This follow-up grounds my earlier “re-rating” thesis in underwriting reality, laying out three lanes for PE-owned SaaS assets (compound, consolidate, transform) and the tests that separate real AI value creation from packaging.

AI adoption is accelerating, at a faster rate than AI revenue. This article introduces a four-level framework for AI monetization, spanning Capability, Feature, Product, and Digital Labor. Using real-world examples from Zoom, GitHub, Intercom, and Klarna, it explains why most AI initiatives stall at the lower levels, how pricing power correlates with workflow integration and economic ownership, and what it takes to move from bundled functionality to outcome-based revenue. The result is a practical guide for product leaders, operators, and boards trying to turn AI investment into durable business impact.

AI tuck-ins only create value when the acquired capability plugs into the core data model, workflows, and decisioning engine of the parent platform. Most fail not because the technology is weak, but because the PMI playbook was built for the cloud era, not the AI era. This post breaks down how AI PMI differs fundamentally from cloud PMI a decade ago. Done right, AI PMI turns small acquisitions into platform-level transformations, and translate to material enterprise value.

Flashy AI demos are easy. Compounding intelligence is not. The companies that win build unified platforms first, then use AI to accelerate learning. Everyone else is just adding features to systems that can’t compound.

Are you building "Surface Area" AI or "System" AI? I analyze the 2025 State of AI reports from both Battery Ventures and McKinsey to show why most integration strategies fail and how the right M&A playbook can move you from “Pilot Purgatory” to durable enterprise value.

Most leaders approach AI with cloud-era instincts. That mistake is costly. Cloud was a delivery upgrade. AI is a capability shift. It rewrites how software creates value, compounds advantage, and pulls winners away from laggards.

Most AI vendors can produce a great demo. The real question is whether their underlying product, data, and engineering foundations can support AI at scale inside a larger platform. Learn how to spot the difference between "demo-ready" and "AI-ready" before you sign the LOI.

AI isn’t one big wave. It’s a mix of technologies maturing at different speeds, which is why the market feels both overheated and full of genuine breakthroughs.

For companies that weren’t born AI-native, the strategic question isn’t “Is AI a bubble?” It’s “Which part of AI actually moves the needle for our business?” Getting your strategy, architecture, and sourcing engine in place now is what determines whether you’re playing offense when the opportunity is right for your business.

Most SaaS companies talk about M&A as a one-off decision. The ones that consistently create enterprise value treat it as a repeatable operating model. This article breaks down how leading platforms build disciplined, programmatic M&A engines that compound value over time. It lays out a practical operating model spanning strategy, sourcing, evaluation, SteerCo governance, integration, and early success metrics. The piece also clarifies the roles of the CEO, CPO, CFO, CRO, and Corp Dev leader, and highlights where most companies succeed or fail.

SaaS multiples have reset, but AI multiples haven’t. Many SaaS platforms bought during the 2017–2021 cycle still have strong cores. They’re just stuck in a market that is now rewarding the next technological shift of AI, and the economics it represents. This piece lays out a practical, investor-focused playbook for identifying which assets can evolve, how targeted AI-led acquisitions accelerate that shift, and why decisive product and architectural transformation is the only path to re-rating into the AI valuation cohort.

As AI reshapes customer expectations and compresses product cycles, most CX vendors are being pushed into a critical decision: build AI internally or buy it. The truth is that neither path works on its own. This post breaks down why a hybrid model is emerging as the most realistic way forward.