This lesson develops practical capability in Azure Data and AI Portfolio, Market Context, Customer Discovery, and Qualification as part of Azure Data & AI Partner Sales Acceleration Program (PSAP). Learners connect the concepts to a realistic workplace objective, perform guided work, validate evidence, and document improvements.
The lesson begins by defining the architecture, stakeholders, information flow, operating assumptions, and quality expectations that shape azure data and ai portfolio, market context, customer discovery, and qualification. It distinguishes essential decisions from optional enhancements and explains how security, governance, usability, cost, and supportability influence the final design.
Learners then map the end-to-end workflow, identify control points, and choose appropriate configuration or analysis steps. Special attention is given to dependencies, permissions, data boundaries, exception handling, documentation, and the evidence required for an accountable operational handoff.
Apply the lesson to a scenario in which an organization must improve positioning Azure data and AI solutions through customer discovery, data estate assessment, analytics and AI scenarios, value mapping, architecture conversations, security, governance, commercial planning, objection handling, and opportunity progression. Produce a concise plan, implementation or analysis evidence, a validation checklist, a troubleshooting record, and a prioritized recommendation for the next iteration.