Team 4 (Penguins) Norms:
– Open communication (projects, timing, etc.)
– Communication channel: default to text communication
– Timeliness: text if going to be 5+ minutes late, explain scheduling demands in advance to allow the team to work around deadlines/availability, avoid procrastination on group work
– Contribute equal effort as much as possible
– Direct, respectful feedback within the group (private if involving other parties)
– Meetings should be in-person when possible (schedule with when 2 meet/evenings)
New Questions:
- Tell me a bit about your company, and your role within it.
- How do you usually first approach a problem?
- What’s one problem you’re trying to solve right now?
- What would you say to someone who is entering the PM field right now?
- How has AI changed the way that you approach PM work? Good and bad…
Affinity Maps:
Our Insights and AI Insights:
We noted from several issues that meetings consume a considerable portion of PMs ‘ schedules, and context switching between meetings is a huge issue, alongside not having long enough meetings. These become issues for PMs because they destroy focus/flow of work. Many are using AI tools to fill in the gap (transcripts, actions to do, context). Additionally, disconnects between teams were a prominent focus: It’s important to make sure that marketing, engineering, and product teams are all aligned. It’s possible to have a disconnect between the teams, which can waste time or make the product unusable. These insights, as well as a handful of others seen in our affinity map, were seen again in the AI-generated analysis.
- It caught our attention that it noted “Problem-first, customer-first thinking” as an issue/finding. This isn’t something that we had included in our map; however, we think it’s a reasonable conclusion to draw about the difficulties of being a PM. That being said, the transcripts don’t necessarily indicate that this is an issue they regularly experience, but rather that it is something that they recommend developing as a PM to rely on throughout your career.
- It flattened some nuance as well; we discussed how our PMs dealt with issues of ambiguity, coordination vs. action, and the evolving tasks/goals for an organization/product. These appear to be grouped under cross-functional alignment and ambiguity, which, although we agree with, perhaps lack some of the specifics.
- Thankfully, it doesn’t seem that the AI analysis hallucinated any details, and overall, it aligns well with our interviews and personal interpretations.
Long List:
| # | Candidate problem | Raised by | Quote |
| 1 | Fragmented calendars and constant context switching leave little time for deep work or meeting prep | 6 of 10 | “A bad day is too many meetings stacked on top of each other where nothing actually gets done or decided.” |
| 2 | Leaders and functions are misaligned on priorities, vision, or ownership, and tooling does not fix it | 5 of 10 | “Different groups or teams within the same company aren’t aligned well. And that’s not something AI can solve.” |
| 3 | AI output sounds confident but needs manual verification, and it misses local context | 5 of 10 | “It sounded authoritative. Like fully certain. That’s what makes it tricky.” |
| 4 | Priorities shift mid-week or mid-build, which wastes planned or finished work | 3 of 10 | “When something urgent pops up and the thing you scheduled just disappears.” |
| 5 | Teams build before the real problem is defined or validated | 3 of 10 | “You go to companies and people just build, build, build, build, build.” |
| 6 | Tool sprawl and manual capture of notes, action items, and decisions | 2 of 10 | “Half the time you’re juggling five different platforms just to move one decision forward.” |
| 7 | Gaps between what sales and marketing promise and what the product delivers | 2 of 10 | “So many products fail because when we promise a customer that, okay, this is what our product does, the onboarding doesn’t live up to that promise.” |
| 8 | Technical or feasibility constraints surface late and force rework | 2 of 10 | “Engineering basically told me, not really feasible with the backend and IVR constraints.” |
TAM:
We based our TAM around the global PM market using the following source (Global PM Estimate Research), which landed the estimate at over 1 million PMs. Other sources, such as LinkedIn (via this source: LinkedIn PM Estimate), place the estimate closer to 850,000. For the purposes of this assignment, we’re assuming that we’re looking at a range of approximately 850K to 1.5 million PMs. Now, regarding what gets spent on tools for them, we looked at the most common PM tools: Jira Discovery, Productboard, and Aha! Roadmaps/Discovery.
- Jira Discovery: 2 tiers, Monthly costs: $10/PM, $25/PM (Atlassian Pricing)
- Productboard: 2 paid per-PM tiers, Monthly costs: $19, $59* annual basis monthly pricing (Productboard Pricing)
- Aha! Roadmaps: $59, Aha! Discovery: $39 (Aha! Pricing)
For a conservative estimate, we took 850K PMs and assumed they use two of the least expensive tools available: Jira Discovery Standard and Productboard Plus (combined total of $29). This gives us a TAM worth approximately $295.8 million. Comparatively, we could consider a maximum scenario in which they have access to all products and a larger total market: 1.5 million PMs (25+25+59)12=$1.96 billion.
Interview Rounds Reflection:
After the first two weeks in class, we felt more comfortable to dig deeper and ask about what problems they had in their day. We also felt better asking the interviewee to elaborate and get into more niche topics of conversation. Interestingly, we found that it’s okay to ask fewer questions and go more in depth about them, rather than asking a wide range of questions. We followed up on questions and let their answers guide the interview. This presented a new challenge of becoming comfortable with the interview diverging from how we originally scripted our line of questioning.
Reading Incorporation:
While the readings this week were focused more on user interviews, we found that the concepts of correct question-building still applied. For example, instead of asking about what happened in the future, we asked about what happened in the past. That’s also why it’s important to ask about a specific day (like yesterday) to ensure that the interviewees have somewhere concrete to start.
