Mind Matrix: Navigation
SOP-1015-01-AI_AI-Knowledge-Transfer-and-Handover-Procedure
Title: AI Knowledge Transfer and Handover Procedure
Effective Date: [Date]
Previous Version: None (New SOP)
Reason for Update: New AI-specific SOP
Owner: Chief AI Officer (CAIO)
Location: [Link or File Path]
Signature/Date: [Owner signature and date]
Objective
This Standard Operating Procedure (SOP) provides a detailed method for knowledge transfer (KT) and handover activities within an AI project lifecycle. It ensures continuity, comprehension, and responsible stewardship when AI systems transition between teams (e.g., from Development to Operations, or from an external vendor to an internal group). This SOP addresses roles, steps, required documentation, and best practices for knowledge dissemination, specifically tailored to the complexities of AI models, data, and ongoing compliance with AI-IRB guidelines.
Scope
- Applicability:
- All AI projects under the Horizon AI-SDLC that need a formal knowledge transfer between internal or external stakeholders.
- Activities that must ensure AI-IRB compliance, data privacy obligations, and alignment with organizational standards.
- Exclusions:
- Routine operational Q&A between team members.
- Formal training sessions for new hires or broad organization-wide training (covered by other SOPs).
- Any deployment-specific push or environment changes (covered by SOP-1220-01-AI).
Applicable To
- AI Development Team (Model creation, codebase ownership)
- Data Scientists (Feature engineering, data curation)
- MLOps/Operations (Production environment, CI/CD pipelines)
- Quality Assurance (Validation, compliance checks)
- Project Manager (PM) (Manages handover scheduling, resources)
- AI-IRB Liaison (Ensures regulatory compliance and signoffs)
- Receiving Organization (Team or department that will take ownership of AI assets)
Sections
- Procedure Overview
- Roles and Responsibilities
- Handover Package Requirements
- Knowledge Transfer Activities
- Sign-Off and Acceptance Criteria
- Metrics and Continuous Improvement
- Records Management
- Revision History
Related Procedures
- SOP-1000-01-AI: AI Program/Project Management
- SOP-1014-01-AI: Regulatory & Ethical AI Compliance Verification
- SOP-1220-01-AI: Deployment
- SOP-1101-01-AI: Training and Documentation
Definitions
| Term | Definition |
|---|---|
| AI-IRB | Internal Review Board for AI, ensuring responsible AI governance, compliance, and ethics. |
| Handover | The process of transferring ownership of AI code, data, and knowledge from one entity to another. |
| KT (Knowledge Transfer) | Structured approach to share domain, technical, and operational knowledge about an AI solution. |
| Receiving Org | The group taking responsibility for the AI solution post-handover. |
| Handover Package | Collection of documents, model artifacts, code, test logs, compliance checklists, etc. used for knowledge transfer. |
| Authorized AI Agent | A validated AI system or subsystem identified within the Mind Matrix as having the authority to execute specific SDLC or operational tasks. |
1. Procedure Overview
This procedure ensures seamless knowledge transfer for any AI system nearing the end of development or requiring re-assignment of responsibilities. It covers:
- Creating or updating a handover package with essential deliverables (model architecture, data lineage, code repositories, test logs, compliance docs).
- Planning and executing knowledge transfer sessions.
- Verifying the receiving team’s readiness with acceptance sign-offs and demonstration of understanding.
- Finalizing any open tasks related to AI-IRB and compliance monitoring.
2. Roles and Responsibilities
| Role | Responsibility |
|---|---|
| Project Manager (PM) | Oversees the knowledge transfer timeline, ensures tasks are planned and completed, coordinates cross-functional sign-offs. |
| AI-IRB Liaison | Verifies continuing compliance with AI-IRB stipulations, ensures all relevant guidelines are included in the handover. |
| AI Development Team | Prepares code repositories, model documentation, addresses open technical questions. |
| Data Scientist | Provides dataset specifics, feature engineering details, data documentation, and provenance. |
| MLOps/Operations | Understands production environment, operational metrics, pipeline orchestrations; ensures environment readiness. |
| Quality Assurance (QA) | Checks completeness and correctness of handover package, validates final compliance logs, organizes final sign-offs. |
| Receiving Organization | Participates in knowledge transfer sessions, reviews documentation, provides acceptance sign-off. |
3. Handover Package Requirements
Each AI system’s Handover Package must include:
- Source Code Repositories: Up-to-date code (main branch), with stable tags or version references.
- Model Artifacts: Final model(s), hyperparameter configurations, training pipeline details.
- Dataset/Feature Catalog: Summaries of data sources, transformations, version info, disclaimers on data usage compliance.
- Test Results: Unit tests, integration test logs, relevant coverage metrics.
- Compliance Documentation: AI-IRB approvals, risk assessments, privacy impact analyses, bias and fairness checks.
- Deployment/Operations Guides: MLOps pipeline instructions, environment variables, credentials (stored securely), performance metrics.
- Support & Maintenance: Escalation procedures, contact lists for domain experts, next iteration or refactoring plans.
- Recursive Subroutine Documentation: Detailed logs and consistency reports for any recursive self-improvement subroutines active in the system. |
4. Knowledge Transfer Activities
4.1 Scheduling Sessions
- PM coordinates dates for KT workshops and invites the Receiving Org, Dev Team, QA, and MLOps.
4.2 Preliminary Review
- The AI Dev Team hosts a pre-review meeting, ensuring the Handover Package is complete.
- QA cross-checks documentation for clarity and correctness.
4.3 Formal KT Workshops
- Session 1: Architecture & Code Walkthrough.
