AI initiatives become more useful when organizations test the business problem, the data foundation, and the operating model before scaling.
01. What decision or workflow will improve?
Describe the user, the task, the current friction, and the value of doing it better.
02. Is the underlying data ready?
Examine quality, permissions, representativeness, access, and governance. A model cannot fix unreliable source information on its own.
03. How will you measure value?
Choose a meaningful baseline. Consider time saved, accuracy, user experience, operational risk, or another business-relevant measure.
04. Who will oversee the results?
Clarify where human judgment, review, escalation, and correction are required.
05. What will production ownership look like?
Plan for evaluation, security, privacy, monitoring, maintenance, and changes in model behavior before a proof of concept becomes a business-critical process.
Key takeaway: Start small, test assumptions, and scale AI only when the results are useful, reliable, and governable.
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