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 their needs.Repeated inclusive user testing reveals patterns across different needs and contexts. Clear evidence is recorded showing that findings materially reshaped or reframe the proposed service.
Technical feasibility
Technical feasibility rests on assumptions.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.Spikes validated the architecture, leading to specific documented changes that lower risk of later issues. Architecture decisions document tradeoffs and overall architecture is cohesive and appropriate for the org's people, processes, and technology.
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 have validated 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.

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