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