AI strategy
Prioritize the use cases, governance, and implementation paths that turn AI from experiment into operating advantage.
Four-time SaaS CPO · available for advisory
Four-time Chief Product Officer. AI SaaS product leader. I help SaaS and enterprise leadership teams move from scattered AI experiments to focused roadmaps, operating models, and measurable execution.
The three disciplines I bring to every engagement — from executive advisory to hands-on implementation.
Prioritize the use cases, governance, and implementation paths that turn AI from experiment into operating advantage.
CPO-level clarity for roadmaps, discovery, team design, metrics, and product-led transformation at scale.
Modernize legacy workflows and software delivery using practical operating models and AI-assisted execution.
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 with deep expertise in AI product strategy, operating model design, and transformation execution. I help mid-market and enterprise leaders move from AI ambition to measurable business outcomes.
My work spans executive advisory at ProductExec and AI implementation delivery at Enterprise AI Studio. Together they cover the full arc from strategy to production systems.
Four-time CPO · London
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 engagements.
AI-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 → 5 min Essay · June 9, 2026Most enterprise AI pilots don't fail. They just never end. The six reasons pilots stall in purgatory, and the 90-day structure that forces a real scale-or-kill decision.
Read the essay → 4 minWhether you need to decide what to build, someone to build it, or a place to start small — the path runs through these practices.
For leaders who need to turn AI pressure into product strategy, operating model decisions, and an executable roadmap before teams commit delivery capacity.
Visit ProductExec →The implementation arm — readiness audits, workflow automation pilots, and intelligent assistant deployment for enterprise teams.
Visit Enterprise AI Studio →Practical AI pilots and guides for smaller teams — working pilots measured at 30, 60, and 90 days, not demos.
Visit Smart Biz AI Hub →Choose the starting point that matches where you are — each pattern is described in detail on the Work page.
Prioritized use-case map, risk assessment, and implementation roadmap.
How it works →Decision rights, roadmap governance, discovery cadence, metrics, and workflow design.
How it works →Working pilot for automation, assistants, document intelligence, or workflow agents.
How it works →Kevin Owens is a four-time Chief Product Officer and AI SaaS product leader based in London. He has led product across SaaS, media, and enterprise software, and now helps SaaS and enterprise leadership teams turn AI ambition into operating models, roadmaps, and products that ship — through executive advisory at ProductExec and implementation at Enterprise AI Studio.
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 three-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.
CPO-level strategy, portfolio prioritization, leadership rhythm, and stakeholder translation for teams at a strategic inflection point — without the twelve-month search for a full-time hire. Advisory and fractional CPO work runs through ProductExec; representative engagement patterns are on the Work page.
Whether you need strategy clarity or a working system — let’s find the right starting point.
Not ready for a call? Start with the 3-question readiness check in the Lab.