With extensive experience in AI and machine learning dating back to 1998, Cohen-Dumani brings valuable insights into the historical and present-day landscape of AI, emphasizing the importance of foundational knowledge, expertise, and knowledge management in making AI work effectively within organizations.

Tune in to this enlightening conversation as they discuss the attention and resources that must be invested in unstructured data and knowledge to leverage the full potential of AI.

Key takeaways:

- A foundational reference architecture is critical for making sense of data and discerning between vendors' aspirational capabilities and reality.
- Traditional long-term technology planning is no longer applicable in the age of AI and large language models (LLMs) due to the unpredictable nature of AI's uses and leveraging capabilities.
- Executives should personally experiment with AI tools and allow more freedom for workers to adopt AI, rather than stifling innovation.
- Building an extensible and expandable data foundation and good enterprise architecture is crucial to avoid data silos and maintain consistency in data.

Quote from the show:
"I think one of the challenges that organizations have is they're not investing the time, the effort, the money, the resources, and the attention on unstructured data, on knowledge. You know, if you look at any accounting department, they spend inordinate amount of time and resources on numbers, on transactional data. But if you look at how much effort is put on unstructured data, it's night and day. And yet unstructured data is 80+% of the data most organizations have." - Daniel Cohen-Dumani

Links:
LinkedIn: https://www.linkedin.com/in/dcohendumani/
Website: https://www.withum.com

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