive savings in minutes, not quarters • 500%+ net retention, hundreds of millions of cloud spend under management • Customer DevOps teams can focus on... drum roll... development and deployment, not forensic cloud accounting exercises. Cloud COGS are becoming core to your business model. North makes them predictable & controllable. Here's why we led their Series A round and why you'll never go back after trying this solution in your cloud infrastructure: https://lnkd.in/eQFHp9ku #DevOps #CFO #venturecapital #FinOps #AIinfrastructure

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Even disciplined management teams experience software spend creep as licenses expand and subscriptions renew. This #GrowthBit by Taylor Koger and Claire King Krikawa shares advice on negotiating SaaS contracts and aligning spend with real value: https://lnkd.in/e6qEKB9q

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Yesterday Thoma Bravo announced it has reached an agreement to acquire HCM software provider Dayforce for $12.3 billion, including debt. I had a pleasure speaking with Dayforce's CEO David Ossip and Thoma Bravo's Tara Gadgil about how this take-private deal can help Dayforce expand on the its AI capabilities. "Going private gives us more space, flexibility and resources to go much deeper on what matters the most, which is accelerating our focus on being that AI leader in HCM," Ossip said, referring to the practice known as human capital management. More in my story for Reuters.

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Enjoyed talking with Matthew Volm on RevOps AF about why most CRMs have an identity crisis. We dug into the “data behind the data” problem, the fact that 87% of CRM records have errors (based on data Kernel has fixed), and how that inaccuracy impacts territory design, account allocation, and rep productivity. I also went through the three-layer data stack: entity, enrichment and orchestration, and why getting the entity layer right is critical for your GTM strategy. If you want to avoid garbage-in, garbage-out and make your CRM ready for automation and AI, this episode is worth listening to. Link in comments.

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PE partnership is an emergent GTM channel that’s proving to be massively successful. Unlike traditional sales motions, PE is a 1-to-many motion—but with that comes a major risk. Michael Belkin, Head of Global Private Equity Practice at Zuora, broke down the pros and cons. The major con? Companies get sold. Often. So, let's say your SaaS product is used by 6 PortCos backed by a partner PE firm. 2 of them sell, and the new owners favor their incumbent SaaS product over yours. Overnight, you’ve lost 2 clients. Since the new owners are not necessarily your customers, traditional client retention tactics won’t work. The benefit of working with a PE firm is that you’re getting warm intros all the time…but when a PortCo sells, you might be left out in the cold. Pursuing a PE-partner GTM strategy means you have to build an expectation of controlled churn into your process—especially if you’re building your comp plan around the total amount of PE-owned ARR. That’s why it’s critical to track where your clients are in the PE lifecycle. You want to keep a healthy balance across the PE investment cycle so you don’t end up with multiple clients rolling off all at once. But, just as PE taketh away…PE giveth. Say you have companies on your books that get bought by other PE firms. Then you have an opportunity to begin a partnership motion with a new firm, opening up other avenues of business. Functionally, that means identifying opportunities by closely monitoring PitchBook data, tracking which strategic accounts are owned by PE for each AE, and zeroing in on actionable relationships. PE partnerships are relatively new, but are already proving fruitful to some. It will be interesting to see which companies successfully integrate this into their GTM playbook—and which ones fall prey to the relentless churn. Listen to the full episode with Mike Belkin on “The Data Room.” Link in comments.

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VC: "I don't understand how you invest in CPG. There are no moats like in enterprise software." Also VC: "I rebuilt our firm's CRM with Claude Code over the weekend and we're cancelling Salesforce." For years, the narrative was simple. Software = defensible. CPG = commoditized. Recurring revenue, integrations, switching costs; that was the holy trinity of moats. But if your internal tools can be rebuilt in 48 hours...what exactly was the moat? Al is compressing build time. Features are replicable. Code is cheaper. Meanwhile, the things that looked "soft"; brand, distribution, shelf space, community; are proving stubbornly hard to copy. This isn't about dunking on software. It's about updating the definition of defensibility. If a product can be cloned quickly, your moat isn't the product. It's everything around it. So maybe the better question isn't: "Is it CPG or enterprise?" It's: "What actually compounds in a world where code is abundant?"

