LEARN. PRACTICE. GROW.Concept and mechanism
Evaluating a solution in use requires measures linked to the intended outcome. Define population, period, data source, and collection conditions before comparing results. An exception count alone can mislead when transaction volume changes. The exception rate can also hide segment differences. Use comparable data and explain what it cannot yet establish. Distinguish an internal solution limitation from a barrier in the organizational context. If functionality fails with valid data, there is a technical hypothesis to investigate. If it works in tests but operators lack access or training, changing the algorithm may not solve the problem. These hypotheses can coexist and need evidence.
Guided application
In an original exercise, a team evaluates 200 transactions before and 500 after a change, with 10 and 15 exceptions. Rates are 5% and 3%, a two-percentage-point reduction despite the rising count. The observed improvement does not by itself prove the change caused it: transaction composition may differ. Analyze relevant segments and conditions before recommending expansion. If the tool reduces manual validation but transfers work to another team, include that cost in evaluation. Recommend an action proportionate to the demonstrated limitation and define how to check the correction’s outcome. Delivery may be complete while value realization still needs follow-up from a business and operations owner.
10/200 = 5%; 15/500 = 3%. Count and rate tell different stories.
Common pitfalls
Assumed causality; changed population; missing training treated as a code defect.
Related topics: Plan analysis and decisions · Elicitation, evidence, and collaboration · Life cycle, priorities, and changes
Connect the recommended action to the limitation and outcome measure.
Reference: Outcomes and value creation · Six-knowledge-area blueprint / handbook May 2026