AI-Native Product Organizations
Redesign how your product organization works — not just the tools it uses.
Most product organizations are asking how AI can make today's teams more productive. I believe the more important question is different:
How should we redesign product organizations when AI fundamentally changes the economics of creating products?
AI is reducing the cost of implementation, learning and crossing traditional disciplinary boundaries. But faster execution alone does not create better products. It changes where the bottlenecks are — and challenges assumptions about team design, roles, decision-making, discovery and leadership.
I help leadership teams explore what those changes mean for their organization and turn that thinking into practical experiments and operating-model changes.
The bigger opportunity is not AI productivity
Coding assistants, AI agents, design tools and automated workflows can already make individuals significantly more productive.
But if the organization around them remains unchanged, much of that potential gets absorbed by the same handoffs, planning cycles, role boundaries, decision latency and governance structures.
The opportunity is therefore not simply to add AI to today's product organization.
It is to rethink the organization around what AI now makes possible.
Broaden individuals
AI reduces the cost of crossing disciplinary boundaries, allowing people to contribute across more of the product lifecycle.
Specialize teams
Organize around problem spaces and outcomes, with the combination of broad builders and deep expertise each challenge actually requires.
Optimize for learning
When building becomes faster, customer understanding, judgment, discovery and fast learning become increasingly valuable.
⚡ AI-Native Product Organization Workshop
A focused leadership session to explore how AI could change the economics, structure and operating model of your product organization.
Rather than starting with tools, we start with the organization: where work slows down today, which assumptions no longer hold, where AI creates new possibilities, and what might be worth testing.
You’ll walk away with:
- A shared leadership perspective on what AI could fundamentally change
- Identification of the most important organizational bottlenecks and opportunities
- Concrete hypotheses for changes to roles, teams, workflows or decision-making
- A small number of practical experiments to run next
Ideal for leadership teams that have moved beyond “we should use more AI” and are beginning to ask what it means for how the organization itself should work.
🧪 Operating Model Experiments
You do not need to redesign the entire organization before learning what works.
We identify one high-leverage part of your product operating model and create a contained experiment around it — for example team boundaries, broader builder roles, AI-supported discovery, faster feedback loops or a new planning cadence.
Depending on the challenge, this could include:
- Testing broader product, design and engineering responsibilities
- Redesigning a team around a stable problem space or outcome
- Reducing handoffs in discovery-to-delivery workflows
- Introducing AI-supported context, planning or decision processes
- Experimenting with shorter learning and delivery cycles
The goal is not transformation theatre. It is to create evidence about what actually works in your organization before scaling the change.
🎯 Strategic Advisory & Leadership Sparring
AI-native organizational change rarely fits neatly into a project plan.
Leadership teams need to make ongoing choices about structure, capabilities, product strategy, hiring, governance and where to experiment next.
I work as a strategic sparring partner to founders, executives and boards, bringing an experienced Product & Engineering perspective to those decisions.
Typical areas of support:
- AI-native product operating models
- Product and engineering organizational design
- Team topology and ownership
- Product strategy, discovery and prioritization
- AI adoption beyond individual productivity
- Leadership and capability development
- Board-level perspective on Product, Engineering and AI
Engagements can range from recurring executive sparring to a more embedded advisory or fractional leadership role.
A practical way to think about the transition
My current thinking looks at the transition through four connected layers:
1. Economics
Understand what AI has made cheaper, faster or newly possible — and where scarcity has moved instead.
2. Capabilities
Develop broader builders who can contribute beyond traditional role boundaries while retaining valuable areas of deep expertise.
3. Organization
Design teams around problem spaces, outcomes and the expertise required to solve them — rather than default role templates.
4. Operating Model
Rethink planning, discovery, feedback, governance and decision-making so organizational speed can keep up with execution speed.
Across all four sits experimentation, enablement and learning. The goal is not to predict the perfect future organization upfront. It is to build the capability to evolve toward it.
Built from operating experience — and continued experimentation
My perspective comes from more than 25 years of building, leading and transforming technology organizations — from consulting and digital products to global B2B SaaS platforms serving 3,000+ customers, 2.5 million users and €25M+ ARR.
I've led Product & Engineering organizations as CPTO, CPO, VP Product & Engineering and CTO, working across product strategy, engineering, organizational design, distributed teams and scale-up execution.
I also continue to solo-build and operate Work2gether AI, while regularly experimenting with new AI-native product concepts and workflows.
That combination matters to me. I don't want to advise companies about AI-native ways of working from the sidelines. I want to keep testing what actually works.
How It Works
Explore
We discuss where you are today, what you are already experimenting with, and where organizational constraints or opportunities are beginning to emerge.
Frame
We identify the highest-leverage questions and decide whether the next step should be a workshop, an experiment or an ongoing advisory engagement.
Experiment & Learn
We turn the thinking into practical changes, establish a plan, learn from what happens, and use that evidence to decide what to evolve next.
How should your product organization change because of AI?
You don't need to have the answer yet. That's a good place to start the conversation.
Let's explore it together