Three years since the launch of ChatGPT, what does the landscape of Enterprise AI look like today? What’s working, what’s struggling and what’s still unknown?

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SHOW NOTES:

HOW ARE ENTERPRISES USING AI IN LATE 2025?

5% have a clear vision of how to apply Predictive and Generative AI to a set of use-cases that drive differentiation, productivity improvements and cost reductions. They are keeping the details close to the vest.
10% have allocated about 3-5% of their IT budgets to AI, typically from a C-level mandate, and have given it to Microsoft or Google. They have checked “the business is AI-enabled” and signaled to the market that they have fully embraced AI. The market is rewarding these companies at higher multiples.
85% aren’t sure what use-cases to focus on, have unrealistic expectations during POCs, and are focused on the “no” areas instead of their own learning curves.
Enterprises don’t have great visibility into AI costs, and limited baselines of what AI should cost - pay for outcomes, pay for seats, pay for tokens, or pay for GPUs?
Enterprises don’t have easy access to GPUs outside of via SaaS services - makes it challenging for Private or Sovereign AI demand to be met
Right now, there is no simple way for Enterprises to build AI Agents
Right now, there is no simple way for Enterprises to share AI experience / learning curve - AI is a very individualized experience

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