Which aspects of software engineering change (and how) when you incorporate generative AI into your applications?
Sort by:
You have to ask yourself, about the risk / reward when it comes to generative AI. There are many factors to consider, ethics, governance, digital provenance, organizational risk, PII, architectural risk in run due to new dependency relationships. If you make a mistake in engineering, it can be very costly to undo. You have to start with AI principles and risk management with business leaders to understand the overall risk profile and give them an opportunity to evaluate business risk vs technical risk and beyond.
We are running a pilot project using GitHub’s copilot at the moment. We are seeing good time savings for repetitive tasks, writing tests and test scaffolding as well as some common algorithms.
You still need to paid attention to the generated code since sometimes there are mistakes or the code is not production ready. But it certainly free the developer time to be more of a thinker and less of a typer.
It changes the development, operation, trajectory and certainty the capability of both team and application. It is the single most important innovation of our lifetimes. It must be a C-level mandate: every aspect of the company & app/product needs to learn (quickly) to fully embrace and benefit - as ignoring or fighting it is no longer viable.