For years, businesses have faced the same problem: there is always more need for software than there are developers available to build it.
Operations teams need better workflows. Finance wants more visibility into budgets. HR needs an onboarding portal. Sales wants a customer-facing app. Field teams are struggling to enter data into spreadsheets while on location. Every department has repetitive processes that could be automated or spreadsheets that have outgrown Excel, yet engineering teams rarely have the time to prioritize every internal request.
Citizen development emerged as a response to this imbalance. Rather than forcing every software project through IT, organizations began giving business teams the tools to build applications themselves. Low-code and no-code platforms have enabled people without traditional programming experience to create useful software, dramatically expanding who can participate in software development.
Today, artificial intelligence is changing that idea once again.
The barriers to building software are falling even further. AI can generate interfaces, write logic, design databases, and even produce entire applications from a natural-language prompt. AI-assisted engineering is significantly faster, easier, and more powerful, making real citizen development programs more achievable than ever for businesses. But it has also introduced challenges.
Here’s what citizen development looks like in the age of artificial intelligence.
What is citizen development?
Citizen development is the practice of enabling employees outside of IT to create applications and automations that solve business problems. The term citizen developer typically refers to anyone who builds software despite not being a professional software engineer. Citizen developers can use platforms (such as no-code or AI tools) to design custom applications, manage data, and automate workflows.
These employees often understand business processes better than anyone else. They know where approvals slow down, which spreadsheets have become impossible to manage, and which repetitive tasks waste hours every week. Instead of having to go through IT or engineering teams and wait months for a solution, citizen development allows them to build it themselves to suit their exact needs in the moment.
Citizen development in the age of AI
In many organizations, citizen development now begins with AI.
An operations manager uploads a spreadsheet and asks an AI assistant to turn it into an application. A customer success team describes a workflow they need, and the platform generates an initial version complete with forms, dashboards, and automations. A finance department asks AI to automate approvals and reporting without manually configuring every step.
The first version appears almost instantly. From there, the human work begins.
Teams refine workflows, improve the user experience, connect external systems, define permissions, and iterate as business processes change. AI handles much of the repetitive construction, while employees apply their operational expertise to create software that genuinely reflects how their organizations work.
Benefits of AI citizen development
Citizen development empowers teams to build their own software to solve problems at work. Businesses that can build a robust citizen development program can become significantly faster, more efficient, and more effective. These are the key benefits businesses get from cultivating citizen development.
Faster software delivery
The most obvious benefit is speed. Business teams no longer need to wait for every internal application to move through an IT backlog. With AI-assisted development, many tools can be built in days or even hours instead of months.
When that software needs to evolve with changing processes, updates can be made almost instantly instead of waiting on IT tickets. That means that software stays relevant and useful for longer. With citizen developers, organizations can respond more quickly to operational challenges, test new ideas, and continuously improve internal processes instead of waiting for annual software projects.
Better software for the people doing the work
Citizen developers are usually the people closest to the problems they’re solving. Because they understand day-to-day operations, they can build applications that better reflect how work actually happens rather than how someone assumes it happens. This often leads to higher adoption, fewer unnecessary features, and software that evolves naturally alongside the business.
Financial savings
Often, citizen-developed tools end up bridging gaps between legacy software systems and giving workers more useful interfaces. Businesses may be able to retire bloated off-the-shelf software with unused features by using a few streamlined custom apps. They can also save on seats for users who only need to log in to see data by connecting that software to an easier interface for view-only team members.
More innovation across the organization
With AI lowering the barrier to software creation, employees no longer need extensive technical knowledge before experimenting with new ideas. They can start with an AI-generated application, gather feedback from colleagues, and improve it over time. As a result, more ideas become working software.
This encourages a culture of creativity, innovation, and continuous improvement, where every department has the ability to solve its own operational challenges. It also has the potential to improve morale as team members deal with fewer operational frustrations, build things they can be proud of, and celebrate each other’s wins.
