Find the original article in Forbes Technology Council here.
While AI assistants such as Copilot have streamlined software development workflows, heavy reliance on them comes with potential risks. Viewing AI assistants as full-fledged partners rather than as tools with both benefits and limitations risks unexpected complications.
Smart use of AI assistants can speed delivery, boost productivity and even strengthen devs’ abilities, but overreliance on them can lead to issues ranging from ineffective code to skills atrophy. Below, members of Forbes Technology Council share some risks that can come with leaning heavily on AI-powered development and how to mitigate them—read on to ensure AI remains a tool, not a crutch, for your team.
1. False Confidence
One key risk is the lack of a “trust but verify” mentality. This risk is exacerbated with less-experienced dev teams, who may feel like they are progressing really quickly in the beginning, but then end up hitting a wall. They may take longer to recover than they would have if they had approached the problem using first principles and leveraged an AI assistant as a tool to support their thinking. - Krishnan Narayan, Palo Alto Networks
2. Security Risks
Relying on AI assistants like Copilot poses security risks, such as injecting vulnerabilities, hardcoded secrets and insecure dependencies. AI may also misconfigure authentication or grant excessive permissions. To mitigate this, we need to enforce security reviews using non-AI-generated scanners and follow least-privilege principles, ensuring AI-generated code meets security best practices. - Pavan Emani, Truist Bank
3. Code Quality Issues
AI coding assistants like Copilot can introduce code quality and security risks, especially in existing larger codebases. Generated code may lack context, leading to nonstandard or vulnerable code. While AI continues to rapidly improve, careful review of AI-generated code is important for now. Don’t blindly accept it. - Manas Talukdar, Labelbox
4. Similar Or Generic Solutions Across Projects
AI tools like Copilot are trained on publicly available code, which can lead to the generation of similar or generic solutions across projects. This reduces diversity in problem-solving approaches and stifles innovation. Teams may end up with codebases that lack unique optimizations or creative solutions tailored to their specific needs. - Kaushik Tiwari, Attack Capital
5. Sabotage Of AI Adoption
The real risk is development teams discovering that most of their repetitive work can be automated, leading to direct or indirect sabotage of AI adoption across the organization. Clear guidance on how to approach an AI-first development mindset—including a reframing of key responsibilities of the development team and the shortcomings of AI tools—is one way to minimize that impact. - Lucas Persona, CI&T
6. Complacency
There is a risk of driving complacent behavior among knowledge workers. Team members don’t intentionally become complacent, but as they trust AI more and more, they tend not to critically analyze any output. This, coupled with ineffective governance and/or LLM micro-hallucinations, leads to complacency—but this attitude can be mitigated by having effective AI governance and controls in place. - Ganesh Padmanabhan, Autonomize Inc.
7. Loss Of Human Creativity, Intuition And Passion
A risk that comes with relying heavily on AI assistants like Copilot is losing the human touch in creativity and design. AI can help streamline tasks, but it can’t replace the intuition, passion and vision that drive truly groundbreaking ideas. To address this, we must use AI as a tool to amplify human creativity, not replace it, keeping the focus on innovation and thoughtful design. - Kalyan Gottipati, Citizens Financial Group, Inc.
8. Failure To Follow Best Practices And Security Standards
One potential risk of development teams relying heavily on AI assistants like Copilot is the introduction of low-quality code and security vulnerabilities, as well as potential copyright infringement. AI-generated code suggestions might not always align with best practices or security standards, leading to issues in the codebase. - Pooja Jain, Meta (Facebook)
9. Skills Atrophy
Heavy reliance on AI coding assistants can lead to skills atrophy, where developers’ problem-solving and foundational coding skills deteriorate. This can weaken code quality, introduce security risks and reduce innovation. To mitigate this, teams should use AI as a supplement (not a replacement), enforce manual coding challenges, conduct thorough code reviews and prioritize continuous learning. - Balasubramani Murugesan, Digit7
10. Lost Time Fixing AI-Generated Code
Depending on the type of coding you are trying to accomplish, fixing the code in Copilot can take longer than just writing the code yourself in the first place. It’s best to use a variety of AI assistants so that if one fails to deliver, you can try another to get better results. - Syed Ahmed, Act-On Software
11. Code That Doesn’t Achieve Intended Goals
The biggest risk I’ve seen with AI-assisted code creation is the production of code that makes sense but doesn’t actually achieve what was requested in an effective manner. Mandatory code review is the best way to prevent bad code from making it to production. - David Van Ronk, Bridgehead IT
12. Lack Of Orchestration Among Systems
AI assistants like Copilot are not becoming part of multiagent systems, or a panel of experts who dynamically collaborate to solve complex problems. As we move from “everything as a service” to agents as a service, the key is orchestration: ensuring the right AI agent is activated for the right task. This is adaptive intelligence—optimizing costs, decisions and human-AI collaboration at scale. - Doug Shannon
13. Bias From Training Data Or Past Patterns
AI-generated code may introduce bias or security flaws inherited from training data or past patterns. Developers might not recognize these flaws if they put too much trust in AI outputs. This risk can be minimized by training developers to critically assess AI recommendations for biases and security gaps. - Matthew Jones, Greenhous Group
14. False Sense Of Team Capability
Overreliance on AI tools like Copilot can undermine project management by creating a false sense of team capability. If AI-generated code masks skill gaps, managers may misallocate resources, leading to inefficiencies. Since AI struggles with evolving languages, project stability can suffer. To maintain effective management, use AI for testing, where context is clearer, rather than core development. - Paul Peloquin, Thumbscore
15. Slower, More Expensive Projects
Overreliance on AI assistant tools like GitHub Copilot can slow development and make it more expensive in the long run. For example, the tool may suggest using an open-source library like D3.js for developing data visualizations to reduce upfront costs. But this approach can lead to needing more engineers to build features from scratch instead of leveraging existing solutions. Critical human oversight can prevent this. - Anujkumarsinh Donvir, ADP
16. Erosion Of Critical Thinking, Source Verification And Collaboration
One major risk of AI copilots is the erosion of critical thinking—this can weaken coding skills and add technical debt. Another risk is source verification issues—a lack of traceability can cause security, licensing or performance risks. One other risk is reduced collaboration—AI assistants can limit team discussions and learning. Solutions include establishing structured code reviews, pair programming and mentorship to ensure oversight. - Lori Schafer, Digital Wave Technology
17. Data Disconnect
LLMs, or assistants like Copilot, surface a massive amount of information, but ultimately, it’s the data that matters. Connecting AI assistants to data and, ultimately, to processes helps drive a deeper understanding of the data and allows you to connect it more holistically throughout your organization. - Alessio Alionco, Pipefy