AI Product Workflows
AI made product work faster. It also moved the bottleneck.
AI can compress discovery, prototyping, documentation, and execution from weeks to hours. But most teams still make decisions, manage quality, and coordinate work through pre-AI operating rhythms.
I help product leaders redesign how their teams actually work when building is no longer the bottleneck.
At reev, this rebuilt the product team's discovery and delivery rhythm with AI in the loop, shipped the first customer-facing AI feature (a natural-language insights engine for fleet operators), and reduced PM/Ops overhead by roughly 30%.
The product process hasn't caught up.
When engineering was slow and expensive, it made sense to spend weeks aligning, documenting, and planning.
Now AI changes that.
But most teams still operate the same way, and that creates friction, misalignment, and wasted work.
- Discovery cycles are too slow
- Roadmaps become outdated quickly
- Quality and escalation points are unclear once AI is in the loop
"If AI can generate discovery in hours, what is my team actually doing?"
I help teams find the new bottleneck.
This is not an AI tools workshop.
Over 2 weeks, we take one real workflow inside your team (discovery, prioritization, delivery, or quality) and redesign it as a human + AI operating system with clear quality and escalation points.
Week 1 · Map and diagnose
Map the current workflow, interview the people inside it, identify where decisions, handoffs, and quality checks are breaking.
Week 2 · Redesign and run
Redesign the workflow as a small human + AI operating system with clear escalation points. Implement it with the team and observe one full cycle.
This is usually relevant when:
- Discovery and decision cycles feel too slow for how fast the team can now build.
- Roadmaps go stale within weeks of being written.
- Quality and escalation points are unclear once AI is in the loop.
- The team keeps adding tools instead of redesigning how it works.
- PRDs, handoffs, and meetings still dominate, even though execution has compressed.
Selected work
Delivered reev's first customer-facing AI feature: a natural-language insights engine for fleet operators. Reduced operational overhead by ~30%.
Co-organised three editions of Product × AI Munich, a meetup series with speakers from Google Cloud, reev, casavi, and Pendo (30–60 people per edition). Also host invitation-only breakfast conversations for senior product leaders navigating the AI shift. Next session: June 26 2026 - invite only.
Advised B2B SaaS product leaders on integrating AI into discovery and delivery workflows without losing quality or escalation clarity.
You leave with:
- A clear diagnosis of what is breaking in the workflow.
- A redesigned workflow with faster decisions and less wasted work.
- A practical operating approach the team can run with.
- A short written summary your team can refer back to.
This does not replace a head of product or an AI lead. It gives the team a working operating model so the next hire lands into clarity, not noise.
The engagement
A focused, hands-on engagement with one product team, working on a real workflow.
Format
- 2 weeks
- 2–4 working sessions
- Async collaboration
Investment
- €4,500 fixed fee
- Paid 50% on start, 50% on completion
Priced separately from the Product Growth Diagnostic engagement.
Output
- A redesigned workflow running inside the team, plus a short written operating summary.
About Luis
Senior Product Leader with 10+ years' experience building B2B SaaS products across Europe. Most recently led product at reev, a Munich-based EV charging SaaS platform, and served on its AI Advisory Board.
Previously worked across Toyota Production Systems, Zalando Innovation Lab, and product organizations operating at scale.
Background across operational systems, product strategy, AI-assisted workflows, and product-led growth.
Not sure if it fits? Bring one bottleneck and we'll talk it through.