Greg Pavlik, executive vice president, AI and data management services, Oracle Cloud Infrastructure, Oracle, chats with Bob Evans about the transformative impact of generative AI on enterprise processes, the challenges of moving from prototype to production, and how Oracle is leveraging AI to optimize its own cloud services and support its customers.
Efficient AI Model Economics
The Big Themes:
- GenAI and its practical application: GenAI has emerged as a major focus in enterprise settings, with organizations exploring its potential to enhance core business processes. While many have successfully integrated generative AI into their workflows, others face challenges moving beyond continuous prototyping to actual production deployment. Key to overcoming these hurdles is understanding the limitations and capabilities of AI models.
- Economic advantages of smaller AI models: Smaller AI models, such as the 35 billion parameter models from Cohere, represent a significant economic shift in the AI landscape. These models, which can be run on a single GPU, offer a more cost-effective alternative to larger models, reducing the financial burden associated with AI deployment. These mid-sized models are not only cheaper but also capable of covering the majority of use cases effectively.
- AI integration into Oracle’s software-as-a-service (SaaS) portfolio: Oracle has integrated generative AI into its software-as-a-service (SaaS) portfolio, enhancing various business functions without additional charges. This integration includes features like automated job postings and Salesforce optimization. Greg notes that this approach adds significant value to Oracle’s offerings.
The Big Quote: “People try to chase the latest and be on the frontier but never really commit to making something work for production.”
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