Although software developers still write much of the code and integrate systems, their role is expanding to include AI oversight. Today’s developers might spend as much time reviewing AI-generated code as writing original code. They act as the last line of defense, ensuring that “almost right” code is made fully right before it hits production. As I’ve written before, developers now serve as supervisors, mentors, and validators for AI. In enterprise settings especially, developers are the custodians of quality and reliability, approving or rejecting AI contributions to protect the integrity of the product. Though prompt engineering made a valiant attempt to distinguish itself as a separate discipline, the reality is that many developers and data scientists are learning these skills. The Stack Overflow survey noted that 36% of respondents learned to code specifically for AI in the last year, showing how important AI-centric skills have become across the board.
The good and bad news is that this issue doesn’t merely plague developers because developers aren’t the only people who build code anymore. Here are a few other roles that may involve code:
- Data scientists and machine learning engineers who work with the models and data that animate the code have a crucial role in building trust. A well-trained model is less likely to hallucinate or produce nonsensical outputs. These experts must ensure that models are trained on high-quality representative data and that they’re evaluated rigorously. They also implement guardrails, for example, ensuring an AI that suggests code doesn’t produce insecure patterns or known vulnerable functions.
- Product managers and UX designers keep the big picture of any software project in mind. They decide where to apply AI and where not to, all while shaping how users interact with AI features and how much trust they invest in them. A savvy product manager will ask: “Is this AI feature truly ready for our customers? Do we need a human in the loop for quality control? How do we set user expectations?” They can also prioritize features like auditability and explainability in AI. UX designers may bolster this by using visual cues to indicate uncertainty about AI results. Great PMs and UX designers can “humanize” AI in ways that build trust by making AI a copilot, not an infallible oracle.
- Quality assurance, security, operations teams, etc., are also essential roles in AI application development.
With so many players involved, where does this leave the classic software developer? In many ways, developers have become the orchestrators of AI-driven software projects. They stand at the intersection of all the roles mentioned. They translate the requirements of product managers into code, implement the models and guidance from data scientists, integrate the prompt tweaks from prompt engineers, and collaborate with designers on user-facing behavior. Critically, developers provide the holistic view of the system that AI lacks. A large language model might be able to spit out code in Python or Java on demand, but it doesn’t understand your system’s architecture, your specific business logic, or the quirks of your legacy stack. A developer does, and that context is everything, as I’ve highlighted.
