5 posts
What happens to enterprise AI projects without sponsor support? A field note on budget erosion, slowing decisions, resistance, and how to keep a project alive.
An integration-delay experience from the field. Why do legacy system APIs, data access permissions, missing test environments and data-mapping exceptions push project timelines?
A lack of data governance quietly drives AI projects into a dead end: ownership gaps, data quality problems, source ambiguity. A field observation with symptoms and minimum safeguards.
A trainer's field note on enterprise AI training experience: what participants actually ask, how to balance theory and practice, why training is forgotten, and how to refresh it.
User adoption determines an AI tool's fate more than the model does. The resistance reasons, usage decline and adoption factors that actually work, from the field.