I want to start the series with the most abused term in the AI discussion: value. Many enterprises still talk about AI value as though it comes from replacing people or automating ...
In this issue, I want to introduce the framework I will use over the next two months to discuss how CEOs can plan, manage, and adjust AI strategy. AI strategy is now sitting at the...
I want to address what we know today about AI (GenAI, more specifically) since the launch of the first commercial GPT nearly 40 months ago. How can we leverage the full strategic ...
The market still talks about AI talent as if it were a shopping list of talent that we are seeking. Alas, enterprises should not be looking for a clean inventory of discrete AI ski...
Today, we are going to begin to cover the issue of talent acquisition for AI needs. There is no checklist of skills or talents to use when hiring, nor are there effective tests to ...
In this issue, we examine a structural shift in enterprise software emerging across pricing, process design, and workforce assumptions. The focus is not on AI as a generic disrupti...
In this issue, we explore why AI delivers value unevenly across the enterprise, what is actually working today, and how organizations should approach extracting value over the long...
In this issue, we are going to address a slew of articles and research released in the last few weeks that examine who is making decisions about AI adoption in the enterprise....
In this issue, we are going to start – so much to cover – addressing context as the next AI evolutionary hurdle. And no better way to do that than by sharing a few links and ideas ...
We are going to avoid distracting you from the game, discuss the latest “agents are coming to take over our work and our lives” announcements from recent days, but dig a little dee...
What happens after GenAI. LLM and GenAI took over in 2025, even “evolving” into Agentic AI. The problem? LLM has limited abilities, like any other single-solution model, and quickl...
