Let’s (Really) Get Product Management Sh*t Done with ChatGPT

Welcome and Introductions

Cameron Lanier | 00:00–03:10
Well, thank you everyone for joining us—we appreciate you being here. We’re going to talk about really getting product-management stuff done with ChatGPT (I’ll go to the PG version here!). We’re excited; we’ve done a ChatGPT webinar before, but this time Dean gets to be hands-on.
If you don’t know us, I’m Cameron Lanier, your moderator today—you won’t hear too much from me because I know you want to hear from Dean. He’s one of our principal consultants and trainers here at Productside, and we both happen to be in the Raleigh, North Carolina area. Dean has spent more time with ChatGPT than anyone I know, so I’m excited for you to see what he’s prepared—it’s elegant, beautiful work.

Poll #1 – How Much of Your Work Is Currently Assisted by AI?

Cameron Lanier | 03:10–05:20
Before diving in, let’s start with a quick poll: How much of your work is currently assisted by AI?
Options: 0–20 %, 21–40 %, 41–60 %, or “You’ve already been replaced by AI overlords.”
All right—zero to twenty percent is the overwhelming winner at 89 %. No surprise—it’s still a new tool. But I’ll let Dean take it from here.

Setting the Context – Being “Smoke Jumped In” as a PM

Dean Peters | 05:20–10:30
All righty—let’s stop sharing the poll and change screens. We’ll use Mural for today’s workshop; if you’d like to follow along you’ll need a free Mural account. Our scenario: a product manager parachuted into a burning situation. We’ve all been there—dropped into a fire and told to “fix it.”
We’ll see how generative AI tools like ChatGPT can help us put out fires and get things working again. The first thing I always do is set context. When using AI, context is everything—define the goal, give background, state the reason. You can literally ask ChatGPT: “Provide instructions for prompt engineering.” It’ll walk you through it. Refine prompts, ask it to use fewer words, add points—it’s iterative.

Building the Toolkit and Scaffolding the Simulation

Dean Peters | 10:30–16:00
Let’s treat this like engineering scaffolding—a starting structure. Our fake company, GetGo, just got a memo from CEO Pilar Di Avila after a tornado. She wants a product connecting donors of household goods with recipients in need. That’s our challenge. We’ll use ChatGPT to identify pain points and unknowns for donors, recipients, and charities.
We’ll apply design thinking and the Amazon Working Backwards method. Before building a minimum viable product, we need a minimum viable segment.

Prioritization Automation with AI

Dean Peters | 16:00–21:00
Once ChatGPT generates a table of problems, we ask it to prioritize using Steve Johnson’s idea-prioritization rubric and then apply RICE and WSJF scoring. We even ask for output in Markdown so it can be copied into Excel or HTML. Now we’ve automated prioritization and saved tokens by reusing tools ChatGPT already knows.

Framing the Problem – Using MITRE’s Canvas

Dean Peters | 21:00–27:00
Everyone talks to us in solutions; our job is to translate that into problem space. So I fed the memo and pain-point table into ChatGPT and asked it to fill in the inward-facing MITRE problem canvas. It surfaced regulatory barriers, item quality issues, and delivery delays. We refined and decided to focus our MVP on donors since recipients are harder to reach after disasters.

Applying the Value Proposition Canvas

Dean Peters | 27:00–33:00
We used ChatGPT to fill out the Alexander Osterwalder Value Proposition Canvas for donors – pain relievers, gains, and jobs-to-be-done. We pasted its output onto Mural sticky notes for discussion with the team. It’s not final—it’s a conversation starter. Always anonymize your inputs to avoid sensitive data leaks.

From Pains and Gains to Opportunities and Solutions

Dean Peters | 33:00–38:00
Next we move into the Opportunity Solution Tree (Teresa Torres style). We narrow focus to one gain creator and one pain reliever—streamlining donation processes for donors. ChatGPT helps populate the tree so our team has a jump-off point for discussion. These examples accelerate our UX and engineering conversations.

Positioning and Vision Statements

Dean Peters | 38:00–42:00
I asked ChatGPT to generate a vision statement and an emotional one-liner based on our Opportunity Solution Tree. The first draft was too technical, so I had it regenerate a more visionary version. Always fine-tune the language until it resonates.

Creating Press Releases and FAQs (Working Backwards)

Dean Peters | 42:00–47:00
Following Amazon’s Working Backwards method, I asked ChatGPT to draft a press release including our positioning, back story, vision, and one-liner, then to generate a 12-question FAQ. Both outputs were solid conversation documents we could share with stakeholders or paste into Word or Sheets for collaboration.

Bridging Strategy to Tactics – User Story Mapping

Dean Peters | 47:00–52:00
I then asked ChatGPT to draft the first portion of a Jeff Patton-style user-story map for donors. It produced activity steps we could drop directly into Mural. Now our story-mapping session becomes a refinement session instead of a blank-canvas exercise—huge time saver.

Building a Now-Next-Later Roadmap

Dean Peters | 52:00–55:00
Using the tasks ChatGPT generated, I had it create a three-lane Now-Next-Later roadmap. These aren’t final plans—they’re conversation starters for prioritization and alignment. You can even ask ChatGPT to export tasks in CSV format for Jira or Azure DevOps.

Writing User Stories and Acceptance Criteria

Dean Peters | 55:00–57:00
Finally, I prompted ChatGPT to write user stories using the INVEST template and Gherkin-style acceptance criteria. It even output them as CSV so we can import into our backlog. Validation still requires human conversation, but AI gets you to the starting line fast.

Poll #2 – How Much Will You Use AI in Your Work Now?

Cameron Lanier | 57:00–59:00
Let’s wrap up with Poll #2: How much do you plan to use AI in the next six months? Half of you now say you’ll use AI about 50 % of the time—a huge jump from where we started. That’s awesome!

Q&A and Closing Remarks

Cameron Lanier | 59:00–End
Please share your questions—we’ll follow up with answers and maybe another webinar. Remember to join our LinkedIn group and check out our upcoming trainings with Dean Peters. Use promo code TAXDAY15 for 15 % off. Thanks everyone for joining and for getting your product management sh*t done with ChatGPT!