AI Workbench

Focus and learning

Alpha mainly produced a demonstration or build without a clear learning question.It tested some assumptions, but their priority, test conditions, or conclusions are unclear.Alpha tested the most important open assumptions. Results changed the direction, risks, or next work.Successive experiments tackled the largest remaining uncertainty. Evidence supports explicit continue, change, or stop choices and identifies what remains unknown.

User desirability

Intended users did not test the proposed service.Feedback came mainly from colleagues, convenient participants, or opinions about a demonstration.Intended users completed realistic tasks. Observed behaviour shows whether the proposed service meets important needs.Repeated inclusive user testing reveals patterns across different needs and contexts. Findings materially reshape the proposed service.

Technical feasibility

Technical feasibility rests on assumptions or architecture diagrams.The team explored some technical risks, but tests avoided critical integrations, data, permissions, scale, or failure conditions.Focused spikes validate the critical architecture and integrations under realistic conditions. Findings and remaining unknowns are recorded.Evidence covers the hardest dependencies, failure conditions, and operating constraints. Architecture decisions reflect the results and remain adaptable.

Organizational viability

Ownership, funding, staffing, procurement, policy, or support needs remain unexamined.The proposed service assumes organizational interest or capacity without enough evidence.Relevant decision makers and operational staff validate the required resources, responsibilities, constraints, and changes.Named owners, credible costs, committed capacity, and resolved dependencies show that the organization can sustain the proposed service.