Kevin Gough

Post-close GTM breakdowns aren't usually about founder capability. They're about structural shift. Founder-led companies often grow through instinct, relationships, and product intuition. Post-close, expectations shift toward repeatability, process discipline, and scalable execution. Without clear alignment around new GTM roles, cadence, and enablement infrastructure, friction naturally follows. The issue isn't leadership. It's transition. PE firms that proactively support founders with GTM clarity; defined processes, role design, and structured enablement, see faster stabilization and stronger post-close acceleration. At SFE Partners, we help bridge the gap between founder-driven momentum and operator-led scale, building the GTM playbooks and infrastructure that turn traction into predictable growth. Learn how we support founder-to-operator GTM transitions at https://bit.ly/3Mzjf53 #PrivateEquity #GTMStrategy #RevOps #ValueCreation #SFEInsights

1

"Don't Hitch Your Dinghy to the Titanic" - A Lesson That's Aged Like Fine Wine Back in 2010, I read an article from Futurescale, Inc that fundamentally shaped how I think about building software. They warned against over-coupling to platform-specific features, using Adobe Flex as the cautionary tale. Sixteen years later, that lesson hits even harder. The author watched Flex die exactly as predicted. Teams had to burn down massive applications and rebuild from scratch because they'd married themselves to platform magic - compiler directives, binding systems, dependency injection frameworks that only existed in that one ecosystem. Today, we're seeing the same pattern with AI build-and-ship platforms. The promise is intoxicating: ship faster, build more, let the platform handle the complexity. But here's what I've learned to ask first: What are my portability options? What are the costs of leaving? Because platforms come and go. Corporate politics shift. The hot framework of 2024 becomes the legacy nightmare of 2030. The principle that survives every technology cycle: architect for portability. Use proven patterns. Minimize platform-specific magic. Keep your core logic separate from the platform boundaries. Whether it's cloud providers, AI platforms, or the next shiny framework, understand the exit costs before you commit. Your future self (and your clients) will thank you. What excessively expensive over-coupling have you experienced that needed dismantling? https://lnkd.in/geYdh_k7 #SoftwareArchitecture #TechDebt #AIEngineering

5

MIT’s recent report found that 95% of enterprise AI pilots fail to deliver ROI, with value trapped in workflows, slow scaling, and misplaced bets in sales and marketing instead of back-office and integration wins. But these are symptoms, not the root cause. The real issue is strategic misalignment, as oftentimes organizations don’t clearly define the kind of bet they’re making. At the core, every pilot is a capital allocation and risk management decision. Pilots fail without clarity on the business angle, hypotheses, and go/no-go criteria. To address this, I’ve developed The Pilot Strategy Framework™, a structured approach to categorizing AI pilots based on their strategic intent and business impact. 1️⃣ Validation pilots - validate feasibility before scaling and committing further resources. 2️⃣ Internal adoption pilots - phased rollout of AI within departments, inside existing workflows, to see if it can unlock ROI. This can often signal market readiness to customers, competitors, investors, and the industry overall. 3️⃣ Customer-facing pilots - test whether existing customers will adopt new AI capabilities and whether it’s commercially safe to scale. 4️⃣ Legacy integration pilots - answer if AI can unlock value in legacy systems. 5️⃣ New product pilots - test whether additional AI offerings can be introduced; offerings that are core or adjacent to our current products and generate new ROI. Each pilot is a bet: upfront decide how much capital to risk, what value it’s meant to unlock, tradeoffs to consider, and the conditions under which you’ll scale or stop. Ultimately, it's about prioritizing the one(s) that provide the most enterprise value. Which pilot types is your organization running, and how are they moving the needle on your business outcomes? I am currently talking to leadership teams to apply this framework to drive clarity and ROI; if your organization is navigating similar questions, I'd be happy to connect. #productstrategy #aistrategy #aitransformation

