Advisory Workstreams
- How to use AI in our business: business process evolution + data estate
- How to use AI in our products: technology direction + product development
- How to engage with large scale infrastructure providers
Advisory Workstreams
Section titled “Advisory Workstreams”- Augmentation vs. automation
- Prompt engineering ([Example prompt for synthetic data generation (for training a [model](Example%20prompt%20for%20synthetic%20data%20generation%20(for%20training%20a%20[model) vs. fine tuning vs. pre-training
- Model capabilities vs. company capabilities
- Evals: making progress towards your North Star
- Data flywheel
- Product management in the age of AI
- Using AI for internal workflow audits
2025-11-11-Tue
Section titled “2025-11-11-Tue”- Core competence vs. borrowed competence
- Eval Strategy (Or, Knowing how good we are): what are we measuring, how do we measure it, what does great look like?
- Models: off the shelf vs. fine tuned vs. pre-trained. Is there something good enough? When will that thing arrive?
- Static vs dynamic strategy: what will change as models improve? What is durable, what will get subsumed?
- Product feedback loop
- Prediction Strategy (Or, what part of the value chain are you using AI to lower the cost of?): how quickly will it cost reduce, what’s the cost floor, what complements become important?
- Business process change? The big lever!
An AI audit of a company examines how artificial intelligence systems are designed, deployed, and governed — ensuring they’re ethical, compliant, secure, effective, and aligned with business goals. The specific elements depend on the scope (technical, ethical, legal, or operational), but here’s a comprehensive breakdown of the key components:
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Governance & Strategy
Section titled “Governance & Strategy”AI strategy alignment: How AI initiatives support the company’s broader mission and objectives.
Governance structure: Who is accountable for AI decisions (e.g., AI ethics board, data governance committees).
Policies & documentation: Presence of AI use policies, responsible AI principles, and lifecycle documentation.
Change management: How updates to AI systems are tracked and approved.
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Data Management
Section titled “Data Management”Data sources & provenance: Where data originates and how it’s validated.
Data quality & representativeness: Completeness, bias, and relevance of datasets.
Data privacy & security: Compliance with GDPR, CCPA, or other regulations.
Data governance processes: Consent management, retention policies, anonymization.
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Model Development & Lifecycle
Section titled “Model Development & Lifecycle”Model design: Choice of algorithms, rationale for architecture.
Training process: Documentation of parameters, hyperparameter tuning, training data lineage.
Testing & validation: Cross-validation, performance metrics, overfitting checks.
Version control: Tracking of model versions and associated datasets.
Continuous learning: How models are retrained and monitored over time.
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Ethical & Fairness Considerations
Section titled “Ethical & Fairness Considerations”Bias & discrimination testing: Systematic review of model outcomes for protected groups.
Explainability & transparency: Ability to explain AI decisions to end users and regulators.
Human-in-the-loop: Safeguards for human oversight and intervention.
Accountability: Clear assignment of responsibility for AI-driven outcomes.
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Security & Robustness
Section titled “Security & Robustness”Adversarial resilience: Testing against data poisoning or prompt injection (for LLMs).
Access control: Who can access model code, data, and deployment systems.
Incident response: Procedures for handling AI system failures or breaches.
System monitoring: Logging, audit trails, and anomaly detection.
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Compliance & Legal
Section titled “Compliance & Legal”Regulatory alignment: Compliance with frameworks like:
EU AI Act
U.S. AI Executive Order
ISO/IEC 42001 (AI management systems)
IP rights: Ownership of AI-generated content or models.
Liability management: Procedures for errors or harms caused by AI.
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Performance & Value Assessment
Section titled “Performance & Value Assessment”Business KPIs: ROI, productivity gains, or cost reduction attributable to AI.
Technical KPIs: Accuracy, precision, recall, latency, uptime.
User satisfaction: End-user trust, adoption, and usability feedback.
Sustainability impact: Energy consumption and carbon footprint of AI workloads.
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Documentation & Audit Trail
Section titled “Documentation & Audit Trail”Model cards / system cards: Standardized summaries of model purpose, data, and risks.
Risk registers: Identified AI risks and mitigation actions.
Audit logs: Records of model updates, access, and performance monitoring.
Evidence repository: Documentation for compliance verification.
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Optional Add-ons (for a Mature audit)
Section titled “Optional Add-ons (for a Mature audit)”Third-party assessment: Independent validation or certification.
Benchmarking: Comparing AI maturity against industry standards.
Ethical impact assessment: Structured review of social or societal consequences.
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Would you like me to tailor this framework for a specific industry (e.g., finance, healthcare, retail, or government)? Each has different audit emphases (like bias/fairness in lending vs. explainability in medical AI).