How AI Is Used in the Editorial Process

The work of Imagine That Performance starts with facing what is true, including what is hard to see from inside. The writing on this site is held to the same practice. This page shows where AI tools help with it, how they are checked, and who decides.

Who writes the articles

Every article on this site was written by Rob Duncan, by a colleague at Imagine That Performance, or by a guest author credited on the piece. They draw on Think Tank sessions, practitioner conversations and leadership research. Some were first published in 2021.

How the review works

The articles here ask leaders to face what is true, to seek signals from beyond their own view, and to check for blind spots as conditions change. Rob applies the same rhythm to his own writing. An article that was accurate when it was written can drift as the world shifts, and its author is often the last to see it.

In early 2026 Rob began a review of the published articles, with AI as an editorial partner. The review asks the same questions of every piece. Does it describe conditions a leader can act on? Are its research citations accurate and current? Does it end with something a practitioner can use?

AI reads each article against those questions and drafts proposed changes. It flags statistics for verification and runs searches to confirm or correct them. When one article turns out to carry a problem, such as a wrong date or an out-of-date figure, AI tools search the other articles for the same problem. One finding then corrects the whole body of work, not only the page where it surfaced.

AI is one check among several. Before the current home page went live in September 2026, Rob asked a reader outside the work to read it cold, and the page changed on that reader’s notes. That same month, reading a newsletter before it went out, Rob saw that The Isolated Leader still gave a session count from 2023. The article now carries the current count and a dated note.

Each check covers what the others miss. AI holds every article to one standard and finds repeats no single reader would catch. A reader outside the work sees what the author is too close to see. Rob decides what matters and whether a change improves the article. Every change is made first on a private copy of the site and read there before it reaches the public site.

Rob reviews every proposal. No change is published without his direct approval. AI proposes. Rob decides.

The review is a practice, not a project with an end. Conditions keep shifting, so the checking keeps going, and every new article is written to the same standard from the start.

What each article shows

Each article carries the date it was first published and, where it has been edited since, the date it was last edited. Where a review brought new evidence to a piece, a short dated note at the end names that evidence and what the piece now gives the reader. Other edits, such as a new title, spelling, links and wording, carry the date alone.

What AI does well here

AI is good at consistency across a body of writing. It can hold many articles to one standard of voice, and check that a statistic cited two years ago still matches its source.

It is also good at drafting a revision that keeps an article’s ideas while bringing its voice in line with the rest of the site.

What AI does not do

AI does not decide what matters. It does not judge whether an article’s core argument is sound. It does not know whether a practitioner’s quote still represents that person’s view, or whether a Think Tank observation still rings true for current members. It does not make the call on whether a revision improves the article or only changes it.

Those decisions take practitioner knowledge and relationships that AI cannot replicate. Rob makes those calls.

Why this page exists

Many of the leaders Imagine That Performance works with are facing their own questions about AI: what it can do, what it should not do, and how to use it without losing the human judgment their roles demand. Much of that question is about trust, transparency, and whether people understand how AI is actually being used.

Rob is working through the same questions in his own work, as a practitioner rather than an expert with the final answer. Showing the process openly is the same transparency that builds trust inside an organization. Not “we use AI” as a claim, but here is specifically what it does and does not do, how it is checked, and who makes the final decision. The answer on this page will keep changing as the tools and the practice do.