Use‑case discovery & value framing – Partner with business stakeholders to identify high‑value copilot scenarios, define measurable outcomes (time saved, deflection rate, quality), and shape a delivery roadmap.
Solution architecture & design – Design end‑to‑end copilot solutions (Copilot Studio + data + integrations), including conversation strategy, orchestration approach, and overall technical architecture.
Knowledge & grounding strategy – Determine the best approach for grounding responses (approved knowledge sources, websites, SharePoint, Dataverse, Graph-connected content), and reduce hallucination risk with guardrails and citations where possible.
Integration design (connectors/plugins/APIs) – Specify how the copilot will securely interact with enterprise systems using Power Platform connectors, custom connectors, APIs, or actions—ensuring performance and reliability.
Identity, auth & access model – Design authentication patterns (Entra ID), role-based access, least privilege, and user context handling—especially when copilots call systems of record.
Security, compliance & DLP alignment – Ensure the solution complies with organizational policies (DLP, data classification, retention, audit), and coordinate reviews with security/privacy teams.
Environment strategy & ALM – Define Dev/Test/Prod environment setup, solution packaging, versioning, release pipelines, and deployment patterns aligned to CoE standards.
Reusable assets & standards – Create and maintain reusable templates (topic patterns, prompt patterns, escalation flows, connector/action patterns), plus naming conventions and design standards to scale consistently.
Prompting & response quality patterns – Establish prompt guidance (system instructions style, grounding prompts, safe completion patterns), response formatting standards, and content safety practices.
Testing strategy – Plan functional testing, conversation testing (happy-path/edge cases), security testing, regression testing, and load/performance considerations; define acceptance criteria.
Risk management & Responsible AI – Identify risks (misleading answers, sensitive data exposure, bias, over-automation), implement mitigations (guardrails, disclaimers, escalation), and document decisions for governance.
Stakeholder & delivery coordination – Act as the bridge across business owners, makers, pro developers, security, and operations—driving design reviews, backlog clarity, and delivery alignment.
Operational readiness & lifecycle management – Ensure go-live readiness (support model, runbooks, incident process), define maintenance cadence, and manage continuous improvement using analytics and feedback loops.
