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As of 2026, Highline Beta argues that teams often confuse surface frustrations for real problems, and introduces the User/Need/Deeper Need framework along with their Problem Statement Validator tool to help teams identify specific users, functional needs, and the emotional or social drivers that give problems weight.
Real problems combine three elements: a specific user, a functional need, and a deeper emotional or social driver that creates urgency. Most teams stop at identifying functional needs like "manage finances better," but breakthrough solutions emerge when teams uncover deeper needs like "feel independent and confident without being judged." Highline Beta's Problem Statement Validator uses an AI-trained assessment algorithm to score problem statements and guide teams through targeted questions that refine vague problems into multiple sharp, actionable opportunities.
Real problems combine a specific user (like "Gen Z professionals starting their first jobs" rather than "young adults"), a functional need they're trying to address, and a deeper emotional or social driver that gives the problem weight and urgency. Surface frustrations typically only capture the functional layer without understanding who specifically experiences the problem or why it matters enough to drive behavior change.
The tool uses an HLB-trained assessment algorithm to automatically rate problem statements across multiple dimensions, then guides users through targeted AI-powered questions to clarify the user, need, and deeper need. After the challenge questions, it generates multiple refined problem statements that score higher against the same criteria and provides recommended next steps for research and exploration.
Apply this framework early in discovery, before solutions take shape or assumptions harden, especially when problems feel vague, abstract, or underspecified. It's most valuable when teams are tempted to jump straight to building without understanding who they're designing for and why it matters, as clear problem framing creates alignment and sets a strong foundation for research and ideation.
The original broad statement about Gen Z "opting out" of financial systems scored well enough to proceed but lacked explicit user, need, and deeper need definitions. After engaging with the AI bot, the tool generated refined statements like "socially-conscious Gen Zers who feel traditional saving is 'hopeless'" needing to "align their limited spending with their personal values" - creating multiple targeted opportunities from one vague starting point.
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Missed last week's edition of Beyond the Core? Read it here.
Teams often confuse surface frustrations for real problems. But real problems run deeper. They combine:
This framework helps teams uncover meaningful opportunities before jumping to solutions.
Good problems are narrow enough to focus on a real group, but broad enough to unlock value. They expose motivations, reveal where innovation matters most, and guide teams toward solutions that people actually care about. Without this depth, even well-built solutions risk indifference.
For a deeper dive, check out our original post here.
Use the User / Need / Deeper Need framework.
Most teams stop at the functional need. Real traction comes from the deeper need. That emotional or social context gives urgency and direction to your solution.
This works in B2C and B2B whether the deeper driver is trust, efficiency, credibility, or competitive advantage.
Instead of one broad problem statement, you now have several sharper, more targeted ones. You're ready for research and exploration.
Apply this approach early in discovery, before solutions take shape or assumptions harden. It’s most valuable when problems feel vague, abstract, or underspecified, or when teams are tempted to jump straight to building.
Clear problem framing creates alignment, uncovers real opportunity, and sets a strong foundation for research, ideation, and solution design.
Original Problem Statement: Gen Z isn’t “bad with money”. They’re opting out of a system that feels misaligned with their realities. High rent, student debt, and everyday costs leave little room for long-term planning. Weak financial education, opaque institutions, and advice built for stable, rising-asset careers reinforce distrust. Disengagement isn’t failure, it’s rational survival.
When we run this through the HLB Problem Statement Validator, the tool assesses how well-defined it is across multiple dimensions.
While the statement scores well enough to proceed, the tool highlights where it can be strengthened, specifically by making the user, need, and deeper need more explicit.
By engaging with the AI bot, the team is pushed to think more deeply. The result is a set of refined problem statements, each grounded in clearer users, sharper needs, and more meaningful drivers.
Once generated, the tool rescoring explains why these statements are stronger and recommends next steps.
Next up: We’ll take two of these refined problem statements into the HLB Starbursting tool to explore the unanswered questions hiding underneath them, before we get into solutioning.
