SUMMARY: As AI within the Enterprise matures, we look at 10 concerns and challenges that are still causing Chief AI Officers to worry about success in the future. 

SHOW: 1040

SHOW TRANSCRIPT: The Enterprise AI Show #1040 Transcript

SHOW VIDEO: https://youtu.be/RyB4m17YK_4

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SHOW NOTES:

THESIS: After spending time with a number of Enterprise companies, what are a list of challenges and concerns they still have in implementing GenAI across a broad set of use-cases within the Financial Services industry?

1. Everybody started with what was available (e.g. CoPilot)
2. Enterprise implementations (now) aren’t autonomous
3. Rising costs are the looming concern
4. Governance is a rising concern
5. Measurements of improvement are available, but varied
6. Explaining measurements is complicated
7. Explaining trust is more complicated
8. Use-cases are fragmented, but there if you apply the technology, but not always obvious
9. De-centralized (shadow AI) to Centralized to De-centralized (semi-controlled) 
10. The learning curves are very asymmetrical across teams
11. Not everyone has access to Mythos or GPT-5.5-Cyber (yet)

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