While it's true that using AI in development can help engineers write code faster, that’s not the full picture. What we saw in practice was broader: AI reduced total delivery time. We completed work that would normally take three to four weeks in just one to two weeks because several stages moved faster — solution design, implementation, refinement, validation, and documentation.
This was especially noticeable in tasks like schema handling, mapping logic, JSON-based operations, and data transformations. With these kinds of problems, the bottleneck isn’t typing speed; it’s structuring the solution correctly and getting to a reliable result without multiple rounds of rework.
For us, one of the most useful applications of AI in engineering is troubleshooting.
AI helped identify the root causes of Databricks and Braze issues, compare schemas side by side, and trace execution flows. It also pinpointed problems that may not have been obvious at first glance and would normally take a long manual investigation, such as incorrect merge behavior, schema mismatches, and platform constraints.
Investigating these types of problems is expensive because it consumes time without visible progress. AI reduced that cost.
The best use of AI is not replacing thinking but supporting it. This is an important distinction. Instead of treating AI like an autocomplete tool, we use it to move faster from ideation to implementation.
Sometimes the hard part of engineering is not writing code but rather figuring out how to translate an idea into a practical starting point. AI helped most in those moments: proposing possible approaches, adapting examples to a real use case, and raising relevant questions. This was especially useful when working with unfamiliar patterns or technologies. Not only did AI accelerate execution, it also promoted understanding.
While documentation is important, it’s often treated as a secondary concern. Teams know it matters, but it usually loses priority against delivery pressure. AI changed that dynamic in a practical way.
READMEs, mapping documents, pipeline explanations, and implementation notes became much faster to produce and became a part of the work itself. This does not remove the need for review, but it dramatically reduces the effort required to get to a solid first version of a project.
That’s often the difference between documentation existing and not existing. It’s expensive and time-consuming to produce documentation consistently, but AI lead to meaningful operational improvements.
An underrated benefit of AI is how much time it saves in the work around implementation.
Researching platform documentation, validating assumptions, generating test data, checking edge cases, understanding flows, comparing structures — all the time required for each of these tasks adds up.
AI reduced that time significantly. Instead of manually navigating long documentation, relevant information could be accessed directly in context. AI could build large testing databases in minutes rather than hours. Not only were engineers faster, but they could also stay focused on more tasks, which was one of the most practical benefits of all.
The main benefit from working with AI was reducing friction across the engineering lifecycle. It’s easy to underestimate these issues until you experience them repeatedly. We spent less time researching, debugging blindly, documenting from scratch, and turning concepts into working solutions. AI is not magic or a replacement for engineers. It’s just more leverage and speed where engineering teams need it most.
Not magic. Not replacement. Just more leverage and speed where engineering teams usually need it most.
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