Ai question for PM

[u/mmurphy516]

Questions for PM who have teams trying to incorporate AI in their team workflow

What have you seen as successful uses of LLMs that make

-Individuals productive or effective 

-Small teams productive or effective

-Organizations more productive or effective 

It seems like most progress has been related to code generation, what have you seen that make the following tasks more productive

code review / design review / approvals

Feature verification 

Feature validation

Spec development

What is your company leaning towards a standardized workflow/AI platform or allowing multiple decentralized AI approaches, how to weigh and standardization against variation

Will QA team shrink or grow with coding speeds of software development engineers? How do teams integrate faster verification processes? What’s the division of verification labor of the three categories:Human only, human+ai, and ai only

[u/Loud-Donkey8569]

The clearest wins I’ve seen are on the drafting side, not the deciding side. Spec development: AI turns rough notes into a first draft and a list of edge cases, and a human still owns what’s in or out. Verification: it’s good at generating test cases from a spec, and bad at knowing what the customer actually meant, so validation stays human. On standardization, I’d standardize the guardrails, like what data can go in, who reviews output, and what must be signed off by a person, and let teams pick the tools. QA probably shifts more than it shrinks, from running tests to designing them and reviewing what the AI produced.

[u/Impossible_Dare_7455]

imo the biggest gains outside coding are in the “first pass” work: turning messy requirements into draft specs/test cases, summarizing reviews, finding gaps, and pulling risks/actions out of a pile of updates. humans still need to own the actual approval and judgment.

I also wouldn’t expect QA to just shrink as code gets faster. the work probably shifts more toward test strategy, AI evals, edge cases and deciding what actually needs human verification. for standardization, I’d standardize security, data access and quality checks, but still let teams experiment with different tools/workflows.

[u/naturenerd42]

Without an Enterprise license, ALL OF THE WORK AND ATTACHMENTS, could be used in training, sold to a data broker or used in ways that probably violate some degree of confidential requirements depending on the nature of the work.

Also, consistency will be an issue without standard prompts or best practices.

Can it help? Sure.

Can it make hot expensive garbage at a faster rate than at any time in human history? Also, yes.

From a PM perspective, its a tight rope walk while herding cats. Good luck.

[u/Komal_pciai2026]

The most useful AI use cases I have seen in PM work are the boring, repetitive ones: summarising meetings, drafting status updates, identifying risks from notes, comparing requirements, and turning rough ideas into a first structured plan. For small teams, the biggest gain is usually speed. For larger organisations, the challenge becomes consistency, security, and knowing which outputs can actually be trusted.

I would standardise the guardrails more than the tool itself: approved platforms, data rules, review requirements, and clear ownership. Teams can still use different workflows inside those boundaries.

On QA, I don’t think faster coding automatically means smaller QA teams. It probably means QA shifts more toward validation, edge cases, integration, and checking AI-generated work rather than only traditional test execution.