Field Note: What I Have Seen in Projects Without Executive Support
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.
30 minStrategy
11 articles
Field Notes
Situations genuinely encountered in consulting and training engagements — where theory cracks in practice.
Definition
- Field Note
- A field note is a short first-hand account of something actually encountered in an enterprise AI project — a decision, a failure or a surprise — reported together with its outcome.
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Latest field notes
Field Note: Unexpected Delays During the Integration Phase
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?
30 minLLMOps
Field Note: Where AI Projects That Start Without Data Governance End Up
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.
30 minGovernance
Field Notes: Distilled from the Experience of Deploying Enterprise AI
How does enterprise AI experience actually work in the field? Field notes distilled from deployment realities across sectors, recurring mistakes, and implementation lessons.
30 minStrategy
Field Note: The Realities I Encountered in On-Premise Deployments
On-premise deployment experience: a field note where timeline, hardware procurement, network constraints and update burden differ from the plan. With a readiness checklist.
24 minLLMs
Trainer's Notes: What I Learned Delivering Enterprise AI Training
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.
30 minSkills
Field Note: The Factors That Determine User Adoption
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.
30 minStrategy
Field Note: What I Encountered in AI Approval Processes in Regulated Sectors
Regulated-sector AI approval is a multi-stage process through security, compliance and legal layers. Requested documents, approval duration and ways to speed it up.
9 minGovernance
Field Note: Where Document Preparation Wastes the Most Time
Document preparation experience repeats one lesson: time is lost not in the model but in the documents. A field note on scans, old versions, access and missing metadata.
9 minStrategy
Field Note: Three Common Reasons AI Projects Stay Stuck in the Pilot
AI pilot project failure usually rests on the same three reasons: undefined criteria, thinning support, and integration debt. A 6-criteria scale-up gate.
9 minStrategy
Field Note: The Enterprise AI Transformation Experience — Recurring Patterns
Enterprise AI transformation experience shows the same patterns regardless of sector: data, ownership, pilot-to-scale and measurement. Field observations and early warnings.
9 minStrategy
Complete index
All 11 articles in this cluster
Grouped by format, newest first within each group.
LLMs & Model Technology1
LLMOps & Production1
AI Governance & Compliance2
Enterprise AI Strategy6
- Field Note: What I Have Seen in Projects Without Executive SupportField Note
- Field Notes: Distilled from the Experience of Deploying Enterprise AIField Note
- Field Note: The Factors That Determine User AdoptionField Note
- Field Note: Where Document Preparation Wastes the Most TimeField Note
- Field Note: Three Common Reasons AI Projects Stay Stuck in the PilotField Note
- Field Note: The Enterprise AI Transformation Experience — Recurring PatternsField Note
AI Skills & Training1
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Comparisons & Decision Guides
An answer to “which one should I pick?” with a criteria table and an explicit decision rule.
Concept Explainers
Understanding a term from zero, with examples — from definition to application.
Implementation Guides
A step-by-step, executable path — with prerequisites, outputs and typical pitfalls.
Analysis & Outlook
The data behind a development and what it means operationally — consequences, not headlines.
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Beyond the overview: architectural decisions, production patterns and measured results.
Roundups & Lists
Every option in one area, in one place, screened against the same criteria.
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A subject treated in depth, without a fixed template.