Problem Framing
Better problem definitions lead to better solutions.
Problem framing is the process of determining what problem actually needs to be solved.
The problem first presented is not always the problem that matters most. How a problem is understood determines which solutions are considered and where effort is directed.
Problems are often framed too broadly, too narrowly, directed at the wrong target, or accepted without being questioned.
Too Broad
What exactly is the problem?
“We don't have good visibility into what's happening.”
That sounds like a problem.
But the underlying issue could be:
The question isn't how to improve visibility—it's what information is actually missing and why.
Too Broad means action becomes guesswork.
Too Narrow
Are we assuming the solution?
“We need to build a dashboard.”
That may be the right solution.
But the real problem could also be:
A dashboard improves visibility, but visibility only helps when the missing information is actually the problem.
Too Narrow means useful alternatives are excluded too early.
Misplaced
Are we solving the right problem?
“We need to improve our website.”
The problem may be real. But it may not be the problem that matters most.
It could also be:
A better website creates little value if the real bottleneck exists before or after people visit it.
Misplaced means the wrong thing may be built well.
Inherited
Why do we believe this?
“Customers want more features.”
This assumption may be correct. But has it actually been examined?
Questions worth asking:
Widely repeated assumptions can remain unchallenged long after customer needs have changed.
Inherited means old assumptions keep directing new effort.
The framing failures above rarely disappear on their own.
IntelAnvil explores alternative ways of understanding the problem, questions assumptions, and adjusts its boundaries until the work is focused on what actually needs to be solved.
This process combines human judgment with AI-assisted exploration. AI can surface alternative interpretations, hidden assumptions, competing explanations, and adjacent possibilities. Human judgment determines which possibilities are relevant, where the boundaries should be drawn, and which problem is ultimately worth solving.
Clarify what is actually being solved before anything is built.
Decide what belongs to software, AI, and human judgment.
Produce the type of solution the problem requires.
Every project begins with assumptions about what needs to be solved. Let's make those assumptions explicit, challenge them, and identify the problem that actually deserves attention.