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Don't blindly worship AI's omnipotence! A practical case study on using the 'AI generation + traditional software' combo to boost enterprise efficiency by 40%

When implementing AI efficiency improvements for a B2B company with over 200 employees, we initially believed in 'AI omnipotence' and wanted to use AI for everything. The result was misassigned task flows, chaotic permission controls, and incorrect data statistics. Later, we re-divided responsibilities between AI and traditional software: AI handles...

Summary

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When implementing AI efficiency improvements for a B2B company with over 200 employees, we initially believed in 'AI omnipotence' and wanted to use AI for everything. The result was misassigned task flows, chaotic permission controls, and incorrect data statistics. Later, we re-divided responsibilities between AI and traditional software: AI handles content generation and knowledge reuse, while traditional software manages processes, permissions, and statistics. After adjustments, initial draft bid documents were compressed from 2 days to 2 hours, proposal PPT efficiency increased by over 50%, and pre-sales staff capacity per person handling projects rose by 40%. This case study thoroughly explains how to execute the 'AI generation + traditional software' combination strategy, targeting clients with medium-to-large enterprise resources...

Our Take

Three things to consider about this case

Why it matters
First, agree on a specific requirement and acceptance criteria, and record the delivery and maintenance effort involved.
Who it suits
Suitable for reference by those who already have similar needs and can verify effectiveness through small tasks.
What to check first
The achievements mentioned in the summary belong to the author's self-report; distinguish between revenue and profit, and consider costs, man-hours, and reproduction conditions.
Our Suggestions / Try This
Who it suits
Small teams providing services
What to try first
First confirm specific needs with a potential user and validate delivery with a small pilot.
What to measure
Record user adoption, delivery time, and subsequent willingness to pay.
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What should you ask before trying this?

  • What specific need does this practice solve for you?
  • How should the minimum viable test be accepted, and how much time needs to be invested?
  • Which steps still require human processing and continuous maintenance?
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