Which aspects of software engineering change (and how) when you incorporate generative AI into your applications?

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CTO in Transportation, 11 - 50 employees
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.
Highly Qualified Expert in Healthcare and Biotech, 10,001+ employees
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. 
President in Software, 51 - 200 employees
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.

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CTO in Software, 201 - 500 employees
Without a doubt - Technical Debt! It's a ball and chain that creates an ever increasing drag on any organization, stifles innovation, and prevents transformation.
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