Enterprises are integrating AI into their operations and develop agent pilots. As we deploy more capable agent-based intelligent applications, including multi-agent systems, we will utilize agent-centric models for planning, reasoning, coordinating, and learning. Such agents will incorporate neurosymbolic components and agent-centric models (LRM, LAM). They will communicate using specialized languages and appropriate communication protocols.
In the first three parts of this series, we achieved three objectives. We described the characteristics of the AI-first company.…
The process of insight generation is changing due to generative AI. While the foundations of insight generation I presented ten years ago remain relevant, the methods, tools, and implications have expanded dramatically. Generative AI is reshaping how insights are derived, validated, and applied across industries.
The current AI spring is in full swing. Entrepreneurs remain extremely excited about generative AI, as manifested by the number of financing requests our firm and many other investors continue to receive but are starting to think more diligently about where the white space they can go after. Corporations are in testing and evaluation mode as they formulate, or reformulate, AI strategies and assess the impact that generative AI will have on their business. Venture investors remain upbeat about the sector but are also concerned about four issues.
During the last six months, we spent time with our firm’s corporate partners to assess whether the enterprise is ready for generative AI and updated our investment theses accordingly. Our work convinced us that Large Language Models (LLMs)/Foundation Models and applications that incorporate them will open the door to the development of a new class of intelligent enterprise applications.