Exploring the Misconception of Resource Requirements in AI Projects

#ai #aifailure
Join us on CXOTalk episode 778 as we delve into the complexities of AI failure with our guest, Iavor Bojinov, Assistant Professor at Harvard Business School, and QuHarrison Terry, Head of Growth Marketing at Mark Cuban Companies. In this episode, we explore the various types of AI failures that can occur, the causes behind them, and how they differ from traditional technology and IT projects.
Our experts also address the implications of these differences and distinguish between technology failures and human errors that can cause AI failures. In the second segment, we discuss solutions to address AI failure, including practical advice on how to avoid it in the first place. We also touch on the crucial ethical and privacy considerations associated with AI.
This is a must-watch episode for business leaders building an AI group within their organization. Our guests offer valuable insights into AI governance, which can help prevent failures and maximize opportunities.
The conversation includes these topics:
► Understanding AI operations and ai project failures
► Differences between AI projects and traditional technology or IT projects: AI is probabilistic
► Five steps of an AI project
--- Step 1: Project Selection - Choosing the Right Project
--- Step 2: Development - Building the Prototype
--- Step 3: Evaluation - Testing on Real People
--- Step 4: Deployment and Scaling - Going from Pilot to Full Launch
--- Step 5: Management - Monitoring and Preventing Failures
► The shift from rule-based expert systems to probabilistic AI
► Implementing AI in small businesses
► AI pilot projects should have direct implications on revenue
► Categories of AI failure
► Successful projects integrate AI into processes and business operations
► Data sets are a key difference between traditional IT projects and AI projects
► Digital Transformation vs. AI
► Ethical considerations of AI
► Principles of privacy by design
► Addressing bias in decision-making based on data
► Governance and regulation of AI
► Advice to business and technology leaders on preventing AI failures
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Read the complete transcript and watch more interviews: https://www.cxotalk.com/episode/why-ai-projects-fail-how-stop-it
Iavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research and writings center on data science strategy and operations, aiming to understand how companies should overcome the methodological and operational challenges presented by the novel applications of AI. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washingon Post, and Reuters, among other outlets.
Professor Bojinov is also the co-creator of the first-year required MBA course “Data Science for Managers” and has previously taught the “Competing in the Age of AI” and “Technology and Operations Management” courses. Before joining Harvard Business School, Professor Bojinov worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. He holds a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King’s College London.
QuHarrison Terry is Head of Growth Marketing at Mark Cuban Companies, a Dallas, Texas venture capital firm, where he advises and assists portfolio companies with their marketing strategies and objectives.
Previously, he led marketing at...