How product leaders actually use AI to ship faster.

Nobody asks for stability work. Customers ask for features, sales asks for features, and the platform underneath quietly sets the ceiling on all of it. At K12 Tutoring, ours had a hard one: the total number of sessions we could book at a time was capped far below what the growth plan required. Through three engineering leadership changes, I was the one covering that seam, and I made the call to protect real roadmap space for scale and stability while the business chased aggressive growth.

What kept the invisible work funded was making it visible. Every stability epic tied to a company OKR, so the line from platform work to business goal was written into the roadmap itself. The capacity ceiling tripled, retention held above 95%, and the customer base grew 7x in three years without cracking the foundation it stood on.

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 best AI features don't replace human intelligence, they amplify it. This was an AI feature that took a messy, unstructured set of inputs and turned it into a clear, readable summary, and underneath, structured data the system could actually use.

That's the part I find exciting. Human expertise shapes what matters about a learner, and AI does what it's uniquely good at: taking raw, noisy input and rendering it two ways at once, human-readable for the people supporting the learner, and machine-readable on the backend for everything downstream. Each does its best work, and the product is better than either could make alone.

And the payoff compounds. Every input that used to evaporate becomes structured signal, richer context about the individual. The more a system genuinely understands about a learner, the more it can do for them: sharper analytics, better support, more of the right help at the right moment. It even opens the door to tying that signal to recognized standards, turning a summary into a map of where someone is and where they could go next.

The whole point is more intelligence about the learner in service of the learner, built so the data is handled with care from the first input forward.

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 — learning highlights, next steps, 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.