Sat in on an
#AI working session today where a developer demonstrated how he uses AI to write code.
My initial takeaway:
#AI is already a powerful accelerator for
#software development but there’s still a lot of work engineers must do to ensure the right code reaches production, especially in product environments where the IP is owned by a single company and not in public domain. At least in the short run.
AI can absolutely generate code. But unless you’re extremely skilled at prompt engineering clearly specifying features, edge cases, conditions, business rules, etc you won’t get 100% accurate code that will pass all the test cases. The tool will get you part of the way there, but it doesn’t remove the responsibility from engineers.
That means the entire codebase still needs proper review before production as you do not want to take a chance. AI may reduce the time required to produce code, but careful validation, testing, and design review remain critical steps in the development process.
In practice, this likely means the development phase becomes faster but not trivial. Engineers still need to spend meaningful time ensuring correctness, security, and alignment with the system architecture.
There’s also the enterprise setup challenge. Organisations will need to build guardrails team or enterprise-wide standards that ensure generated code meets security requirements, compliance rules, and internal coding standards. This won’t happen overnight.
It will be an evolving process that matures over time through company wide initiatives and governance.
Cost is another factor even though it is not a huge factor. The more tasks AI performs, the more usage costs accumulate. While this may still be significantly cheaper than equivalent human labor for certain tasks, it introduces a new operational cost model that teams need to manage. As more and more usage happens companies will explore the best practices in AI usage to ensure the cost is at check.
Where I see immediate and significant upside is maintenance work documentation, internal admin tooling, and routine updates. These are tasks developers often don’t enjoy but are essential for healthy systems. If AI can handle a large portion of this work, developers can spend more time on actual product development, which ultimately keeps teams more productive and motivated.
Bottom line: AI won’t replace engineers anytime soon at least in the companies like above, but it will change how engineering work is done. The real value will come from teams that learn how to combine human expertise with AI acceleration effectively at least until AI is going to replace them entirely ( if that happens) .
#AIAdoption #Technlogy #Innovation