Scaling GenAI Series
Shatter the Myths of Generative AI
Debunking misconceptions and delivering transformative outcomes
The biggest challenges to driving impact with AI has little to do with AI and everything to do with humans. Nowhere is this greater than with Generative AI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. Innumerable GenAI projects are setting themselves up for failure resulting, at best, in perpetual PoC purgatory, and, at worst, jeopardizing the company’s overall attempts at AI-driven transformation.
Watch our on-demand webinar with Rowan Curran, industry analyst at Forrester Research covering AI and data science, and Dr. Kjell Carlsson, head of AI Strategy at Domino, where we will debunk the most harmful myths and discuss examples of how advanced AI teams in industries are shattering these myths and delivering transformative outcomes.
What are the biggest misconceptions around driving business value with GenAI? What is the impact and what are the best practices to pursue instead?
- Generative AI is the most Important kind of AI
- Bigger is better
- You don’t need data scientists for Generative AI
- I need to hire prompt engineers
- LLMOps is a separate discipline from MLOps
- Someone else is responsible for responsible AI