The 3 Options for AI Adoption
When a company puts AI into work, the options are broadly 3: SaaS (use a ready service) / in-house (own development) / SI (outsource development). Discern the first branch here.
Each One's Character
- SaaS: usable immediately, low initial cost, operation handled. But limits on customization and proprietary-data integration
- In-house: optimized to requirements, know-how stays internal. But needs talent and continued investment
- SI outsourcing: can build requirement-specific even without dev capability. But high cost and vendor lock-in risk
Judgment Frame
| Aspect | Suits SaaS | Suits in-house/SI |
|---|---|---|
| Work uniqueness | Generic | Company-specific |
| Data confidentiality | Mid–low | High (can't go outside) |
| Internal engineers | Few | Have/want to grow |
| Launch speed | Top priority | Can wait somewhat |
| Long-term differentiation | Unneeded | Want as competitiveness |
The Realistic Answer Is "Combination"
Most companies settle on combining SaaS for quick results, in-house/SI only for parts to differentiate. Trying to build everything in-house from the start and failing is the typical failure.
First Step
Start by sorting per task "is SaaS enough for this work, or is it highly company-specific?" The next chapter covers vendor comparison, then the in-house break-even.
