In an era where 95% of generative AI pilots fail to reach production and 80% of AI projects fail — double the rate of non-AI IT projects — the decisive factor separating success from failure is not the sophistication of algorithms, but the rigor applied before a single line of code is written.
This article builds on the core Ideation & Feasibility phase of the Managing Innovative AI Projects (MIAP) framework from the book Managing Innovative AI Projects (Jayakumar K R and Prof. Alain Abran, 2025) and extends it through the Discovery Vectors lens introduced in AI Project Pulse Issue No. 4. It introduces Value Discovery — a structured framework enriched with the latest industry data and practitioner insights — transforming Ideation & Feasibility from a cursory exercise into a disciplined, six-investigation process:
- Strategic & Ethical Scoping
- Technical Feasibility Assessment (covering data readiness, compute costs, and regulatory alignment)
- Hybrid Requirements Modelling
- Premortem Risk Analysis
- Standards Alignment
- Emerging AI-Assisted Ideation Tools
These six investigations together answer the fundamental question: Is this problem worth solving with AI, and will it deliver measurable value?
Readers will discover how to avoid the most common mistake in AI project management that of applying the same discovery rigor to every project type. The article culminates in an actionable Go/No-Go decision framework that saves months of wasted effort, millions in sunk costs, and preserves organizational credibility, making this essential reading for project leaders, technical teams, executives, and compliance officers navigating the complex landscape of AI innovation. Access the full article here.