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AI is not new – in fact the term was coined in 1956. Why then, if it’s been around for decades, and we are now on our third major wave of AI interest and hype, do we keep making the same mistakes that have brought us to two previous AI Winters? Are we still overpromising what AI can deliver? In this podcast hosts Kathleen Walch and Ron Schmelzer will discuss why we are still overpromising and underdelivering on what AI can do, how we can address this, and what can be done to set realistic expectations to ensure AI project success.
Show Notes:
- CPMAI Methodology
- AI Today podcast: AI Failure Series- AI projects are NOT like traditional software development projects
- AI Today Podcast: AI Failure Series – ROI Misalignment
- AI Today Podcast: AI Failure Series- Data Quality Issues
- AI Today Podcast: AI Failure Series – Data Quantity & Data Understanding Issues
- AI Today Podcast: AI Failure Series – Not Understanding the Model & Data Lifecycle
- AI Today Podcast: AI Failure Series – Believing Vendor & Industry Hype
- AI Today Podcast: AI Failure Series – The Real World Mismatch
- AI Today Podcast: AI Failure Series – Iteration Time & Proof of Concept vs. Pilots
- AI Today Podcast #005: The AI Winters
- AI Today Podcast #67: AI in the museum: Interview with Kristi Delich, Deputy Director, Office of Visitor Services, Smithsonian Institution
