How product leaders actually use AI to ship faster.

Designers are a shared resource, and their time belongs on brand and product, not on throwaway concept sketches. So when I need something visual to pressure-test an idea with stakeholders, or to put in front of users before we commit engineering time, I build the mock myself with AI. A still image when that's enough. A clickable prototype when the idea needs to be felt.

It collapses what used to take hand-drawn sketches, Miro boards, and a slot in the design queue into minutes. And it produces a real file. I can sit with a designer and update the mock live in the same meeting, then take it into real design once we know the concept holds. This doesn't replace design. It protects it, and it makes sure the thing we build is the thing users actually need.

Release notes are a tax you pay every cycle: export the tickets, then translate a wall of CSV rows into something a human can read. It's repetitive, rules-based, and the time back is real, all three of my Rs, which made it an obvious first target.

I built a custom GPT that turned a Jira export into clean, persona-aware release notes, trained on ten "golden" examples until the output was reliable, then shared it with the whole team behind one rule: proofread every time. As the tooling matured, I moved the whole workflow into Rovo inside Jira, where it now drafts notes automatically off the released version.

User stories, defects, epics: they all have to be airtight. A spec gets handed from product to engineering to QA and back, and every ambiguity is a round-trip. So even when the real problem is "the button's broken," the writing it takes to make that unmistakable is real work.

I've taken the typing out of it. For each deliverable I keep a Claude project with a pre-built prompt that enforces the exact structure I want, formatted to paste straight into Jira. Then I just talk, and speech-to-text feeds it in. If anything's vague, the AI asks me targeted questions before it writes a line. And when a section needs something only a human can supply — a technical diagram I'll build later with an architect — it doesn't invent it. It leaves the slot marked TBD, so the gap stays visible instead of quietly filled with something made up.

The company was new to building with AI, and the temptation when you're new is to chase the flashiest idea. I ran the candidates through my three Rs instead, and the academic summary won.

Every virtual session already produces a transcript. Off-the-shelf agents turn that into generic meeting minutes. We didn't want minutes, we wanted to know what was actually being taught. So I shipped a feature that reads the transcript and extracts the academic topics covered, as structured data we could hand to the teacher, the student, their parents, and the school or district. One feature, a whole chain of audiences.

The reason I pushed it first wasn't just the payoff, it was that it was a foundation. Every summary it generates also becomes stored data, the groundwork for pairing academic summaries with teacher feedback to draft the next session's lesson plan — tracking where a student was, where they are now, and where we're trying to get them.

On most platforms a user's information is scattered. One page for this flow, another for that feature, a separate screen just to manage the account. You never actually see the whole person in one place.

This dashboard pulls it together: who the user is, the account controls you need, their real interactions, and the data points that matter, all on a single page. Then it layers AI on top — strengths, areas to improve, a risk status, recommendations — generated from everything the platform already knows. Once a user's full picture lives in one place, AI can actually answer questions about them and suggest what to do next, instead of guessing from a fragment. And the same pattern scales from one student to an entire business account.