That is the common belief I see on the internet. I do not agree with that and have a process to prove otherwise.
Currently, open-source models that can run on affordable hardware are not as capable as commercial AI models running in huge data centers. Everyone knows it, and I do not argue with that.
My point is that it is perfectly possible to use open-source models running on local hardware to generate enterprise-class solutions that directly impact business operations and provide tangible business results. I know that for a fact, as I work this way.
I often hear people say they do not trust open-source AI to deliver enterprise-quality outcomes. Let me reframe the question and ask if you trust a junior/mid developer to design a mission-critical application?
Probably you don’t, and you shouldn’t have.
What if there is an experienced senior architect who designs the solution and guides the junior/mid developer during the implementation? To be honest, that is how the major consulting firms operated for years and delivered (more or less) enterprise-grade solutions.
That is the way I work with my local AI development team:
- I prepare the technical design and describe important edge cases that must be handled.
- I let my local AI team work to deliver the task.
- I run a separate AI team to test the generated results.
- Part of verification is the execution of static code analysis tools to check the code quality and security vulnerabilities.
- Any detected issues are reported back to the AI development team to analyze and fix.
Once the code passes all checkpoints, I review it manually (the same way I used to review code provided by my colleagues before deployment).
Same process, different team, same quality of outcomes.
AI, neither closed nor open source, is not yet ready to deliver enterprise-level, production-ready solutions. Guide it accordingly, and it will work for you like a junior/mid-level development department.
👉 How do you keep your AI development team in check?