Harvard Business School assistant professor Iavor Bojinov presents a structured five-phase approach—project selection, model building, rigorous evaluation, strategic adoption, and ongoing management—to navigate AI’s probabilistic challenges, embed ethical safeguards, and maximize organizational impact.
Key points
- Defines a five-phase AI project lifecycle: selection, development, evaluation, adoption, and management.
- Emphasizes hypothesis-driven experimentation to tackle AI’s probabilistic nature and optimize performance.
- Integrates ethical AI principles—fairness, transparency, privacy—throughout development to build user trust.
Why it matters: Embedding structured governance, ethical safeguards, and iterative evaluation into AI lifecycles dramatically reduces failure rates and turns experiments into sustainable, value-generating solutions.
Q&A
- Why do AI projects fail more often than traditional IT initiatives?
- What is responsible AI and why integrate it early?
- How can experimentation improve AI project outcomes?
- What metrics should organizations use beyond predictive accuracy?
- How do you maintain user trust during AI adoption?