HBR IdeaCast Setting AI Projects Up for Success
45 snips
Dec 5, 2023 Iavor Bojinov, an assistant professor at Harvard Business School and former LinkedIn data scientist, shares his expertise on successful AI project management. He discusses the high failure rate of AI initiatives, stressing the importance of strategic setup. Listeners learn about five critical steps: selection, development, evaluation, adoption, and management. The conversation also delves into balancing innovation with ethical standards, the significance of user trust, and the cyclical nature of project management to harness AI's potential effectively.
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AI vs. IT Projects
- AI projects, unlike IT projects, are not deterministic, leading to unpredictable outcomes.
- This probabilistic nature introduces uncertainty and complexity, increasing the failure rate compared to traditional IT projects.
Trust in AI
- Even successful AI products can fail due to lack of user trust.
- Building a valuable product isn't enough; users must trust it to adopt it.
Project Selection
- Prioritize AI projects based on impact and feasibility, including ethical implications.
- Consider data availability, infrastructure, privacy, fairness, and transparency from the outset.