4

1 Comment

Just finished diving into the Pavilion Benchmarkit 2025 SaaS Performance Benchmarks report, and it's packed with crucial insights for anyone in the B2B SaaS space! A few key takeaways that really stood out: - New Customer CAC Ratio on the Rise: The median New Customer CAC Ratio hit $2.00 in 2024, meaning it costs $2.00 in Sales and Marketing expenses to acquire $1.00 of New Customer ARR. This is a 14% increase from 2023, highlighting a growing challenge in efficient new customer acquisition. - Growth Rates are Declining: Median growth rates decreased to 26% in 2024, down from 30% in 2022 and 27% in 2023. Even top quartile growth dropped from 60% in 2023 to 50% in 2024. - Expansion ARR is Critical: Expansion ARR now represents 40% of Total New ARR, a 5% increase in 2024. For companies with over $50M ARR, Expansion ARR accounts for more than 50% of Total New ARR. - Usage-Based Pricing Outperforms in GRR: Companies primarily using Usage-Based Pricing showed a Gross Revenue Retention (GRR) of 92% at the median, compared to 88% for traditional subscription and hybrid models. Private vs. Public R&D Spend: Private SaaS companies are investing significantly more in R&D, with a median of 34% of revenue, while public SaaS companies invest 23%. It's clear that the landscape is evolving, with a greater emphasis on efficiency and existing customer expansion. Definitely recommend giving the full report a read! https://lnkd.in/duvm-iDw

5

Why should Partnerships operate differently than the rest of your SaaS GTM org? 🤔 Too often, I see partnerships teams build bespoke processes to recruit and onboard partners—disconnected from how the rest of the organization operates. It’s well-intentioned, but it creates silos. Other teams lack visibility. Alignment suffers. And ultimately, so does growth. But here’s the thing: Partnerships can and should operate in lockstep with the broader GTM motion. That means aligning to the same (partner) lifecycle stages—from Awareness to Expansion—and applying the same SaaS growth principles that drive success across every revenue function. The classic funnel has evolved 🎯 into the Bowtie model, built for recurring revenue. And that evolution applies just as much to partner-led growth as it does to direct sales. From partner activation and enablement (Onboarding) to joint GTM execution (Retention & Expansion), the same rigor, and orchestration apply. This isn’t about folding Partnerships into Sales—it’s about bringing the same operational discipline to how we recruit, enable, and grow with partners. Partnerships aren’t an adjacent strategy. When done right, they are the engine of SaaS growth. 🚀 Let’s stop building in silos—and start scaling as one system. #SaaS #Partnerships #GTM #RevenueArchitecture #BowtieModel #CRO #RecurringRevenue #PartnerEcosystem #TechLeadership #B2BGrowth

19

1 Comment

This soundbite from Udi Ledergor is always a good one for CMOs to come back to. "As a CMO, if you don't know how your CRO takes their coffee, you're doing it wrong." I.e., the two roles need to be tied at the hip and the CMO needs to be in the trenches with the CRO to find joint success.

14

I've been sending around this HBR article (in the comments) on why transformation stalls at the "last mile". The article identifies seven structural frictions blocking AI transformation: productivity gaps, tribal knowledge, architectural complexity, and governance. What was underweighted for me in the list of frictions: change management and risk management. The article frames the identity problem as a knowledge capture challenge. How do you get what's in people's heads into the system? But there's a deeper issue. When you ask someone who has built their career around being the person who knows to hand that knowledge to a machine, you're asking them to redefine their professional identity. That requires trust, time, and intentional change management. The same is true for risk. The governance section is framed operationally: things like who can create agents and where accountability sits. But organizations scaling AI are also taking on new categories of risk: model bias, decision transparency, regulatory exposure, and reputational harm. Those deserve to be first-class considerations in any transformation. Great technology, poorly managed, creates great problems at scale. #AI #DigitalTransformation #ChangeManagement #Leadership #RiskManagement #Experient

36

2 Comments