AI agents are an important component of the transformation to become an AI-first company. Corporations undertaking this transformation must understand where and how to incorporate agents and which agent types to utilize in the AI-centric processes they establish and the organizational structures they adopt. The article presents AI agent types, outlines how corporations should think about “agentification” as they transform to become AI-first, and explains how our firm’s AI methodology fits agents into AI-first business processes.
In today’s corporate digital transformation landscape, “AI-first company” is emerging as a term that deserves real attention, not just another consultant’s buzzword. But can an incumbent truly transform to become AI-first, as Intuit is attempting, or must it be designed that way from inception, like Tesla? In my previous article, I defined the AI-first business process and contrasted it with AI-enhanced processes. This piece builds on that foundation to define what an AI-first company is, describe its defining characteristics, and provide examples of corporations undergoing this transformation.
I recently participated in an on-stage discussion about the state of autonomous mobility at the Ride AI Summit in Los Angeles. During the event, I engaged in several conversations with other participants about the robotaxi customer experience. The essence of these discussions reaffirmed what I’ve long maintained—customer experience, rather than just vehicle technology, will determine the winners in the next phase of mobility.
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.
AI, generative or otherwise, holds immense promise for enterprises looking to improve efficiency, enhance decision-making, and unlock new business opportunities. Yet, despite the enthusiasm, many companies struggle to transition from pilot projects to large-scale AI deployments. The path to effective AI adoption is not as straightforward as acquiring technology or hiring data scientists. Enterprises must navigate challenges from defining the right problems to preparing their data infrastructure, fostering a culture that embraces AI, establishing governance frameworks, and understanding the true costs of scaling AI solutions.




