TL;DR
Compare automation, custom build, vendor buy, hiring, and waiting as portfolio choices.
What the paper develops
AI proposals are often evaluated against doing nothing instead of against the full set of available responses. This paper creates a common decision frame for automation, custom build, vendor purchase, hiring, and waiting. It compares value mechanism, operating cost, time to evidence, reversibility, dependency, and risk so leaders can choose the smallest credible intervention without mistaking novelty for strategic fit.
The operating move
Compare automation, custom build, vendor purchase, hiring, and waiting against the same value, risk, capacity, and reversibility criteria before treating AI as the default answer.
WORKFLOWCONTROL EVIDENCEHUMAN OWNER
Inside the white paper
- A common comparison model for five intervention paths
- How sequence, reversibility, capacity, and operating cost change the decision
- Evidence gates for funding, expanding, redirecting, or stopping the choice
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
- NIST, "AI Risk Management Framework Core," excerpt from AI RMF 1.0, 2023. Verified July 7, 2026. airc.nist.gov
- Johannes-Tobias Lorenz, Joshan Cherian Abraham, Robert Levin, and Douglas Ziman, "From promise to impact: How companies can measure and realize the full value of AI," McKinsey & Company, April 24, 2026. Verified July 7, 2026. mckinsey.com