Solution Capability · 6.7
Use production evidence, user feedback, business change, and evaluation results to improve AI capability through a controlled backlog.
AI systems can stagnate after launch. New documents appear, workflows change, users find gaps, business priorities shift, and teams accumulate improvement ideas without a transparent method to prioritize and release them.
Business and operations owners
AI product managers
Application and engineering teams
Knowledge and content owners
Governance and support teams
Collect signals from users, support, monitoring, evaluation, incidents, content owners, and business outcomes.
Classify issues and opportunities by value, risk, effort, and dependency.
Maintain a shared backlog with accountable owners.
Design and test improvements against the evaluation baseline.
Release through controlled change management.
Measure impact and update priorities.
New use cases or supported intents.
Knowledge and content updates.
Workflow and tool improvements.
Quality, safety, cost and performance improvements.
User experience and adoption improvements.
Governance and support process improvements.
The existing AI and application stack.
Content, data, and integration systems.
Monitoring, ticketing, analytics, and workflow tools.
Security & Governance
Improvement uses approved data, change control, testing, regression evaluation, release evidence, access control, rollback, and governance review. High-risk changes require appropriate Human in the Lead approval.
A readiness workshop is the fastest way to find out if this is the right starting point.
