What I've seen in an AI-forward looking environment is that it's much more common for PM/POs to be knocking up at least a UI prototype now, and experimentation is happening often even before writing the tickets. Similarly when devs are proposing something they often are coming with a couple of prototypes already implemented. Both of those mean decisions are coming a lot quicker.
replyI've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM. A continuing architectural diagram is constructed and graph is updated until all cases are covered and then the LLM writes the code, writes the validations, pushes to CI environments, runs tests, schedules prod deploy (by looking at company event schedule), gets CAB approval, deploys code, tests in prod, and fixes regressions.
replyI'm not saying this is the correct thing, but companies are implementing it and it is "working". I don't think keeping our head in the sand is helping.
> I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM.
replyBut the LLM is not aware of how the business works and why, so someone needs to work with the business to extract the information. Typically it's not well documented.
is it working though? The main outcome we've seen with companies that drink the AI Kool aid en masse is buggy unstable systems. clearly there's a level of rigor that's being missed for ship velocity
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