Mind Matrix: Navigation
SOP-1030-01-AI_AI-Ad-Hoc-Reporting-Procedure
Title: AI-Ad Hoc Reporting Procedure
Version: 1.0
Effective Date: 2025-01-30
Previous Versions: None
Owner: AI Data & Reporting Office
Approved By: Chief AI Officer (CAIO)
1. Purpose
This Standard Operating Procedure (SOP) defines the process for requesting, generating, and delivering ad hoc AI-based reporting in alignment with the AI-SDLC. It provides guidelines to ensure compliance with the AI-IRB (Artificial Intelligence Institutional Review Board) and fosters consistent, timely, and accurate reporting of analytics, insights, and metrics beyond scheduled or routine outputs.
2. Scope
This SOP applies to all requests for one-time or urgent AI-driven data insights, as well as non-routine analytics that leverage the organization’s data platforms, ML/AI models, or big data infrastructure. It covers:
- Collection of requirements for ad hoc AI-based reports.
- Validation, prioritization, and authorization workflows (including any AI-IRB steps).
- Report design, development, quality checks, distribution, and lifecycle management.
Excluded from scope:
- Routine operational dashboards or regularly scheduled AI analytics, unless they are significantly changed or re-scoped such that they become “ad hoc” in nature.
- Model building or large-scale data ingestion tasks (covered in separate SOPs, e.g., SOP-1020-01-AI).
3. Roles and Responsibilities
| Role | Responsibility |
|---|---|
| Requestor | Submits ad hoc AI-reporting request, providing clear objectives, scope, timeline, and compliance considerations. |
| AI IRB Liaison | Verifies that any ethical, regulatory, or privacy implications are vetted before an ad hoc request is processed. |
| AI Data Analyst | Receives request from Requestor, refines requirements, and ensures alignment with AI governance frameworks. |
| Data Scientist | Reviews AI or ML model usage needed for the ad hoc request, ensures model interpretability, compliance, and validity. |
| MLOps Engineer | Sets up or updates necessary computing environments, manages version control of data scripts and integrated ML code. |
| Quality Assurance | Ensures the ad hoc report meets acceptance criteria, verifies data correctness, and logs any defects for resolution. |
| Operations | Manages final distribution or automation of the ad hoc request outputs if needed. |
| Technical Support | Provides Tier 1 user training and help desk function for questions about the final ad hoc report or results. |
| AI Ethics/Compliance | Monitors any compliance or privacy constraints associated with data or model usage in the request. |
4. Definitions
- Ad Hoc AI-Based Report: A single-use or urgent analytics deliverable that applies AI or ML-driven techniques, beyond existing standard or scheduled deliverables.
- AI-IRB: Oversight body ensuring that AI solutions adhere to organizational ethics and compliance requirements, including privacy and fairness.
- Data Wrangling: The process of cleaning, structuring, and enriching raw data into a desired format for advanced analytics.
- Model Interpretability: The ability to understand and explain how an AI model arrives at specific outputs or insights.
- MLOps: The set of practices to deploy and maintain machine learning models in production reliably and efficiently, bridging data science and IT operations.
- 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. |
5. Procedure Activities
5.1 Request Initiation
- Requestor completes an Ad Hoc AI Reporting Form (or uses the request ticketing system) describing business question(s), data scope, urgency, and any compliance concerns.
- AI IRB Liaison reviews the request for potential ethical, bias, or compliance triggers. If flagged, additional approval steps are inserted.
5.2 Requirements Gathering & Validation
- AI Data Analyst meets with Requestor to confirm details:
- Clarify the requested timeframe, desired granularity, data sources.
- Identify potential use of any existing AI/ML models or creation of new ones.
- Data Scientist ensures model reusability or the need for any custom-coded solution.
- Quality Assurance reviews the request scope for test and validation feasibility.
- (Conditional) AI IRB Liaison obtains rapid ethical clearance if personal/sensitive data or high-impact AI usage is foreseen.
5.3 Feasibility & Prioritization
- AI Data Analyst:
- Assesses data availability, complexity, and existing pipeline readiness.
- Drafts resource/time estimate for producing the report.
- MLOps Engineer verifies environment readiness for any specialized computations.
- AI Data Analyst recommends priority level (e.g., Critical, High, Medium, Low).
5.4 Development & Testing
- Data Scientist and AI Data Analyst:
- Gather relevant data from internal or external sources.
- Perform data wrangling, feature engineering as needed.
- Apply or adapt ML models, if required.
- MLOps Engineer:
- Manages any code integration and ensures version control of data scripts.
- Sets up or reconfigures the environment for large-scale model usage, if necessary.
- Quality Assurance:
- Validates intermediate results, checks for anomalies.
- Logs any defects into the AI-SDLC defect tracking system and ensures they are addressed.
5.5 Security & Compliance Checks
- AI Ethics/Compliance verifies if final outputs are consistent with data usage agreements, privacy constraints, and the AI-IRB’s guidelines, specifically auditing for any recursive self-improvement subroutine deviations.
- Data Scientist ensures the interpretability portion (if model-based) is documented and that usage of data does not violate any HIPAA, GDPR, or internal data policies.
