Introduce AI where it actually improves the work.
AI creates value when it is connected to a process, an owner, and a measure. Fleming Wilde helps teams separate impressive demos from uses that reduce delay, improve quality, or accelerate decisions.
When this mandate fits
The team is experimenting with AI without a shared standard.
Writing, triage, research, or support tasks are taking too much time.
Leadership wants measurable gains before broader adoption.
Privacy, hallucination, and validation risks need boundaries.
What the mandate should produce
A prioritized list of AI use cases by value, risk, and feasibility.
Usage protocols for sensitive data, validation, and escalation.
Assistants, templates, or AI workflows integrated into existing tools.
A simple measure: time saved, quality, delay reduction, or error reduction.
Operational before / after
Before: personal prompts, uneven output, and no shared validation.
After: common work patterns, verification criteria, and allowed uses.
Before: AI produces more text without improving the workflow.
After: AI prepares, classifies, or summarizes; humans decide and remain accountable.
Common operating contexts
Proof we look for
Filtered use cases
AI uses are selected only when operational value is clear and risk is manageable.
Human validation
Sensitive decisions keep a human owner and a verification trail.
Team routines
Uses are integrated into existing routines instead of relying on one isolated champion.
Common questions
Where should an SMB start with AI?
The best start is a repeated process where quality can be checked: triage, synthesis, response preparation, internal research, or simple compliance review.
Can AI handle confidential data?
It depends on the data, vendors, and organization settings. The mandate includes boundaries for allowed uses, excluded data, and required validation.
Prioritize AI use cases
We can start from your real workflows and identify the two or three AI uses that deserve a controlled pilot.