AI across a portfolio
Score each company on AI maturity, find the plays that repeat, and write business cases that partners and management both sign.
Four-time CPO · AI value creation for PE portfolios
I turn AI from a slide into shipped software and a number that moves. Four-time Chief Product Officer, now working with private equity firms and their portfolio companies: find the plays that pay back, prove the case, build it, and measure the return.
The same person writes the business case, leads the team and ships the first version. That is the whole point.
Score each company on AI maturity, find the plays that repeat, and write business cases that partners and management both sign.
CPO-level leadership inside a portfolio company: the AI thesis, the roadmap, the team design and the measures behind each bet.
I write the PRD and ship the MVP with the company’s own team, and I run my own fleet of AI agents in production.
Under his leadership everything turned around. The entire platform was redesigned and rebuilt to live in the cloud using modern technologies… making it best-in-class in the industry. User engagement increased dramatically, and so did sales.
I have rarely met a Product leader more aware of the value of customer experience and customer support as Kevin is. His openness to include Support in the conversation around Product was refreshing and inspiring.
I’m a four-time Chief Product Officer. I’ve run product in PE and VC-backed B2B software, and most recently I was embedded by a private equity owner as AI transformation lead inside one of its software companies.
Today that work runs through ProductExec, AI value creation for private equity portfolios, with implementation through Enterprise AI Studio. This site is where the thinking gets written down.
Four-time CPO · Los Angeles
Every engagement starts with the same discipline: understand the system before prescribing a fix.
Long-form essays on operating models, pricing, pilots and the CPO role — the same frameworks I use inside portfolio companies.
Product-led growth assumed a person was on the other side of the signup page. Increasingly it is an agent. A go-to-market model for when agents choose, buy and use your product, and run your funnel too.
Read the essay → 9 min Essay · July 21, 2026AI-native is not a property of your product. It's a property of your operating model. Five shifts separate the SaaS companies compounding with AI from the ones demoing it.
Read the essay → 6 min Essay · June 30, 2026Seat pricing was built on an assumption AI just broke: that serving one more user costs you nothing. How SaaS leaders should price intelligence without eroding margin or suppressing adoption.
Read the essay → 6 minPick the sentence that sounds like your week. Each door is a practice with its own scope, pace and price.
Your fund needs AI to show up in the numbers. Find the plays that repeat across the portfolio, prove the case in one company, then roll it out.
You know what to build and need it in production. One workflow, built with evals, human approval gates and a runbook, in weeks.
You want to start small and prove it. Practical AI pilots and guides for smaller teams, measured at 30, 60, and 90 days.
Kevin Owens is a four-time Chief Product Officer based in Los Angeles. He now helps private equity firms and their portfolio companies turn AI into measured results: assess the portfolio, prove the business case, ship the software inside each company and measure the return. That work runs through ProductExec, with implementation through Enterprise AI Studio.
In four steps. Assess the portfolio to find the AI plays that repeat across companies. Prove the case in one company, with a business case partners and management both sign. Ship the first version with the company’s own team, in shadow mode first. Then measure adoption, hours and payback, and roll the same design out to every company with the same problem. The method is on ProductExec.
An operating model where AI investment runs as a bet portfolio instead of a fixed roadmap, quality is defined by evals instead of prose specs, success is measured as learning rate instead of launches, discovery runs as evidence loops, and AI fluency lives inside every product team rather than a separate AI group. The flagship essay The AI-Native Product Operating Model covers all five shifts.
With the AI Readiness Score: a two-week diagnostic that scores five dimensions from 1 to 5 — data foundations, workflow clarity, decision rights and governance, capability density, and adoption posture. The lowest dimension determines the right first move, which is almost never buying model access. A five-question quick version lives in the Lab.
On a three-rung ladder that follows how much of the job the AI completes: assistive copilot features included in seats, workflow features priced per unit of completed work on a hybrid platform-plus-meter model, and end-to-end agents priced against outcomes. Pricing AI Features in a Seat-Based World lays out the full framework and the margin math behind it.
A senior product and AI leader inside a portfolio company, often at the owner’s request, for a set period: strategy, prioritization, leadership rhythm and the AI thesis, without the twelve-month search for a full-time hire. Fractional AI and product leadership runs through ProductExec; representative engagement patterns are on the Work page.
Whether you need a portfolio view, a business case or a working system, start with one company and one decision.
Not ready for a call? Start with the five-question readiness check in the Lab.