- AI Dev Team leads, with DS providing data pipeline details.
- Session 2: Live demonstration of model training and typical usage.
- MLOps covers production pipeline orchestration.
- Session 3: Regulatory/Compliance briefing.
- AI-IRB Liaison explains relevant IRB constraints, risk acceptance status, any ongoing audits.
4.4 Q&A & Action Items
- Log all open issues in the KT Issue Tracker.
- Dev Team or DS addresses each item with target deadlines.
4.5 Validation & Acceptance
- QA organizes a final sign-off meeting.
- If the Receiving Org states readiness, the PM logs formal acceptance in the project library.
5. Sign-Off and Acceptance Criteria
- Handover Package is fully delivered and stored in the designated repository.
- Receiving Org acknowledges thorough understanding of code, data, model, compliance constraints, and next steps.
- No high-severity open issues remain in the KT Issue Tracker (medium or lower severity can remain if documented).
- AI-IRB Liaison confirms ongoing or closed IRB items are addressed, no pending compliance red flags.
- PM obtains signatures from Dev Lead, QA Lead, MLOps Lead, AI-IRB Liaison, and the receiving team’s sponsor, utilizing Exochain Peer Reviews for automated acceptance validation.
6. Metrics and Continuous Improvement
| Metric | Description |
|---|---|
| KT Cycle Time | Time from the formal request of knowledge transfer to final acceptance. A shorter cycle time implies an efficient process. |
| KT Issue Reopen Rate | Number of reopened or new tickets after the receiving team attempts to operate or modify the AI system. High rate indicates incomplete or unclear KT steps. |
| Compliance Gaps Post-Handover | Incidents of newly discovered compliance issues within 30 days after acceptance. Low or zero indicates thorough compliance coverage. |
7. Records Management
- Handover Package: Stored in the project’s central repository with a naming convention
[ProjectName]_Handover_[Date].zip(or similar). - KT Attendance & Sign-Off: Meeting minutes and acceptance forms stored under
[ProjectName]_KT_Signoff_[Date]. - Compliance Documentation: AI-IRB sign-off, risk logs remain in the compliance folder under
[ProjectName]_IRB. - Post-Handover Review: Summaries placed in the same folder, referencing any newly discovered issues or follow-up tasks.
8. Revision History
| Version | Date | Revised By | Changes |
|---|---|---|---|
| 1.0 | [Date] | CAIO | Initial creation |
END OF SOP-1015-01-AI
@startuml
title “SOP-1015-01-AI: Knowledge Transfer & Handover Sequence Diagram”
’ Define participants (avoid vertical-bar style)
participant “Project Manager” as PM
participant “AI-IRB Liaison” as AIIRB
participant “AI Dev Team” as DevTeam
participant “Data Scientist” as DataSci
participant “MLOps/Operations” as MLOps
participant “Quality Assurance” as QA
participant “Receiving Organization” as RecvOrg
’ 1. PM initiates knowledge transfer scheduling
PM -> DevTeam: Initiate KT request & timeline
PM -> QA: Confirm readiness for doc review
PM -> AIIRB: Verify IRB obligations
’ 2. DevTeam and DataSci prepare Handover Package
DevTeam -> DataSci: Coordinate final code & model docs
DataSci -> DevTeam: Provide data lineage & feature catalog
DevTeam -> QA: Submit draft Handover Package
’ 3. QA reviews content for completeness and correctness
QA -> DevTeam: Provide feedback on missing/unclear items
DevTeam -> DataSci: Clarify data or model queries if needed
DevTeam -> QA: Resubmit updated documentation
’ 4. MLOps environment notes added
QA -> MLOps: Request environment details for handover
MLOps -> DevTeam: Provide pipeline & operational docs
’ 5. AIIRB compliance check
QA -> AIIRB: Validate AI-IRB compliance docs
alt IRB compliance
AIIRB -> QA: Approved
else IRB not compliant
AIIRB -> QA: Request additional clarifications
QA -> DevTeam: Clarify or remediate compliance issues
DevTeam -> AIIRB: Resubmit compliance docs
AIIRB -> QA: Approved
end
’ 6. PM schedules formal knowledge transfer
PM -> RecvOrg: Invite to KT workshops
RecvOrg -> PM: Confirm schedule availability
’ 7. Conduct formal KT sessions
group KT Session 1: Architecture & Code
DevTeam -> RecvOrg: Present architecture, code structure
end
group KT Session 2: Model & Data
DataSci -> RecvOrg: Demonstrate training pipeline, data usage
end
group KT Session 3: Compliance/Ethics
AIIRB -> RecvOrg: Summarize compliance constraints & risk
end
’ 8. Q&A & open issues
RecvOrg -> DevTeam: Raise open questions
DevTeam -> RecvOrg: Provide clarifications
’ 9. Decision: Are there unresolved issues?
alt Issues remain
DevTeam -> RecvOrg: Resolve all open items
QA -> DevTeam: Verify resolution
else No issues
RecvOrg -> PM: Acknowledge readiness
end
’ 10. Final sign-off
QA -> RecvOrg: Request acceptance sign-off
RecvOrg -> QA: Provide acceptance signature
QA -> PM: Confirm official knowledge transfer completion
@enduml
Short textual explanation:
This sequence diagram shows how the Project Manager initiates the AI knowledge transfer, how the DevTeam and Data Scientist create a Handover Package, and how Quality Assurance and the AI-IRB Liaison review compliance. Once the MLOps environment details and any outstanding issues are addressed, formal knowledge transfer sessions occur. Finally, the Receiving Organization signs off, indicating successful AI knowledge handover.