Bas Kamphuis

Global enterprise SaaS M&A is defying expectations, turning Q3 2025 into a massive hot streak that rivals the activity records of 2021. Deal volume surged more than 26% QoQ, fueled largely by a dominant shift toward private equity. PE buyouts “blew the roof off” the market, capturing two-thirds of total deal value and significantly outpacing corporate acquirers. While heavyweight segments like enterprise resource planning and customer relationship management maintained the largest market share, the knowledge management systems segment emerged as a breakout star, achieving a staggering 545% increase in value. The quarter was defined by 17 multibillion-dollar megadeals, led by the $12.4 billion take-private of Dayforce and Blackstone’s $6.5 billion acquisition of Enverus. As interest rate cuts begin to take hold, institutional appetite is returning for market leaders, particularly in human capital management and digital commerce. Read our full report to explore why 2025 is shaping up to be a banner year for SaaS dealmaking and see which subsegments are commanding the highest premiums.

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I had the chance to hear MIT Sloan Professor Nelson Repenning and Jason Child (CFO of Arm) speak at the MIT CFO Summit yesterday — and their discussion was one of the clearest, no-nonsense breakdowns of what’s really happening with AI inside organizations today. 💡 One story in particular stuck with me: Professor Repenning revisited General Motors’ legendary 1980s push for “lights-out factories.” GM spent $40B–$50B automating everything — robots welding, painting, assembling — expecting technology to be the silver bullet. Instead, it became one of the biggest automation failures in modern corporate history. What went wrong? 🤖 Robots welded the wrong panels 🚗 Automated carts crashed into walls 🎨 Paint robots sprayed each other 📉 Productivity dropped The problem? GM automated broken processes built on: - Disorganized workflows - Poor-quality incoming parts - Erratic production schedules - Outdated plant layouts Sound familiar? Because many companies are doing the exact same thing today… just with AI. As Jason Child put it: 🎲 AI is probabilistic 📏 Business operations are deterministic 🧩 If the data feeding AI is messy, siloed, non-granular, or held together by heroic spreadsheets… ➡️ AI will confidently give the wrong answer — faster and at scale. Professor Repenning said it best: “There are few ways to lose money faster than automating a process you don’t understand.” The lesson from both speakers was loud and clear: 👉 AI isn’t the strategy. Your data foundation is the strategy. 👉 AI doesn’t fix fragmentation. It amplifies it. 👉 AI doesn’t replace process maturity. It depends on it. The real question isn’t: “What’s our AI roadmap?” The real question IS: “Are our data, workflows, and processes ready for AI to actually work?” Because when your data is clean, unified, connected, and deeply granular — AI becomes a multiplier. When it’s not… You get a modern version of GM’s lights-out factory: huge investment, small ROI, and a lot of executives asking what went wrong. #CFO #Leadership #AITransformation #DataQuality #DigitalTransformation #ChangeManagement #OperationalExcellence #Automation #FutureOfWork #MITCFOSummit #FinanceLeadership FinQore

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Here’s some sobering news for all the AI cheerleaders out there. There are successful ways of implementing generative AI, but very few have followed such approaches. Warning; this contains actual data and analysis from actual human beings: https://lnkd.in/g_QZc-gD

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