TL;DR
Begin with the recurring workflow constraint, then choose the minimum intervention and proof needed to scale.
What the paper develops
Most AI portfolios can show activity long before they can show value. This paper explains why tool-first adoption creates that gap and replaces it with a constraint-first operating model: identify the recurring workflow, name what limits value, compare AI with simpler interventions, and decide what evidence would justify more reliance. It connects experimentation to a five-gate portfolio cadence so that pilots become comparable investment decisions rather than isolated demonstrations.
The operating move
Before buying or piloting anything, name the recurring workflow constraint, compare AI with simpler interventions, assign an owner, and define the evidence that would justify another increment of investment.
Inside the white paper
- How to distinguish a workflow constraint from a technology opportunity
- A five-gate path from intake and intervention routing to value realization
- A practical scorecard, evidence pack, and decision-rights model for scaling
Sources and notes
- Alex Singla, Alexander Sukharevsky, Bryce Hall, Lareina Yee, Michael Chui, and Tara Balakrishnan, "The state of AI in 2025: Agents, innovation, and transformation," McKinsey & Company, November 5, 2025. Verified July 7, 2026. mckinsey.com
- James Ryseff, "The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI," RAND Corporation, 2024. Verified July 7, 2026. rand.org
- Arun Chandrasekaran, "Why 50% of GenAI Projects Fail - And How to Beat the Odds," Gartner, January 26, 2026. Verified July 7, 2026. gartner.com
- Alexis Krivkovich and Lucia Rahilly, "AI is everywhere. The agentic organization is not yet," The McKinsey Podcast, April 2, 2026. Verified July 7, 2026. mckinsey.com
- NIST, "AI Risk Management Framework Core," excerpt from AI RMF 1.0, 2023. Verified July 7, 2026. airc.nist.gov