At the Red Hat Summit and Ansible Fest in Boston this month, much of the hype and overpromising about generative AI took a back seat to conversations about how organizations can actually build and deploy AI for their own business using their own data.
Of course, this is a Red Hat Summit, and there was plenty of focus on core topics like open source, with the release of Red Hat Enterprise Linux 10, and automation and management with Ansible. But like everything nowadays, AI took up a lot of the attention at the conference, but at least much of it was refreshingly and critically practical.
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Rather than the more hyped AI-areas such as AI assistants, which a recent Aberdeen/ZDNet poll found to be of limited interest to a majority of users, most of the sessions and even major announcements were focused on technologies and strategies that business can use today to help them get the most out of AI while leveraging their own data in a secure and efficient manner.
For example, there was a great deal of focus on inferencing, the process of running an AI model with new data to make predictions or decisions. Announcements on technologies such as vLLM and llm-d provide improved scaling and deployment options that simplify the complexities of inferencing while spreading compute loads.
Aberdeen research has consistently found high levels of interest around inferencing for a majority of businesses, with 37% of organizations looking to deploy AI inference internally. These organizations want AI solutions that help them use their own data to drive AI solutions that are tailored to meet their specific needs while improving data sovereignty and internal control.
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Along with inferencing, we also saw a large focus on agentic technologies and emerging technologies that can help businesses automate and integrate with custom-built AI agents. Many of these discussions focused on MCP (Model Context Protocol), a new open source standard designed to ease the creation of agents built using custom data and integrated with custom services.
Again, rather than focusing on hyped AI assistants and chatbots, the Summit looked at this technology for its ability to help businesses automate critical tasks, streamline operations and free up human resources for more strategic initiatives. And there were even honest discussions about the security implications, especially as some outstanding issues such as authentication are expected to be improved in the next version of MCP.
This tied into one of the most welcome discussions at the Summit on how to enable the secure utilization of AI within a business context. While discussions about AI often include questions about data integrity, especially when public LLMs are involved, talks about the security implications of building and deploying AI are often hard to find.
Again, much of this focus aligns well with the research that Aberdeen has been doing into AI use in businesses. Our research shows that cybersecurity is the top of concern for companies looking to leverage AI and the top barrier to effective AI deployment.
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It will be interesting to see if AI follows the same path as other over-hyped technologies. There are already convincing arguments that AI is on the downward path of public sentiment.
But in past hype cycles, we’ve seen that just when the over-the-top predictions turn out to be false is exactly when businesses and practical users are putting the technology to effective use outside of the hype cycles. Looking at the focus of the Red Hat Summit/Ansible Fest on practical and business-ready use cases for AI, that may be just what’s happening now.
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