Can Citizen Developers Now Use Generative AI to Build Applications?
Generative AI (Gen AI) has revolutionized the way professional software developers create applications by eliminating much of the grunt work. The question is: can citizen developers also benefit from this new paradigm in code creation?
Some experts believe so. Over the coming year, citizen developers will deliver 30% of Gen AI-infused automation apps, according to Craig Le Clair, principal analyst with Forrester. They have the necessary domain expertise to envision and develop these solutions, and require concerted training to ensure the safely provisioned and controlled proliferation of AI models and copilot platforms.
The Current State of Gen AI for Citizen Developers
However, one big issue is that citizen developers might not be ready to handle bare-metal Gen AI when creating applications. While Gen AI is breaking down barriers by allowing them to experiment and rapidly create no-code applications just by describing what they need in natural language, a hybrid approach remains essential.
Designing User Interfaces and Workflows
A good analogy is how a word processor allows users to switch between draft mode and full WYSIWYG layout depending on editing needs. Many tasks, such as designing user interfaces and workflows, are better suited to visual representation. Citizen developers need to extend their apps easily, which is why a hybrid approach is necessary.
Customization and Governance
Another issue is customization. Kawasaki said citizen developers need to extend their apps easily. While they could directly modify generated code, it’s easier for humans and AI to update declarative models, which are at the heart of no-code platforms. Additionally, within enterprise environments, citizen developers must consider design trade-offs, best practices, and compliance with governance, security, and regulatory standards.
Lessons from the Professionals
Gen AI-powered coding has proven to be a preferred solution for many developers. This proliferation offers important pointers for non-professionals. Gen AI coding for developers has rapidly taken off because it literally speaks their language — the language of procedural code. The code may be created differently than traditional software development, but once generated, the code output fits naturally into existing development methodologies and DevOps practices.
Risks and Considerations
However, the use of Gen AI by professional developers has highlighted some important risks. While Gen AI coding is powerful, it’s the responsibility of the enterprise to ensure proper governance to mitigate risks. Without proper oversight, AI-generated code can introduce bugs, security vulnerabilities, and inconsistencies across applications. Lack of standardization also poses risks in Gen AI coding environments.
Conclusion
In conclusion, while Gen AI has the potential to revolutionize the way citizen developers create applications, it’s essential to recognize the challenges and limitations that come with its use. By understanding the current state of Gen AI and its limitations, we can better navigate the complexities of its implementation and ensure that it is used responsibly.
FAQs
Q: Can citizen developers use Gen AI to build applications?
A: Yes, but with proper training and governance to ensure the safely provisioned and controlled proliferation of AI models and copilot platforms.
Q: What are the challenges of using Gen AI for citizen developers?
A: One big issue is that citizen developers might not be ready to handle bare-metal Gen AI when creating applications, and they need to extend their apps easily.
Q: What are the risks of using Gen AI?
A: Gen AI coding can introduce bugs, security vulnerabilities, and inconsistencies across applications, and lack of standardization poses risks in Gen AI coding environments.
Q: How can enterprises ensure proper governance of Gen AI?
A: Enterprises must ensure proper oversight to mitigate risks, and invest in composable architectures and curated marketplaces to reduce the risk of data inconsistency, variations in workflows, and uneven usability standards.
