Resources
Articles
- Demystifying AI ML – Part I GOFAI
- Demystifying AI ML – Part II
- Demystifying AI ML – Part III
- Demystifying AI ML – Part IV NN Brainworks
- AI currency between Snake Oil and New Oil
- Artificial General Intelligence: A Soul-Searching Reflection
- Applied AI Project Typology: An Extended Set of Illustrative Examples
- AI Project Typology Meets the Ground Truth of the IT Services Market
- AI Mirage: Beyond the Code – Why Human Expertise Matters More Than Ever
- What We Mean by “AI Toolscape” — Context & Need
- A Curated Journey Through Five Faces of AI Innovation
- The Discipline of Delivering AI Value: MIAP Framework
- AI Governance, Risk & Compliance Platforms: AI Toolscape Series – Part 3
- AGI Mirage: The Human Reality Behind the Hype
- Discovery Vectors : Value Discovery
- AI Project Pulse – The Essential Recap
- AI Tools for Prevention and Correction, The Two Pillars of AI Quality & Reliability
- Community Dialogs – Srikanth (Issue 5)
- Beyond the Hype: Classifying Your AI Project Type and Why It’s the First Step to Success
- What We Mean by “AI Toolscape” And Why It Matters – Brief
- Navigating the AI Tools Landscape: A Map for Successful Delivery
- Community Dialogs – Yokesh (Issue 5)
- Intro to Issue No. 6 AI Project Pulse
- Ideation & Feasibility: The Decisive Phase of AI Project Success
- Abstract – Ideation & Feasibility
- Data Discovery: The Decisive Phase of AI Project Success
- The Future of India in the Intelligence Economy – a conversation with Ravindra Barlingay
- Audit Happens
- a life – detached
- The Emperor’s New Algorithm: A Guide to Surviving the AI-fication Epidemic
- Technical Discovery: The Decisive Phase of AI Project Success
- Intro to Issue 09 – Architectural Investigation
- Technical Discovery – Investigation 2: Key Performance Dimensions
- Technical Discovery: Investigation 3 – Validation Approaches
- Technical Discovery Vector
- Discovery Vectors – A New Way to Navigate AI Projects
- Technical Discovery: Investigation 4 – Experiment Tracking and Management
Standards & Technical Documents
- ISO/IEC 5259 series — Data quality for analytics and machine learning
- ISO/IEC 5338:2023 — Artificial intelligence — AI system life cycle processes
- ISO/IEC 8183:2023 — Artificial intelligence — Data life cycle framework
- ISO/IEC 19761:2011 — Functional size measurement (Reviewed in 2025)
- ISO 31000:2018 — Risk management — Guidelines
- ISO/IEC 20546:2019 — Big data — Overview and vocabulary
- ISO/IEC 22989:2022 — Artificial intelligence — Concepts and terminology
- ISO/IEC 23053:2022 — Framework for AI systems using machine learning
- ISO/IEC 23894:2023 — Artificial intelligence — Guidance on risk management
- ISO/IEC TR 24030:2024 — Artificial intelligence — Use cases
- ISO/IEC 38507:2022 — Governance implications of artificial intelligence
- ISO/IEC 42001:2023 — Artificial intelligence — Management system (AIMS) framework
- ISO/IEC 42005:2025 — Artificial intelligence — Guidance for early AI system impact assessments
- NIST (2023) — AI Risk Management Framework
Articles & Reports
- Chen, L., et al. (2023). Data acquisition: A new frontier in data-centric AI.
- Dahmen, U., Osterloh, T., & Roßmann, J. (2023). Structured validation of AI-based systems by virtual testing.
- Dubey, A., Yang, Z., & Hattab, G. (2024). A nested model for AI design and validation.
- Jain, N. S. (2024). Data preparation algorithm for AI workflows.
- McKinsey Global Institute. (2023). The state of AI.
- Mendez, F. (2020). Hybrid modeling for requirements in AI projects.
- Mollick, E., De Cremer, D., Neeley, T., & Sinha, P. (2024). Generative AI: The insights you need from Harvard Business Review.
- Plätke, O., & Geibel, R. C. (2024). The use of artificial intelligence for idea generation in the innovation process.
- Vazquez, D., et al. (2025). Preparing good data for generative AI: Challenges and approaches.
- Ye, R., et al. (2025). The design space of recent AI-assisted research tools for ideation, sensemaking, and scientific creativity.
Industry & Organizational Resources
- DAMA International. (2017). Data Management Body of Knowledge (DMBOK v2).
- Select Star. (2024). Data preparation for AI: Best practices and step-by-step guide.
- Deloitte. (2023). Transforming AI outcomes with effective data readiness.
- Futurium. (2024). Implementing AI governance: From framework to practice.
- Microsoft. (2024). Manage AI operations and deployment (MLOps & GenAIOps).
- Martyr, R. (2025). Managing the AI lifecycle in 2025: A comprehensive guide.
- Quanta Intelligence. (2024). New standards for model validation in tech.
- Evidently AI. (2024). Open-source ML monitoring and observability.
- Google. (2023). Rules for machine learning.
- Singapore PDPC. (n.d.). Singapore Model AI Governance Framework
- iTech Creations. (2023). Understanding data acquisition and data preparation for AI project cycle
- IBM. (2024). What is AI model lifecycle management?