It has become relatively easy to use AI, to create the kind of Product Management assets that I have used for 15 years. This is an exploration of how to create tools in a full AI-native Product Operations pipeline with MCPs and Agents. Since context is key, here is a diagram of these tools in a broader network, and an overview of the current state of Product Operations.
Most PMs can describe their user in a sentence. Getting from that sentence to a persona detailed enough to anchor a journey map — with a Jobs to Be Done statement, org context, tech comfort, key interactions, and relationship friction — is where research stalls. This tool closes that gap.
A journey map is only useful if it reflects what actually happens — not what teams assume. This tool provides an editable, stage-by-stage canvas for mapping customer actions, goals, touchpoints, emotions, and organizational responses, with a draggable sentiment curve and one-click AI export for analysis.
Enter your V2MOM — vision, values, methods, and measures — and this tool cascades it directly into a Hoshin Kanri X-matrix. Values become annual objectives. Methods map exactly to activities. Measures drive key metrics. The matrix is color-coded by value group, correlation dots are interactive, and Claude can align and refine the whole thing on demand.
Engineering and infrastructure teams constantly have to translate what they need and why it matters into language that earns executive approval. This tool does that translation — from project inputs to a structured, stakeholder-ready business case in three analysis depths.
A live demonstration of agentic tool chaining — four sequential AI calls where each step's structured output becomes the next step's input. Takes a raw feature idea from any format through intake triage, JTBD framing, assumption mapping, and a RICE-based prioritization brief. An explanation panel at each step shows the design decision behind the chain.
Product operations health is rarely measured — it is felt. This tool makes it legible: a structured self-assessment across the core dimensions of delivery operations that surfaces where teams are strong, where they are struggling, and what to prioritize first.
Every tool here started as a friction point observed embedded inside delivery teams — a moment where a capable PM was spending hours on work that should take minutes. The hypothesis behind all of it: AI is most valuable when it is inside the workflow at the moment of decision, not bolted on afterward.