The CEO AI Dashboard Series: All the Posts

During the months of June and July of 2026 I wrote a series of posts describing the items and metrics a CEO had to keep in mind as they created, managed, monitored, or updated their AI strategy for their enterprise.

The genesis for this model was the quarterly updates I create for The Board, which is basically a summary of all that matters in enterprise technology. If you have not yet looked into it, it is a fantastic resource with great feedback (someone told me is the best way to keep management consultants and vendors under control - I'll take that). These updates stem from a combination of published research, tons of reading, and more conversations with executives and board members that should be possible (what can I say? I am amazing.... and Charles helps a lot).

This series looked at the six areas where a CEO should focus their AI strategy, and they are:

Part 1: Introduction to the series.

Quotable: "My goal is not to create a maturity model. I want a working instrument a CEO can use to identify where the enterprise is aligned, where it is improvising, and where the appearance of progress is covering weak operational foundations."

Part 2: Business Value of AI

Quotable: "AI creates limited value when it speeds up a task. It creates larger business value when it changes the economics of the process."

Part 3: Data Readiness

Quotable: "Data readiness is not only about quality or availability, but also about understanding what the data means, what it is called across the enterprise, where it originated, what context it requires, and how it can be used safely."

Part 4: Infrastructure Readiness

Quotable: "The enterprise needs a mix of brains and brawn. Brains means CPUs, logic, orchestration, workflow control, and the systems that decide how work should move. Brawn means GPUs, dense compute, memory, networking, storage, and the capacity to process large volumes of data and inference requests at speed."

Part 5: Governance

Quotable: "Traditional governance models were built around systems that people operated. AI systems increasingly operate with people supervising from further away, or eventually not at all. AI governance stops being theoretical the moment an agent can act, commit, deny, approve, or escalate on behalf of the enterprise without waiting for a human to intervene."

Part 6: Economics of AI

Quotable: "Over time, raw model access is likely to look more like commodity infrastructure than differentiated value – the public models will all look and work the same in the long run. The higher-margin will move upward, into context and semantics, workflow design, proprietary and privileged data, and the operating systems that turn AI into enterprise outcomes. In other words, the value that enterprises can add to those models, not the models themselves."

Part 7: Talent

Quotable: "The enterprise is allowing its hardest-to-replace operating asset to leave while recruiting for skills that are becoming easier to buy."

There you have it, what do you think? This is the second step on this framework, later in the fall I will create a longer, all inclusive report that will give you more in-depth on how CEO's I worked / am working with are using these concepts, including metrics and real-life results.

Comments? you know where to put them.... (down below, of course)