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# What Problem Your AI Estate Is Meant to Solve?
- URL: https://www.cindyschwartz.com/what-organisational-problem-your-ai-estate-is-meant-to-solve/
- Published: 2026-08-31T21:47:00.000Z
- Updated: 2026-09-08T02:27:07.000Z
- Description: Licence counts aren't a strategy. Real leadership defines the problem before choosing the tool, not access mistaken for transformation.
- Author: Cindy Schwartz
- Tags: Rewriting Leadership Norms, AI & Transformation, EEG Feature

I sat in on an executive briefing recently where the opening slide was a licence count. Thousands of seats, a handful of vendor logos, a rollout timeline running to the end of the year. Nobody in the room asked what any of it was for, or whether it was the right tool for what they were trying to fix. Access was being treated as the plan.

Many leaders assume that giving people access to a tool is itself the strategy. Choose a platform, issue the licences, measure adoption, call it transformation. The real leadership job, defining the problem precisely and choosing the tool deliberately, gets skipped in favour of the part that is easier to report on.

The scale of that assumption is visible in the data. A Senate estimates question from Senator Jane Hume has produced what iTnews called the first real census of AI subscriptions across federal government. Of the 67 entities that had responded, roughly 29,000 paid Microsoft Copilot licences were in use, including more than 7,000 at DFAT and more than 5,000 at the Department of Health, Disability and Ageing. Google Gemini, GitHub Copilot and Amazon Bedrock also appear, alongside smaller scale Claude trials at the DTA and PBO. That is a great deal of access. It is not, on its own, evidence of a problem being solved.

Access is easy to adopt without changing anything around it. AI products are not interchangeable, they differ in reasoning, writing, coding and how safely they handle sensitive information, and those differences matter for different problems. But once someone starts on Copilot because it sits inside the Microsoft environment they already use, or reaches for ChatGPT because that's what they use at home, that becomes the default for everything, regardless of whether it is the strongest tool for the task in front of them. Familiarity is doing the deciding, not fit, and licence counts cannot tell leaders whether that is happening.

We can count licences, measure active users, and calculate hours people say they have saved. None of that tells us whether the tool matches the problem, or whether anything important has changed. If 5,000 people have access to an AI assistant but decisions still get stuck in the same places and employees still do work that adds little value, leadership has deployed technology rather than transformed anything.

Rewriting this norm means starting somewhere different. What work are we actually trying to change, and where is capacity being consumed that could be used differently? Which decisions take too long, and why does that keep happening? Which problems are being handed to whichever tool people already have open, rather than the one built for the job? Sometimes the constraint isn't technological at all. A slow process may be slow because five people have to approve it. A decision may take three weeks because nobody is sure who has the authority to make it. AI can make each of those things faster without making them better, and in some cases leaders risk using extraordinary technology, chosen out of habit rather than fit, to accelerate organisational habits that should have been questioned years ago.

Access wasn't the aim. The aim for leaders is to define the problem precisely enough to choose the right tool, then make sure the enterprise has the operating capability to support it.

*— Cindy Schwartz*

*Founder, Executive Excellence Group | Rewriting Leadership Norms*