The evolving landscape of AI emphasizes the need for businesses to prioritize inference over merely developing new models. Many enterprises, including OpenAI, are now focusing on creating robust infrastructures that can support the daily operations of AI models rather than solely investing in new model development. This shift is reflected in IDC’s prediction that spending on inference infrastructure will soon surpass spending on training by 2025, indicating a significant pivot towards applying existing AI capabilities in efficient ways.
Company leaders realize that without relevant and contextualized data, even the most sophisticated AI models lose their value. Larry Ellison of Oracle points out that the true potential of AI lies in linking models to sensitive and critical business data. Furthermore, the integration of retrieval-augmented generation (RAG) technologies shows promise in overcoming the limitations of traditional models by enhancing their contextual awareness and relevance. The emphasis is now on how enterprises can effectively leverage their data to create value through inference, rather than merely competing to build larger AI models.
To navigate this shift, businesses need to adopt a clear strategy that includes understanding their data environments and utilizing cloud services that allow models to be adjusted based on real-time access to enterprise data. Organizations must also develop tight governance and monitoring practices to ensure AI applications adhere to necessary standards and effectively support business processes. This comprehensive approach ensures that AI can be integrated as a vital operational tool rather than just an experimental endeavor.
👉 Pročitaj original: CIO Magazine
