Thinking on technology, data, and organizational change.
Substantive perspectives on the decisions executives face when transforming how their organizations use technology and data. Not trend pieces. Practical analysis.
Why Most AI Roadmaps Fail Before They Start
Organizations are investing heavily in artificial intelligence, but a significant majority of AI initiatives fail to deliver meaningful business value. The root cause is almost never the technology. It is the absence of the strategic clarity and data infrastructure that would allow the technology to work. Before you can build a useful AI roadmap, you need an honest answer to a harder question: what decisions are we trying to make better, and do we have the data to make them?
This piece walks through the four questions every executive team should be able to answer before committing to an AI investment, and what the honest answers typically reveal about where to start.
Read the full piece →Most governance frameworks fail because they are designed for compliance, not for use. A practical look at what separates frameworks that stick from ones that collect dust.
System go-lives are not finish lines. An honest look at why adoption metrics matter as much as technical metrics, and how to design for both from the beginning.
Legacy systems feel safe because they work. But the compounding cost of technical debt and missed opportunity is real, and this piece makes the case in terms executives can use.
Higher education institutions have decades of data and very little of it in usable condition. A case-based look at what a realistic transformation path looks like for a mid-size university.
Responsible AI frameworks designed for technology giants do not translate cleanly to the context of a 5,000-person bank or a regional health system. Here is what a practical framework looks like instead.
Organizations that are drowning in dashboards often have a decision problem, not a data problem. A framework for distinguishing the two and addressing both.