AI Adoption Essentials: Laying the Groundwork for Success in Enterprises
Jan 25, 2024
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Matt Hicks, President and CEO of Red Hat discusses the essentials needed for successful AI adoption in enterprises. Topics include evaluating AI goals, expected focus in the coming year, use cases and industry applications, trust and control in AI models, tips for AI adoption, and concerns and excitement for the future of business and technology.
Enterprises are evaluating AI and generative AI with structure and caution, focusing on patterns and models that are known to work.
Investing in infrastructure that allows for scalability, cost optimization, and experimentation is key to long-term success in AI adoption.
Deep dives
AI adoption in enterprises
Enterprises are evaluating AI and generative AI with structure and caution, focusing on patterns and models that are known to work. Many companies are further along in AI adoption than expected, thanks to the foundation of data science and machine learning. AI and generative AI are expected to be significant investment areas in the coming year, with a choice between massive cloud-based models and more specialized, open-source models. Companies are looking at various use cases, including customer interactions, support augmentation, autonomous driving, telecommunications, and factory automation.
Infrastructure and AI adoption
Infrastructure decisions are crucial in AI adoption. While direct coupling with NVIDIA hardware offers high capabilities, opting for an abstraction layer like PyTorch provides flexibility with different infrastructures. Companies should carefully consider whether to rely solely on public cloud stacks or keep hybrid options open. Investing in infrastructure that allows for scalability, cost optimization, and experimentation is key to long-term success in AI adoption.
Securing AI adoption
Enterprises must navigate the risks associated with AI adoption, including licensing models, copyright infringements, and ownership of generated outputs. Trust in model producers, understanding license terms, and controlling data origin are crucial considerations. Organizations need to plan their AI journey, differentiate between efficiency and differentiation goals, and establish a culture that embraces AI while addressing the impact on teams. Balancing cost optimization, ROI, and continuous integration of AI into business processes are also vital for long-term success.
Future trends and concerns
The optimization of AI models for CPU usage and the trend towards smaller, specialized models offer opportunities for innovation and closer deployment to end-users. However, the increasing complexity and layers of AI can be a concern, as it becomes harder to understand the entire stack and potential risks. The balance between excitement and concern lies in the open innovation and removal of barriers, allowing for greater creativity and possibilities in the future of AI.
What essentials do you need to have in place to make Generative AI work in the enterprise? Join me as I discuss this important topic with Matt Hicks, President and Chief Executive Officer, Red Hat.
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