Hypothesis: What Does Success Look Like?
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Hypothesis: What Does Success Look Like?
Most AI projects fail because nobody defines what “working” looks like before they start.
A hypothesis isn’t a wish. It’s a testable statement with a number attached. Before you start using a tool, write down what you expect it to do for you. Be specific.
Examples at the personal/prompt level:
- “This prompt will generate usable first drafts 80% of the time”
- “AI output will need less than 5 minutes of editing per piece”
- “Using AI for client research will cut my prep time from 2 hours to 30 minutes”
- “This automation will save 2 hours per week with less than 30 minutes of initial setup”
Examples at the small business level:
- “AI-assisted proposals will reduce turnaround from 3 days to 1 day”
- “Using AI for bookkeeping categorisation will save my VA 4 hours per week”
- “AI content drafts will maintain my voice well enough that editing takes 15 minutes, not an hour”
The point isn’t precision — it’s commitment. Writing “I think this will save time” is useless. Writing “I think this will cut my research time from 90 minutes to 30 minutes per client” gives you something to actually check against in a month.
How you’ll know it worked:
Speed: How much faster are specific tasks? Where did the freed-up time go — did you reinvest it or just fill it with more busywork?
Quality: Are outputs more accurate? More consistent? Are you catching fewer errors in your review?
Adoption: Are you actually using the tool daily, or did you try it twice and forget? If you have a team, are they using it after the first week?
Business Impact: Can you trace a revenue change, cost saving, or client satisfaction shift back to the AI tool?