Problem Framing
Better problem definitions lead to better solutions.
Problems are often framed too broadly, too narrowly, directed at the wrong target, or accepted without being questioned.
Too Broad
Action becomes guesswork.
Example 1
“We don't have good visibility into what's happening.”
The underlying issue could be:
The question isn't how to improve visibility—it's what information is actually missing and why.
Too Narrow
Useful alternatives are excluded too early.
Example 1
“We need to build a dashboard.”
The underlying problem could be:
A dashboard improves visibility, but visibility only helps when the missing information is actually the problem.
Misplaced
Effort is spent on the wrong problem.
Example 1
“We need to improve our website.”
The problem that matters more could be:
A better website creates little value if the real bottleneck exists before or after people visit it.
Inherited
Old assumptions keep directing new effort.
Example 1
“Customers want more features.”
Questions worth asking:
Widely repeated assumptions can remain unchallenged long after customer needs have changed.
A problem frame is not improved only by thinking harder about it. It can be investigated.
IntelAnvil uses different ways to question assumptions, explore alternatives, and obtain useful evidence.
Challenge assumptions, consider alternative explanations, and identify what is not yet known.
Read how human judgment and AI work togetherExamine existing information, customers, markets, technologies, or other relevant sources.
Ask people, observe behavior, compare alternatives, measure demand, or run experiments.
Build something lightweight to see whether it addresses the right problem.
Clarify what the real problem is before deciding how to solve it.
Determine the right roles for human intelligence, AI, and software.
Determine how to reach the solution and what it needs to deliver.
Before committing to a solution, make sure the problem itself has been examined, challenged, and supported by the right evidence.