Not every problem needs AI. This filters out bad candidates early.
Step 1: Start With the Task, Not the Tool
For each task, ask:
- What is the goal of this task?
- What happens if it goes wrong?
If failure is unacceptable, be cautious.
Step 2: Identify Strong AI Candidates
Tasks are more likely eligible if they are:
- Repetitive
- Text, voice, or data-heavy
- Slowed by searching for information
- Based on patterns or rules
- Already partly automated
Simple example: Summarizing support tickets before escalation.
Complex example: Reviewing large volumes of documents to flag risks or inconsistencies.
Step 3: Identify Hard "No" Tasks
Exclude tasks that:
- Require legal or regulatory judgment
- Involve sensitive personal data without safeguards
- Happen rarely or inconsistently
- Depend on deep human context or intuition
- Have high cost if wrong
These tasks stay human-led.
Step 4: Check Data Availability
Ask:
- Is the needed data accessible?
- Is it mostly digital?
- Is it reasonably clean?
If data doesn't exist or is scattered, flag the task as not ready.
Step 5: Assess Trust and Adoption Risk
Consider:
- Will people trust the output?
- Will they double-check everything anyway?
- Would mistakes damage credibility?
Low trust means low value.
Step 6: Label the Task
Assign one label:
- AI-Eligible
- Not AI-Eligible
- AI-Eligible Later (needs data or process fixes first)
What You Should Have Now
✅ AI Eligibility List
✅ Tasks clearly labeled
✅ Notes explaining exclusions or delays
Quality Check
- Excluded tasks have clear reasons
- No task is labeled "AI" just because it sounds impressive
- Data readiness is considered
- Risk is weighed more than novelty
Next Step: With eligible tasks identified, you're ready to find quick wins.
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