Clarity in Product Thinking before Speed in Delivery
AI won’t fix how teams think — it will only expose when they stop thinking.
In my last post, I wrote about how product teams drift into project mode: when deadlines matter more than customers, and delivery is treated as the outcome (not output). That drift isn’t new.
What is new is the belief that artificial intelligence will fix this problem.
AI is in every strategy deck. Leaders are pushing teams to use it, add AI to OKRs, and show productivity gains from it. But here’s the reality: most teams are not using AI to write code, automate test cases, or meaningfully change how products are built.
A recent MIT report showed that 95% of enterprise AI initiatives haven’t produced measurable business value (page 3). The technology isn’t the issue. The issue is the lack of workflow integration, unclear governance, limited tool access, and almost no training. Teams are being asked to “use AI,” but they don’t know which tools are approved, what data is safe, or how to even begin.
When expectations increase but clarity doesn’t, pressure replaces purpose.
Speed Without Focus Isn’t Progress
Some people are experimenting with AI outside of work. Others are using it mainly to generate more output: more user stories, more test cases, more slide decks, more prototypes.
But does an increase in output mean an increase in understanding?
Efficiency doesn’t come from moving faster. Efficiency comes from working on the right problems. AI only creates efficiency when teams know which problems are worth solving.
If a team already operates like a feature factory, AI will help them ship the wrong things faster. If a team is grounded in the user, the problem, and the outcome, AI becomes an accelerator.
AI doesn’t replace product taste; it reveals whether product thinking exists at all.
But… what does Product Taste mean?
Product taste isn’t a vague instinct. It is a combination of:
Empathy for the user: understanding what customers want, what they’re trying to do, what frustrates them, and what “good” looks like to them.
Domain expertise: knowing the subject matter ecosystem, constraints, and realities the product lives in.
Focus: choosing what to build and what not to build.
Data + intuition together: using data where it exists and informed intuition where it doesn’t.
Smell tests: recognizing when something feels off, even before numbers show it.
Decision clarity: being able to explain why this, why now, and what happens if we don’t.
Courage: defending what matters for the product, even when it isn’t the easiest or most popular choice.
Taste is what separates a product team from a delivery team.
What It Looks Like When Taste Is Missing
You can tell when a team has lost product taste. It shows up in small everyday decisions:
Features are built because someone asked for them — no one asks “why?”
Prioritization follows stakeholder pressure instead of user or business value.
Backlog items remain for months without anyone checking if they still matter.
Teams can’t clearly explain the problem a feature solves or who benefits.
Timing isn’t questioned — “should we launch a risky feature during a holiday surge, or just ship it to check a box before year-end reviews?”
Decisions are justified with “leadership wants it,” not “this moves the product forward.”
Teams focus only on their roadmap or component — not the whole user experience or ecosystem.
This is when the team stops doing product work and starts doing output work.
And, AI doesn’t correct that behavior; it just exposes it faster.
Leadership Pressure, Governance, and the Reality of AI Adoption
In most large organizations, leadership expects AI adoption to increase efficiency. At the same time, governance, legal, and compliance teams block access to many of the tools people hear about.
Teams are expected to use AI, but they don’t know which tools are approved, don’t have access to the right systems, haven’t received training, and are not sure what’s safe or allowed.
So most people are stuck in high expectations, low enablement, and many don’t even know where to start.
But not everyone waits. Some PMs and engineers teach themselves. They experiment in small, safe ways. They learn how to use AI for research, summarization, prototyping, or testing assumptions. Not because they were told to, but because they’re curious and want to get better at the craft.
This is where product taste becomes the real differentiator:
Without taste, AI will lead to more noise — more output, not more value.
With taste, AI will create space to think — faster validation, better decisions, precise focus.
AI will not create efficiency on its own. AI will enable efficiency only when teams know which problems matter and which don’t.
Restoring Product Taste
Product taste isn’t built through frameworks. It’s built through discipline and staying close to the problem:
Problem clarity. What problem are we solving? For whom? How do they solve it today without us?
Trade-offs made visible. Alignment isn’t everyone agreeing. It’s a shared understanding of what we will and won’t do.
Validation early and often. A prototype. A small rollout. Directional data. Any signal is better than none.
Data used to learn, not to defend decisions. It should inform choices, validate hypotheses, and challenge assumptions.
Time to think. Taste disappears when every minute is spent in fire drills, escalations, slide reviews, and status calls.
These aren’t big processes. These are small habits. AI can support each of them, but only if product taste guides how AI is used.
Clarity before Speed
AI is not the threat. Losing the ability to think deeply is. And that responsibility is still ours - product managers, leaders, and teams.
If AI revealed how your team makes decisions today — not your roadmap, not your Jira board, but your thinking — would you be comfortable with what it shows?
In my next post, I’ll write about how teams actually build product taste — especially when access to users is limited, data is imperfect, and time is short.