TL;DR
Product development today is not what it was a few years ago, and that’s a good thing. A big part of this change comes from how we work with AI and how our roles as “Product people” have evolved.
The product role and context
Being in Product is no longer just about writing specs, prioritizing tickets, and owning the roadmap, it’s about knowing when to lead, when to enable, and when to get out of the way, and about using AI to move faster, communicate better, and stay truly agile for team collaboration.
The way a Product person gets involved changes a lot depending on the size and maturity of the company.
In some moments, we are responsible for:
- Defining direction
- Creating a product vision
- Shaping strategy
- Planning features and timelines
- Aligning stakeholders around a roadmap
In those phases, we are more “strategic”: thinking long term, connecting product to business goals, and deciding where we’re going next.
But there are other moments when our role shifts into something much more operational:
- Making sure decisions turn into real execution
- Helping to unblock dependencies
- Coordinating launches
- Supporting distribution and go‑to‑market
Here, we’re more like facilitators than “owners”: we help the system move instead of trying to control every part of it.
Knowing when not to intervene
One of the most underrated skills in Product is knowing when not to intervene. Balancing control and collaboration in product roles is crucial. Sometimes, the best thing we can do for our engineering and design teams is:
- Not over‑specify
- Not interrupt focus with unnecessary questions
- Not add more noise or more meetings
Our job is to give input where it truly adds value and to respect the moments where the team just needs space to build. Where the team needs space to build, that means no new ideation, no planning, and nothing else that creates distraction.
It all comes down to timing:
- When to collect feedback
- When to share user feedback
- When to reframe a decision
- When to execute a change
Not everything has to be decided or discussed immediately, and not everything has to be perfect in the first iteration.
Startups and the value of time
In small startups, this becomes even more critical. Time is a constraint that defines everything.
You can’t afford:
- Endless back‑and‑forth on decisions
- Heavy processes for every feature
- Over‑polishing before anything sees the light of day
What you can do is:
- Keep things simple and functional at first
- Ship earlier
- Learn from real usage instead of only user research
Having a technical team that understands and values design and product thinking horizontally changes everything.
And it’s gold, especially when engineers, designers, and Product all share:
- The same understanding of the problem
- The same sense of value for the user
- The same awareness of design
- The same direction
When that happens, Product doesn’t need to micromanage, and the role becomes more about alignment, context, and momentum.
AI as Multiplier
AI tools help us move faster and stay agile in very concrete ways.
1. Faster, deeper research
Two detailed examples of how we use AI tools for faster product research:
- Summarize large amounts of qualitative feedback by looking for keywords, filtering reviews from different sources, and generating overall insights for User Research to draft conclusions.
- Cluster user pain points into clearer themes instead of spending days structuring raw information, so we can get to insights much faster.
2. Faster prototyping and better feedback
We can show up to conversations with design and development more prepared, more focused, and with clearer context by:
- Creating a v0 or small prototype and showcasing the functionality and user journey we’re expecting, which reduces back‑and‑forth and accelerates ideation and definition.
- Generating first drafts of UX copy, suggesting flows or variations of a concept, and creating quick visual references for design conversations.
What we do is reduce the friction of getting from idea to something concrete we can react to. This shortens the gap between “I have a thought” and “We have something to review together,” even if it starts as a static comment on a Figma design.
One of the biggest gains in agility comes from reducing unnecessary feedback loops in product design and pre‑validating ideas before bringing them to the team, so the loop becomes tighter, more focused, and more intentional instead of disappearing.
AI-powered prototyping for product managers is a bless, just prototyping, not for production.
Time, intentionality, and judgment
Agility is about moving fast in the right direction and being very conscious of how we invest our time.
With the help of AI, we can:
- Spend less time on low‑leverage tasks like formatting, summarizing, and rewriting.
- Keep decisions lightweight when they can be and deep when they need to be.
We can confidently say:
- “This solution is simple and functional enough for now.”
- “We’ll revisit after we launch and have data.”
That is not being careless; that is being intentional.
The pattern becomes:
- Design something good enough to ship
- Launch
- Then review, walk through it again, and learn with real data
AI creates more space for better judgment.
Enabling over controlling
Product development today is increasingly about:
- Less “I control everything”
- More “I enable the right people at the right time”
- Less perfection before launch
- More learning after launch
- Less friction in communication
- More clarity, thanks to better tools
AI is not the hero of the story, but it is a powerful ally. It speeds up research, improves how we prototype and communicate, reduces unnecessary back‑and‑forth, and helps teams stay agile without burning out.
The challenge for Product people now is to learn how to work with AI intentionally.
Good product work is about how we help the whole team move forward together with clarity and agility.
Stay curious, keep learning, and join us on this evolving product journey at Invent.
