“Your job title won’t protect your role. Your skills will.”
In the AI era, many professionals across tech roles are asking the same uncomfortable question:
“With AI evolving so quickly, will my work still matter?”
This isn’t just a Product Manager’s concern. It’s equally relevant for data analysts, data scientists, engineers, designers, and more.
While AI continues to automate tasks, one thing remains irreplaceable:
The ability to deeply understand problems, define them clearly, and solve them effectively.
Founders are moving faster. Where does that leave the rest of us?
Startups today often operate in what some call “founder mode.”
Decisions are made quickly. Products are shipped even faster. Traditional roles are more fluid than ever.
So if your value is tied only to your job title or your tools, you may be vulnerable.
What matters now is this:
Can you create value, regardless of your role or title?
What AI still can’t do: Understand real human problems
Whatever your domain, one truth remains:
You need to know who you’re building for, what their pain points are, and why solving that problem matters.
For example, a team once considered using AI to automate security questionnaires. On paper, it sounded great.
But after interviewing real users, they found that speed wasn’t the main concern. It was accuracy.
These users worked in cybersecurity. A fast but unreliable answer wasn’t useful.
What they needed was trust and precision.
That level of insight only comes from talking to users, understanding their world, and asking the right questions.
A better way to ask questions
Want deeper insights from users, customers, or even teammates?
Avoid asking vague questions like:
“What do you find frustrating?”
Instead, ask something more grounded:
“What was it like the last time you did [specific task]?”
This opens the door to real stories, emotions, and breakdowns in experience.
And it works just as well in data analysis, user research, or debugging workflows.
What does it mean to “define and solve the right problem”?
This phrase is often repeated, but rarely unpacked.
Let’s define it clearly.
Definition:
The ability to identify root causes behind surface-level symptoms, understand the broader context, and reframe vague issues into specific, solvable problems.
It’s not about jumping into solutions. It’s about stepping back and asking:
- What’s really going wrong here?
- Why is this happening now?
- What would success actually look like?
How to build this skill
1. Focus on context, not just symptoms
When a system fails or a user gets stuck, don’t just fix the surface issue.
Ask about the full journey: what led to this moment, what constraints were in play, and what trade-offs they were juggling.
Helpful prompts:
- “When was the last time this happened?”
- “What were you trying to do?”
- “Why did you choose that method?”
2. Form hypotheses before solving
Instead of jumping to conclusions, form a testable hypothesis.
- “I believe users are abandoning the form because it’s too long.”
- “I think this model is failing due to data imbalance in specific segments.”
This lets you test assumptions before overbuilding the wrong thing.
3. Break big problems into smaller ones
Statements like “The model isn’t accurate” or “The data is wrong” are too vague.
Instead, investigate:
- Which segment is failing?
- Is the issue in input data, processing, or interpretation?
- What edge cases cause errors?
When you can deconstruct a problem, you’re already halfway to solving it.
Why this matters more than ever
AI can write code, generate images, and even suggest solutions.
But it still struggles with this:
Choosing the right problem to solve.
That job still belongs to us.
The people who will thrive are not just those who use AI tools,
but those who know why and when to use them,
and who understand what problem is actually worth solving.
Three types of AI-native organizations
If you’re working with AI, it’s helpful to understand which type of organization you’re in:
- Model-first companies
Focused on training and improving foundational models - Application-focused teams
Using AI to solve real user problems - Infrastructure/tooling providers
Helping others build with AI more effectively
Each type demands different skills.
Where you sit along this spectrum affects how close you are to the customer, and how much room you have to shape the product.
Wherever you are, the key is the same:
Understand the problem, not just the technology.
AI is a kitchen, not a magic box
Here’s a useful metaphor.
Some chefs grow their own ingredients.
Others open the fridge and make magic with what’s inside.
AI is the fridge. You don’t always control the model, or the data, or the limitations.
But your job is to know what’s available, what’s not, and what’s possible within those constraints.
And most importantly, you need to know what the person you’re serving actually wants to eat.
Your AI-era survival strategy
No matter your role, here are five skills that will help you stay relevant:
- Understand the real problem before solving it
- Know what AI can and cannot do
- Validate with data and test constantly
- Set realistic expectations around limitations
- Connect customer needs, data, and technology
Whether you’re an analyst, engineer, designer, or scientist, these are the muscles to build.
Final Thought
AI might replace parts of your workflow.
But it can’t replace your judgment, your empathy, or your ability to define the problem no one else saw.
To stay ahead, don’t just keep up with AI.
Learn how to use it to solve the right problems.
Because that’s still something only humans can do.
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