Has GenAI changed your software developers’ output?

Engineering Machine Learning (ML)

Yes, they produce more work now19%

Yes, they produce less59%

No, their output is the same11%

Don’t know8%

Not using GenAI for software development1%

82 PARTICIPANTS

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The concept of AIOps is still poorly defined.

Strongly agree7%

Agree68%

Neither agree nor disagree15%

Disagree8%

Strongly disagree

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Agree or disagree: The GenAI tool landscape is too fragmented.

Agree — it’s hard to build a well-integrated stack58%

Disagree — we’ve found an all-in-one solution that fits our needs34%

Not sure — we haven’t invested in GenAI tools8%

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What advice would you give to a senior software engineer/developer who is on the fence about transitioning into leadership?

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I am interested in people who have tried generative AI coding projects. In your experience, are there project attributes that make generative AI coding capabilities a "good fit"? Are there project attributes that are likely indicators that generative AI will not result in something useful? I would think complexity of requirements is a big factor. If the complexity of the project or its requirements is low - I assume genAI coding can get you within the ballpark. For instance, logic stores: if your project has critical business logic spread out all over the place and not centralized I think that generative AI would not be able to make sufficient sense of that.

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What are your most important criteria when weighing a new software leadership opportunity? Which aspects of the job do you prioritize over others?

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