Assume They Are Wrong
A Necessary Mindset AI Product Managers Need to Find the Right Problems for AI
#beyondAI - You’re sitting in a meeting with a department lead who enthusiastically says, “We need AI to fix our reporting delays.” Or maybe someone approaches you, saying, “We’re struggling to meet our delivery timelines, but we’re not sure if AI can help.” Then there are those times when no one approaches you at all, and you’re left uncovering hidden pain points in processes no one is actively questioning. Welcome to the world of discovering AI use cases in an enterprise context.
For over a decade as an AI Product Manager, I’ve been navigating these scenarios. Whether building AI products for end-consumers or internal teams, the underlying purpose of AI remains the same: to make things easier, faster, and cheaper. The real challenge isn’t in the promise of AI but in finding the right problems for AI to solve—and doing so efficiently.
In large enterprises, where processes are complex and stakeholders are diverse, discovering AI use cases is rarely straightforward. From my experience, the requests and opportunities typically fall into three distinct scenarios:
Scenario 1: Someone believes they know the problem and is confident AI is the solution.
Scenario 2: Someone believes they know the problem but isn’t sure if AI is the right fit.
Scenario 3: No one recognizes the problem, let alone considers AI as a potential solution.
Of these, Scenario 1 is often the easiest to approach. When stakeholders know you as someone who delivers AI solutions, they proactively reach out to you with their ideas. However, that doesn’t mean it’s without challenges. Scenarios 2 and 3, on the other hand, require deeper exploration, closer alignment with stakeholders, and a methodical approach to uncover the real opportunities AI can address.
A Crucial Mindset: Assume They Are Wrong
Let me share one critical piece of advice, especially for Scenario 1 and 2: always assume stakeholders are wrong in their assumptions. This applies to both their understanding of the underlying problem and their belief that AI is the right solution. While this might sound overly cautious, it’s a mindset that ensures you approach every idea critically and methodically.
Stakeholders often view problems through the lens of their immediate frustrations, focusing on surface-level symptoms rather than the root cause. For instance, a department lead might say, “We need AI to make our chatbot smarter.” On the surface, this sounds reasonable, but upon closer investigation, you might uncover that the real issue lies in outdated or incomplete FAQs feeding into the chatbot. Or perhaps the department lead was actually referring to this root issue but framed it in a way that’s open to misinterpretation. Either way, relying solely on their initial framing can easily send you down the wrong path.
The same caution applies to their belief that AI is the solution. While AI is undoubtedly powerful, it’s not a one-size-fits-all tool. Many challenges can be addressed more effectively—and often more efficiently—using simpler approaches like process optimization, basic automation, or off-the-shelf software solutions. The reality is that stakeholders often perceive AI as a magic bullet without fully understanding its capabilities or limitations. That’s where you come in: to guide them toward realistic, impactful solutions.
Now, I’m not suggesting you dismiss their ideas outright. On the contrary, validating assumptions is a key part of effective collaboration. The way forward is to ask thoughtful, probing questions that help clarify the situation, such as:
“What makes you think AI is the right solution here?”
“What data do we have to support this idea?”
“Is this problem repetitive or pattern-based?”
Digging deeper to validate the stakeholder’s assumption typically takes longer than just asking whether AI is the right fit. If your team’s focus is solely on delivering AI products, your first step should be to determine if the assumed problem can actually be solved by AI. If it can’t, you can politely guide the stakeholder to a different team better equipped to address their challenge.
However, there’s a tricky downside: if the assumed problem turns out to be incorrect, but the actual underlying issue is something AI could solve, you risk losing that opportunity. 🙂 Well, I never said this was an easy world to navigate.
By approaching every proposed idea with healthy skepticism, you ensure only well-founded, high-impact opportunities move forward. This mindset not only prevents wasted efforts but also positions you as a trusted advisor. Stakeholders will appreciate your thoughtfulness and rigor. They recognize that you’re not just executing their requests but carefully aligning solutions with their real needs.
Why This Mindset Matters
Adopting a skeptical mindset isn’t about being difficult or contrarian; it’s about protecting both your resources and your credibility. In the fast-paced, often ambiguous world of enterprise AI, jumping into a solution without validating assumptions can quickly lead to expensive missteps. Pause to critically evaluate the problem and its fit for AI. You will minimize your risk but also elevate the quality of your outcomes.
This mindset is especially important in large organizations, where AI use cases often face scrutiny from multiple angles: leadership wants measurable ROI, technical teams need clear feasibility, and end-users expect seamless solutions. It’s not about saying “no” to every idea but about ensuring the right problem is being solved in the right way.
Lessons from My Experience
In my 10+ years of navigating these scenarios, I’ve learned that skepticism often leads to unexpected breakthroughs. Stakeholders may approach you with a flawed framing of their problem, but asking the right questions can uncover opportunities that no one saw coming. One of the most satisfying moments in this role is when a stakeholder realizes, mid-conversation, that their initial assumption wasn’t quite right—but that together, you’ve identified a problem far more valuable and solvable.
I remember a time when a sales team came to me saying, “We need AI to analyze why we’re losing deals.” After digging in, it turned out they already had plenty of insights into why deals were being lost; the real issue was that sales reps didn’t have an easy way to act on this data in real-time. By reframing the problem, we shifted from building a generic AI analytics tool (is AI needed at all?!?) to developing an AI-powered recommendation engine that suggested the best next steps for reps to take during a deal cycle. The result? Higher adoption, faster decision-making, and a direct impact on revenue.
It’s this kind of journey—from assumption to clarity—that makes skepticism so powerful. Not only do you deliver better solutions, but you also build stronger relationships with stakeholders.
Making It Practical
If you’re an AI Product Manager or working in a similar role, here’s how you can adopt this mindset in a way that’s both efficient and collaborative:
Start with Curiosity, Not Criticism: Stakeholders may not articulate their problems perfectly, but they often have valuable context. Use their initial ideas as a starting point, not an endpoint.
Example: “That’s an interesting challenge. Can we explore what’s behind it?”
Be Transparent About AI’s Strengths and Limits: Educate stakeholders early about what AI can realistically achieve and where it might not be the best fit.
Example: “AI works great for repetitive, data-driven problems, but this challenge might be better addressed with process changes.”
Collaborate to Reframe the Problem: Use structured methods like workshops, hypothesis-driven discovery, or even simple brainstorming sessions to align on the real issue.
Example: “If the problem isn’t the chatbot’s intelligence but the content feeding it, we might need to start there before applying AI.”
Don’t Be Afraid to Pivot: If AI isn’t the right solution, guide the stakeholder toward other options. This honesty builds trust and sets the stage for future collaboration.
Final Thoughts
While this article focused on the mindset required for companies and, in particular, AI Product Managers when identifying the right problems for AI, there are plenty of techniques—like Hypothesis-Driven Discovery, Value Stream Mapping, Design Thinking, Job Shadowing, or Process Mining—that can help boil down to root problems and at the same time discover if AI might help. Each of these fits better with specific scenarios introduced here, and not all are necessary. But knowing what tools and methods you can leverage makes Product Discovery easier and much more structured.
Once you’re ready, I’ll write about those methods, showing how they can enhance your process. For now, let’s focus on building that mindset—it’s the foundation for everything else.
JBK 🕊️
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