5.6 Approval & Delivery
- AI Data Analyst compiles the final AI-based report (tables, charts, predictions).
- Quality Assurance conducts final acceptance testing.
- Requestor (and potentially AI IRB Liaison if flagged) reviews the final deliverable for acceptance, utilizing Exochain Peer Reviews for automated validation of reporting logic.
- Operations manages secure distribution or automation, if needed.
- Technical Support is briefed on the output to handle user queries.
5.7 Documentation & Archiving
- AI Data Analyst or Data Scientist updates standard knowledge repository with the request details, final approach, data sources used, and results.
- MLOps Engineer logs final code or data transformations into the organization’s Git repository for reproducibility.
- Quality Assurance stores final sign-off in the official AI-SDLC documentation library.
5.8 Post-Implementation Review
- AI Data Analyst schedules a post-implementation review if the ad hoc request led to critical organizational decisions or if there were complex modeling steps.
- All relevant roles provide lessons learned (technical, compliance, ethical).
- AI IRB Liaison updates risk registers or ethical guidelines if new issues were uncovered.
6. Tools, Forms, & Software/Technology
- Ad Hoc AI Reporting Form: Standard template that captures request details (business objective, data sources, compliance flags).
- AI-SDLC Ticketing System: Tracks status, changes, approvals.
- Version Control Repos: Git-based environment for code and data transformation tracking.
- MLOps/AI Pipeline: Automated pipeline for ML model training and deployment.
- AI-IRB Registry: Oversees approval logs and compliance flags.
7. References
- SOP-1000-01-AI: AI-SDLC Overview
- SOP-1040-01-AI: Requirements Definition
- SOP-1005-01-AI: Release Planning
- AI-IRB Charter & Guidelines (latest revision)
8. Revision History
| Version | Date | Description | Author/Change Owner |
|---|---|---|---|
| 1.0 | 2025-01-30 | Initial release of AI Ad Hoc Reporting SOP | AI Data & Reporting Office |
| N/A | N/A | N/A | N/A |
Signature Approval
- Owner: [Name], AI Data & Reporting Office
- Approver: [Name], Chief AI Officer (CAIO)
Document Ends
@startuml
skinparam participantPadding 20
skinparam boxPadding 10
skinparam notePadding 5
skinparam noteBackgroundColor #FEFECE
participant “Requestor” as R
participant “AI IRB Liaison” as IRB
participant “AI Data Analyst” as A
participant “Data Scientist” as DS
participant “MLOps Engineer” as M
participant “Quality Assurance” as QA
participant “AI Ethics/Compliance” as E
participant “Operations” as OP
participant “Technical Support” as T
== Request Initiation ==
R -> R: Fill out Ad Hoc AI Reporting Form
R -> IRB: Submit request for potential ethical/regulatory flag
alt If flagged by IRB
IRB -> R: Request additional info or updates
R -> IRB: Provide clarifications
end
IRB -> A: Forward validated request details
== Requirements Gathering & Validation ==
A -> R: Schedule meeting to refine request
A -> DS: Check if new/existing AI/ML models are needed
A -> QA: Ensure request is testable
alt Additional IRB approval needed
IRB -> A: Provide final compliance approval
end
== Feasibility & Prioritization ==
A -> A: Assess data availability & complexity
A -> M: Confirm environment readiness
A -> A: Draft time/resource estimate
A -> R: Recommend priority level (Critical/High/Med/Low)
== Development & Testing ==
A -> DS: Gather data and perform data wrangling
DS -> M: Request environment setup or code integration
M -> M: Update code repo & environment (version control)
QA -> A: Validate intermediate results
A -> QA: Address any defects found
DS -> DS: Re-test with revised data/model
QA -> QA: Approve or log additional defects
== Security & Compliance Checks ==
E -> A: Verify final outputs vs. data usage agreements
DS -> E: Confirm model interpretability & compliance
alt If compliance issue found
E -> DS: Mitigation or revision required
DS -> E: Provide updated approach
end
== Approval & Delivery ==
A -> QA: Submit final ad hoc report for acceptance
QA -> QA: Final test & acceptance
QA -> R: Present final deliverable for approval
alt Accepted
R -> OP: Request distribution if needed
OP -> T: Notify for Tier-1 support readiness
else Rejected
R -> A: Request modifications or rework
A -> DS: Resolve issues
end
== Documentation & Archiving ==
A -> M: Ensure code, transformations are in Git
A -> QA: Archive sign-off in AI-SDLC library
A -> T: Provide user instructions or brief training tips
== Post-Implementation Review ==
A -> A: Schedule post-implementation review (if critical)
A -> R: Gather feedback, lessons learned
A -> IRB: Update risk or ethical registers if new concerns
@enduml
Short textual explanation:
This sequence diagram for SOP-1030-01-AI (Ad Hoc AI Reporting) shows how an ad hoc reporting request is initiated, reviewed by AI IRB if necessary, clarified by the AI Data Analyst, and then developed and tested by the Data Scientist and MLOps Engineer. Quality Assurance and AI Ethics/Compliance ensure correctness and compliance. After final approval, Operations and Technical Support manage distribution and user support, while the AI Data Analyst coordinates documentation and schedules a post-implementation review if needed.