Solution Capability · 6.4
Improve AI quality, task completion, speed, cost, reliability, and user experience using evidence from monitoring, evaluation, and real operations.
Production AI may be useful but inefficient. Prompts grow complex, retrieval misses key sources, tools fail, latency increases, costs rise, and user feedback is not converted into improvements.
AI product and application owners
Engineering and platform teams
Operations and service leaders
Knowledge and content owners
Organizations seeking more value from an existing AI solution
Review business outcomes, monitoring, evaluations, incidents, cost, feedback, and architecture.
Identify the most important failure patterns and constraints.
Prioritize changes across prompt, model, retrieval, content, tools, workflow, integration, infrastructure, and user experience.
Test changes against the evaluation baseline.
Release with controlled change and observe production impact.
Prompt and instruction refinement.
Retrieval, indexing and content improvement.
Model and routing optimization.
Tool and integration reliability.
Latency and cost optimization.
Conversation, workflow and escalation redesign.
Azure AI Foundry, Copilot Studio, OpenAI, Claude, and IGNA.
SharePoint, Microsoft 365, and Salesforce.
ERP, APIs, databases, and observability tools.
Security & Governance
Optimization uses controlled releases, regression evaluation, access control, change approval, rollback, evidence, and Human in the Lead review. Cost or quality improvements should be verified before claims are published.
A readiness workshop is the fastest way to find out if this is the right starting point.