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PE Growth TAILWIND: 3 GTM Metrics PE-Backed Teams in the Lower Middle Market Underuse - And Why It Matters in 2026 In PE, growth isn’t just an outcome - it’s the primary mechanism for value creation. Research consistently shows organic revenue growth drives more than half of total PE value. Which means every GTM decision either compounds that value or quietly erodes it. At a high level, PE firms aim for the same 3 growth outcomes: • Accelerated revenue growth without proportional cost • More predictable customer retention & expansion • A faster, more efficient path to profitable growth The mistake? Treating these as results to monitor, rather than outcomes that can be deliberately ENGINEERED using the right GTM metrics. Everyone tracks growth metrics. Few use them as growth levers. And in 2026, there are three GTM metrics I’ll watch most closely across PE-backed businesses because they directly enable faster, more profitable growth. These shouldn’t just be reviewed in board meetings. They should shape how teams plan, sequence & prioritize growth initiatives: 1. Sales Velocity - How fast revenue moves through the pipeline • Elements: opportunity volume, win rate, deal size, sales cycle • Why it matters: breaks revenue into controllable levers. Small gains compound into faster growth & better execution efficiency • How to use it: identify bottlenecks & prioritize initiatives that improve conversion, deal size or cycle time 2. Net Revenue Retention (NRR) - Retention plus expansion in the base • Elements: renewals, upsells, customer churn • Why it matters: protects existing revenue & compounds growth before adding customers • How to use it: forecast predictable revenue & target onboarding, adoption & expansion programs to lift NRR 3. Gross Margin-Adjusted CAC Payback - True profitability of growth • Elements: CAC, gross margin contribution, payback • Why it matters: shows how quickly gross profit covers acquisition cost • How to use it: set targets for profitable acquisition, reduce CAC or improve margin & benchmark acquisitions And these metrics map directly to the GTM mechanics driving PE value creation: • Sales Velocity determines how efficiently new revenue compounds • Net Revenue Retention shows whether retention & expansion turn the installed base into a predictable growth engine. • Gross Margin-Adjusted CAC Payback enforces profitable growth discipline by tying acquisition spend to real gross profit. Together, they link daily GTM execution to what sponsors care about most: efficient growth, durable revenue & scalable profitability. Which GTM metrics will you watch most closely this year? 👍 React 💬 Comment ♻️ Repost ➕ Follow Brian Gustason for more PE growth insights 🔗 Access 250+ PE Growth posts: https://lnkd.in/egaTG95M #PrivateEquity #GTM #OperatingPartner

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Salesforce announces purchase of Informatica to accelerate its move into an AI-first world 🌍 After talks fell apart for a $10B+ acquisition of Informatica in 2024, Salesforce is now acquiring the company for $8B, a 30% premium to Informatica's current market valuation. Salesforce was early in its realization that owning the data layer was going to be the scaffolding for the entire enterprise software stack, and this acquisition is the next big move in owning that. The real story is how this acquisition fits into their AI operating system strategy, especially around Data Cloud and Agentforce, Salesforce's fastest-growing segment at 120% YoY. Going a bit deeper: For AI agents to work safely and autonomously, you don’t just need more data. You need clean, contextualized data that comes with best-in-class access management and security. Guess where Informatica shines? Enterprise-grade data governance Master data management capabilities Deep pipelines into legacy systems and cloud environments Tools for unifying data across silos In its next act, Salesforce aims to go beyond being a system of record and deploy autonomous agents that act across workflows, including sales, service, and marketing. Doing that with trust and scale requires a real-time, governed, enterprise-wide data layer. There is an argument that this is the final piece of the stack: MuleSoft integrated systems. Tableau visualized them. Data Cloud centralized them. And now, Agentforce will act on them. Informatica will make those actions safe and reliable. While there will be tons of work between now and that vision becoming a reality, you can see a path to Salesforce becoming the first true enterprise AI operating system. Strong move #CRM!

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In most B2B companies, the gong rings in sales and a fire drill starts in finance. Sales is signing custom deals while finance scrambles to figure out how to bill, invoice, and recognize the revenue. Quote to cash is one of the most fragmented processes in the CFO org. Legacy billing tools were built for v1 of the subscription economy. They handle simple subscriptions, then break the moment you add custom enterprise terms and usage-based pricing. In an AI world where everything is metered and usage-driven, that setup is unsustainable. That’s the gap Sequence is going after. Their end to end AI revenue platform is built for this reality, handling real-world Q2C complexity while automating the tedious work and keeping finance in control. An intake copilot turns messy custom contracts into billable schedules. A Rev Rec agent flags ASC 606 risk before entries hit the ERP. A Dunning agent tailors follow ups instead of spamming generic “please pay” emails. Huge thanks to Riya Grover, Enda Cahill, and the Sequence team for an incredible session. We’re grateful to everyone who joined and shared their questions. That wraps our final Scale CFO Demo Day of 2025, what a year! We’ll be back in the new year with more sessions and initiatives across the CFO community. If you’re interested in joining, link in the comments.

