There is a moment every product leader faces in their AI journey. It usually begins with someone up top saying, “We need to do something with AI.”
Now, if your reflex is to jump straight into brainstorming features, stop right there. This is where seasoned product thinking either levels up or gets derailed. Because in AI, defining the right problem is not just step one, it is half the game. And doing it precisely, with the right framing, can mean the difference between launching something magical and burning months on a solution no one needed.
Let me walk you through how I think about this, especially in the wild context of social media. This is where comment threads become battlegrounds, feeds overflow with noise, and everyone wants their moment in the spotlight. In this world, precision matters.
Think Outcomes, Not Features
If you come from traditional product management, you might be used to thinking in features. Add a button. Launch a filter. Build a dashboard. That mindset does not translate cleanly to AI.
In AI, we start with outcomes. What are we trying to optimize? What behavior are we hoping to change or predict? Features are just one possible expression of the solution, and in some cases, not even necessary. For example, if your team wants to reduce spam comments, your first instinct might be to design a filter UI. But an AI PM would reframe it: “Can we detect and demote toxic content automatically, while preserving healthy conversation?”
This becomes a classification problem, with measurable outcomes like fewer abuse reports or higher satisfaction scores. It also creates clear alignment: everyone from data science to engineering knows what success looks like and what the model needs to do.
Ask if AI Is Even the Right Tool
This part cannot be overstated: not every problem needs AI. If a simple rule will do the job, use it. AI shines when things are too complex for hard coding, when user preferences shift constantly, or when you are dealing with patterns buried in behavior at scale.
Sorting content by time? Use a rule. Predicting which posts someone will love based on context, time, and past engagement? That is AI territory.
Hypotheses Over PRDs
When I define AI problems now, I start with a hypothesis. It goes something like this:
If we implement an ML based solution that scores content relevance based on user history, then we will increase feed engagement by 10 percent, as measured by dwell time and content interaction rates.
This small shift from writing specs to formulating hypotheses completely transforms how your team works. It gets everyone focused on impact. It encourages experiments. It makes it easier to pivot when the data tells a different story.
Real Examples Make It Real
Let me share a few anonymized examples from real social media teams I have worked with.
1. Comment Moderation
Old way: “Add a keyword filter to block bad comments.” n AI way: “Train a model to classify comment toxicity in real time, with thresholds tuned to minimize false positives and maximize conversation quality.” n Outcome: Reduced abuse reports, better sentiment in discussions, and creators sticking around longer.
2. Feed Personalization
Old way: “Let users sort their feed manually.” n AI way: “Rank posts by predicted engagement likelihood per user, using signals like past behavior, time of day, and content type.” n Outcome: Higher retention, more time spent in app, and fewer complaints about irrelevant posts.
3. Content Sharing Visibility
Old way: “Add a new tab for shared links.” n AI way: “Predict the quality and relevance of shared content for a given audience and elevate high potential posts in the feed.” n Outcome: More link clicks, better distribution of shared posts, and higher satisfaction without cluttering the UI.
System Thinking Is a Must
AI features do not live in isolation. They are part of systems. If you build a comment classifier, how does it surface in the UI? Does it hide comments, warn users, flag for moderators? Can users give feedback to improve it?
Defining the AI problem means defining the system, the data inputs, the prediction task, the user feedback loop, and the business metric it drives.
Actionable Habits I Recommend
- Always define the problem before thinking about the model or feature
- Write down your hypothesis including inputs, prediction, and success metric
- Confirm the problem really needs AI, start simple if you can
- Use real data or examples to ground the problem statement
- Bring engineers and data scientists in early
- Think through the full user experience and how the AI fits into it
- Document scope boundaries: what you are solving and what you are not
Final Thought
Framing AI problems with precision is not about sounding smart. It is about setting up your team to solve the right problem, in the right way, with the right tools. Do it well, and you will not just ship smarter features, you will create AI experiences that feel effortless, human, and genuinely valuable.
Next time someone says “Let’s add AI,” smile and say: “Great. Let’s define the problem first.”
That is where the real product magic begins.
