Processes of Problem Solving

 The conceptual models understand the problem, generative models create solutions, and predictive models rank solutions. A predictive model estimates which fix is most likely to reduce delay with the least cost.

Problem-solving is about seeing a problem and actually finding a solution to that problem, not just the band-aid approach to fix the symptom. A conceptual model explains the problem space, a generative model creates candidate solutions, and a predictive model estimates which solution is most likely to work.

That distinction matches the broader difference between generative AI, which produces new content, and predictive AI, which forecasts outcomes from historical patterns. Here are the conceptual, generative and predictive models of problem solving.

Problem-solving roles

-Conceptual: define the problem, constraints, goals, and relationships.

-Generative: propose possible actions, designs, or hypotheses.

-Predictive: score those options by estimating success, risk, or impact.

This is a useful way to think about problem solving because one model frames the issue, one expands the option set, and one helps choose among options.

Simple example: For a product delay problem, a conceptual model maps causes such as supplier risk, staffing, and approvals. A generative model suggests fixes such as alternate vendors, schedule compression, or process changes. A predictive model estimates which fix is most likely to reduce delay with the least cost.

So problem-solving is both art and science. The conceptual models understand the problem, generative models create solutions, and predictive models rank solutions.

Posted in: Business Process Management,Principle/Practice,Problem Solving