AI is changing how software engineers build and deliver solutions at an incredible rate. With more than 84% of developers using AI, according to Techreviewer’s research, it’s obvious that it’s now commonplace. From helping developers write code to debugging, it streamlines the entire development process.

Although the use of AI for workflow automation improves productivity, it’s not without its issues. Jens Wessling, Chief Technology Officer at Veracode, explains that, “Despite the advances in AI-assisted development, it is clear security hasn’t kept pace.” Errors, security risks, and legal and ethical concerns are some drawbacks of its adoption.

To address these challenges, organizations must develop strategies to identify and prevent risks. Transitioning from the issues to practical solutions, the following sections examine the importance of human oversight in mitigating such problems while maximizing the efficiency of AI.

The Reality of AI Code Errors: What Developers Are Actually Experiencing

LLMs are trained on a wide amount of data to generate results for developers quickly. However, these models do not understand the context or the unique requirements of every project. They make code completions based on past examples or even hallucinate results.

Stack Overflow’s Developer Survey found that 66% of developers identify nearly-correct solutions as their primary frustration. Developers often encounter different mistakes with AI-generated code, including:

• Logic mistakes: The system can produce correct code that doesn’t apply to the intended business logic. This occurs when it fails to consider the project’s context or specific needs. Its output might look right, but it doesn’t actually solve the obvious problem or account for edge cases.
• Insecure or vulnerable code: Techreviewer’s data reveal that 46.7% of developers experience security risks with AI-generated code. Studies also show that while these models are powerful for automating workflows, they often overlook security best practices.
• Outdated or incompatible libraries: Models trained using outdated libraries may suggest solutions that do not adhere to current best practices. For developers, this training gap results in broken code and wasted hours spent implementing outdated approaches.

About 20% of respondents in Techreviewer’s survey have noticed outdated practices. Conversely, 33.3% experience incorrect API usage, including outdated SDK calls.

• Incorrect assumptions: Another risk with adopting an AI development workflow is incorrect assumptions, particularly when developers enter ambiguous prompts. Without explicit details, the model may generate solutions based on statistical patterns rather than the developer’s intent.

Software developers working with AI workflows verify output before implementation. Only about 18% trust AI-generated code, treating it as a draft rather than the final product.

Why Human Oversight Remains Essential

Developers treat AI outputs as drafts and review them before use, highlighting the need for human oversight.

Teams integrating these models have built-in verification steps in their workflows to validate the generated code. 62% of the respondents from the Techreviewer research say they always verify AI-generated solutions. Up to 26% of them say they validate outputs often.

The method of verification differs for each company, but generally includes manual code reviews by engineers, automated testing suites, and static code analysis tools. 77.8% of respondents apply a human-first approach before using the given output in production. Teams may also layer in additional reviews or automated checks to increase accuracy.

• Automated tests: Teams typically use established automated testing frameworks to run both unit and integration tests on the generated code. According to 48.9% of respondents, this method ensures that code behaves as expected and correctly handles edge cases.
• Static analysis: Approximately 17.5% of teams utilize static analysis tools – software that examines code for potential errors, vulnerabilities, or deviations from coding standards – after running automated tests. This adds an extra layer of quality control before code deployment.
• Peer reviews: A less common method, with only 18.8% of respondents participating, involves having fellow developers manually review the generated code for logical errors, adherence to standards, and security concerns. This emphasizes independent validation within teams.

There are other methods, such as checking generated code against documentation, runtime monitoring, and cross-referencing with multiple AI tools. The presence of human oversight, regardless of the approach, ensures that leveraging AI doesn’t introduce potential issues.

Ethical Dilemmas: IP, Data Privacy, Bias, and Disclosure

The integration of generative AI in the development workflow also raises serious ethical concerns. About 80% of respondents in the research encounter ethical dilemmas occasionally. Privacy risks and intellectual property concerns are the top concerns, with both at 45.5%.

These models are trained on publicly available code, some of which may have copyright licenses. Intellectual concerns arise when developers are unsure about who owns the code generated by the system. Also, are they infringing copyrights by copying this code?

There are also concerns about data privacy when exposing company code to external artificial intelligence tools. When developers feed these systems sensitive information, there’s a possibility of leakage.

Stanford mentioned in a recent article how some companies use user feedback to train their AI models and improve their capabilities. This is a cause for concern for development teams, as they must think twice when sharing information with their AI agent.

Another ethical dilemma arises from the potential bias that can occur when AI is trained on biased or non-inclusive datasets. This is a significant issue for approximately 43.2% of respondents.

Transparency and disclosure are primary concerns for developers as well. About 40% disclose the use of LLMs occasionally, 37.8% disclose rarely, while 20% do not reveal it at all.

Additionally, whether teams openly disclose the use of AI depends on factors such as contract terms and the type of project. All these dilemmas listed above create real legal and organizational risks if left unaddressed.

Balancing Efficiency With Responsibility: Best Practices for Safe AI Adoption

The solution to code errors and ethical dilemmas is to adopt the use of LLMs responsibly. Proven steps and best practices for development teams include:

• Always verify generated code: As Techreviewer shows, teams should use manual reviews and automated testing to verify their work, treating AI-generated code as a starting point rather than the final product.
• Prioritize data privacy: Teams should always limit the amount of sensitive details they share and anonymize them when possible. This includes everything from proprietary code to credentials and user data.
• Establish internal guidelines: Companies will benefit from establishing clear procedures on how members can adopt these tools. Having guardrails will help keep the team in check and reduce potential risks.
• Adopt AI in high-ROI areas: Avoid using these tools for complex logic and architectural decisions that require human judgment and discretion. Instead, teams can focus on areas where they add measurable return, such as debugging, code generation, and documentation.
• Developer Upskilling: Companies must encourage continuous developer upskilling to maintain critical thinking and problem-solving skills. Moreover, without proper training, staff may unintentionally overshare data or use unvetted outputs.

Conclusion

AI workflows are now an important part of development processes for organizations. They offer speed and increased efficiency, which help teams improve their productivity. At the same time, AI introduces new technical errors and ethical challenges.

To use these models responsibly, human supervision, continuous testing, and clear ethical guidelines are vital. That way, teams can leverage their strengths while utilizing human judgment at every step for quality control.

Read this Techreviewer survey, which covers this topic even more extensively: https://techreviewer.co/blog/how-ai-reshaping-development-workflows-in-2025. It reveals deeper insights into how dev teams use AI, the issues they face, and how ethics is redefining AI for programmers.