Before introducing AI,
you must first decide something.
Is this problem
one that AI should solve?
STAR-T is not an organization that "implements" AI for you.
We distinguish
what AI should do
from what humans must be responsible for.
Most AI adoption failures
are not technical problems.
Decisions that should not be automated
Judgment with unclear responsibility
Data exists but criteria do not
Designs that remove necessary human involvement
AI used wrongly becomes risk, not just inefficiency.
STAR-T
judges whether AI should be used.
Distinguish
✓ What AI may do
✕ What AI must not do
Boundary
✓ Areas that can be automated
✕ Areas humans must be responsible for
Condition
We implement with AI only when responsibility and risk are clear.
We judge by these criteria
Reversibility of decisions
Can it be undone?
Accountability
Who bears legal and organizational responsibility for outcomes?
Basis for judgment
Data vs. context
Impact if automation fails
What happens when it goes wrong?
That judgment
is delivered as services.
AI adoption feasibility assessment
AI–human role separation design
Implementation after risk clarification
Services where judgment is reflected in code
We have been validated
where judgment was needed.
300+ "whether to use AI" assessments
Lectures on AI adoption criteria350+
Average satisfaction4.7/5
Judgment experience across 20+ domains
The STAR-T team
are people more familiar with judgment than with AI.
People who structure complex decisions
Accustomed to defining scope and criteria together.
People who handle responsibility and risk
Focused on who is accountable for what.
People who know the cost of automation failure
We look at impact of wrong adoption first.
Is this problem
one where AI should be used?
AI is a tool.
Used without judgment, it becomes risk.
STAR-T
starts with that judgment, together.
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