### Vision Create an organization that: - Never loses critical knowledge when employees leave - Can onboard new staff efficiently with self-service resources - Understands the ripple effects of changes before they happen - Progressively automates routine work while maintaining human oversight - Transforms IT from reactive service to proactive strategic partner ### Overview Institutional Intelligence transforms siloed knowledge into a digital twin of your operations. It builds capacity for AI inference engines to understand and interact with your organization effectively. By treating processes as the fundamental unit of organization, we create a framework that AI can reason about. ### Implementation Phases #### Phase 1: [[ai process documentation template|Process Mapping]] **What:** Document all processes using the standardized template **Core Principle:** Processes are the fundamental unit - they define what skills, systems, and data an organization needs **Value:** Creates searchable, consistent operational knowledge **Usage:** - Department reference guide - Training resource for new employees - Foundation for AI ingestion #### Phase 2: AI-Powered Help System (Informational) **What:** Deploy AI that answers "how-to" questions from process documentation **Value:** 24/7 self-service assistance for process execution **Usage:** - "How do I submit ACA compliance data?" - "What access do I need for payroll reconciliation?" - "Who approves expense reports over $10k?" - "What are the steps for employee onboarding?" #### Phase 3: Intelligent Analysis & Change Management **What:** AI analyzes processes, implications, and helps articulate problems **Value:** Better requirements, impact analysis, reduced rework **Usage:** - "What happens if we change the submission deadline?" - "Which processes would be affected by upgrading System X?" - "Why is our error rate increasing this month?" - "Process seems broken" - Problem articulation: "Returns suck" → AI helps identify "Validation fails on split shipments" #### Phase 4: Progressive Automation **What:** AI learns patterns from process execution and data **Value:** Automates routine work, escalates exceptions **Usage:** - Automatic handling of standard requests - Pattern recognition for common issues - Suggested optimizations based on metrics - Edge case detection and routing ### Key Use Cases #### For Employees - **Self-Service Help**: Get immediate answers about how to complete tasks - **Process Navigation**: Understand what steps come next - **Problem Articulation**: Turn "this is broken" into clear issue descriptions #### For Managers - **Impact Analysis**: Understand ramifications of proposed changes - **Bottleneck Identification**: See where processes slow down - **Skills Gap Analysis**: Identify training needs from required skills - **Capacity Planning**: Understand workload from process frequency/duration #### For IT/Operations - **Requirements Translation**: AI helps users articulate actual needs - **Change Impact Assessment**: Trace system changes through dependent processes - **Automation Candidates**: Identify high-volume, consistent processes - **Issue Triage**: Better understand problems before they become tickets #### For Compliance/Audit - **Process Verification**: Documented steps with approval chains - **Success Metrics**: Clear thresholds for process health - **Edge Case Handling**: Documented procedures for exceptions ### Measuring Success #### Immediate Metrics - Reduction in "how do I..." questions to IT/managers - Time saved finding process information - Clarity of requirements submitted to IT #### Medium-term Metrics - Reduced rework from better initial requirements - Faster issue resolution through better problem articulation - Decreased process-related errors #### Long-term Metrics - Percentage of processes with some automation - Knowledge retained despite employee turnover - Operational efficiency improvements - Reduction in IT ticket volume