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Seismic announced major advancements in our partnership with Salesforce! 📣 Now available on AgentExchange, Seismic Aura AI integrates directly with Salesforce Agentforce to help revenue teams boost productivity, accelerate deal cycles, and deliver greater value in every client interaction. "Go-to-market teams don't just need automation – they need AI that drives action and results. That's what our expanded work with Salesforce delivers," says Chief Revenue Officer and President, Hayden Stafford. We're proud to be among the first partners delivering enterprise-ready Agentforce solutions – and even more excited about what's ahead. Read more in Seismic's Newsroom here: https://lnkd.in/gDQyQduT ​

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A line from the Financial Times stopped me in my tracks today. 🔥 “Nobody’s going to replace payroll with AI. Payroll has to be right.” 🔥 That quote comes from Orlando Bravo of Thoma Bravo, in the context of why specialized software still matters even as AI reshapes everything else. And he’s exactly right. Payroll isn’t a “nice to have” workflow. It’s not experimental. It’s not something you iterate on in production. If payroll is wrong, nothing else matters. That’s why payroll remains one of the most defensible, domain-driven categories in software: Deep regulatory and compliance complexity. Zero tolerance for errors. High trust requirements. Institutional knowledge built over decades Now don't get me wrong - AI will absolutely augment payroll - catching exceptions, flagging risk, improving visibility [we are doing many of these Netchex] but it won’t replace the core system of record. The stakes are too high. This is also why serious investors continue to back payroll platforms with real expertise. The value isn’t the code. It’s the domain knowledge, the process mastery, the support and the confidence that every employee gets paid correctly and on time. In a world obsessed with what AI can replace, payroll is a reminder of what can’t afford to break. https://lnkd.in/gZVmH99Q

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As of February 4, Thoma Bravo has completed its ~$12.3B takeover of Dayforce, delisting the company from NYSE/TSX. For Dayforce, its return to being a private company (it originally went public in 2018), this will likely make a notable impact on its strategic posture and ability to invest through multi-year cycles. Without quarterly earnings volatility shaping every decision, returning to being a private company may mean a return to longer-term investments in AI-driven products and delivery, payroll/time compliance depth, implementation tooling, and global expansion. The things to be cautious of are the typical ones that come with private equity ownership. There will likely be greater scrutiny of efficiency and predictable growth to make a successful selloff in the coming years, so shrewd customers should monitor service levels, pricing discipline, and roadmap transparency. Competitors should anticipate a more “leaned-in” version of Dayforce, featuring faster release cycles, more compact vertical packages, and more acquisitions. This could put pressure on companies like Workday, UKG, ADP, and others to quickly demonstrate clear differentiation and openness within their ecosystems and is part of the broader industry-wide shift towards delivering tangible results rather than shiny new tech. #HRTech #HCM #FutureOfWork #Dayforce 3Sixty Insights

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Five9 has named Amit Mathradas as CEO. The choice tells you everything about where this is headed. Mathradas scaled Avalara from $272 million to nearly a billion in revenue. Vista Equity took it private for $8.4 billion. He then moved to Nintex—TPG and Thoma Bravo backed. His entire career has been preparing software companies for private equity transactions. He has no contact center experience. None. Meanwhile, Five9 turned down Zoom's $14.7 billion offer in 2021. Today the company trades at $1.5 billion. Revenue growth dropped from 17% to 8%. They've done three rounds of layoffs. Anson Funds has a board seat. The new CEO's compensation includes double-trigger acceleration on a change of control. His equity pays out if Five9 gets acquired and he's terminated. Five9's AI revenue is growing—42% year-over-year, about $100 million annually. That's real. But Forrester called their agentic capabilities "still immature." NICE, Genesys, and Amazon Connect are shipping at the same pace. The cloud contact center pioneer built the category. Now it's a mid-market asset with PE-friendly leadership and a board structured for optionality. This is what late-stage consolidation looks like.

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