Better use of engineering resources
Citizen development doesn’t replace professional developers. It actually allows them to focus on higher-value work.
Engineering teams can prioritize customer-facing products, infrastructure, security, and complex integrations while business teams create the departmental applications they need. This increases the organization’s overall software development capacity without requiring a proportional increase in engineering headcount.
Challenges of AI citizen development
Citizen development can be challenging to implement if you don’t have the right tools or the right approach with your team. Here are some challenges businesses should watch out for when implementing a citizen development program.
Choosing the right projects
Not every system should be built through citizen development.
Departmental tools, approval workflows, dashboards, customer portals, and operational applications are often excellent candidates. Highly regulated systems, safety-critical applications, or software with complex technical requirements generally require closer collaboration between business teams, IT, and professional developers.
The goal isn’t to eliminate engineering. It’s to allow every team to build the software appropriate for its level of complexity.
Governance and oversight
As more employees build applications, organizations need visibility into what is being created.
Without governance, businesses can quickly accumulate dozens of disconnected tools with duplicated functionality, inconsistent data, and unclear ownership. Successful citizen development programs establish standards, approval processes, and platform guidelines without recreating the same bottlenecks they were intended to eliminate.
Security and data management
AI-powered coding platforms, also known as “vibe coding” tools, can generate software quickly, but they can also present significant security risks. Vibe coding tools have been shown to reproduce insecure code, and using LLMs makes it easy to inadvertently share private data.
Companies need to carefully choose the platforms they use for citizen development programs. If your team is building apps that don’t deal with data, like landing pages, games, and lightweight informational apps, they can use vibe coding platforms like Lovable. If you’re building business applications that manage customer, employee, financial, or operational data, you’ll need to choose an AI platform that provides strong security assurances, like GlideOS.
Long-term maintenance
Your citizen-built tools need to be able to adjust as your needs change and grow with your scaling business. Business processes evolve. Integrations change. Regulations shift. New employees inherit existing systems.
Using AI for citizen development can make changes incredibly fast if you’re on a governed platform. However, scaling vibe-coded software can be a significant challenge as you don’t have developers who can understand the AI-generated code and make changes to it. Structured citizen development platforms generally make it easier to scale and maintain your software than standalone AI-generated code because the application’s logic, data, and workflows remain visible and manageable.
Finishing a functioning app
The bottleneck in AI engineering is no longer getting a prototype up and looking great. It’s finishing the final 10% of a build, which is critical. This includes things like verifying safety and trust, debugging, and adding more complex features like SSO, user authentication, integrations, automated workflows, and more.
Tools and platforms for citizen development
Various tools and platforms exist to help non-coders develop software. These tools may be designed for different users, have varying learning curves, and build different types of software. Businesses wanting to use AI to help their teams become citizen developers will need to choose a tool or combination of tools that best suits their needs.
No-code/low-code platforms
For most of the past decade, citizen development has been closely associated with no-code and low-code platforms. Instead of writing code, you can assemble applications using visual development, designing user interfaces, configuring workflows, and connecting databases. Low-code platforms are designed for technical users and still require some coding knowledge. No-code platforms don’t require coding, but still require users to have some technical skills and may have a learning curve.
Many low-/no-code platforms are adding AI-assisted features and development capabilities, but they are still largely structured around prebuilt components and drag-and-drop design systems.
Best for:
While no/low-code tools are being supplanted by more modern AI-based platforms, they are still the right tool for specific use cases. For example, teams that are heavily invested in the Microsoft ecosystem might want to continue using Power Apps since it ties into their other tools.
Tools:
- Bubble
- Softr
- Microsoft Power Apps
Vibe coding
Non-developers can build software using AI coding assistants or LLMs such as ChatGPT or Claude Code to write code for them, a practice known as vibe coding. Entire applications can be built in minutes, allowing users to validate ideas far faster than with traditional development methods.
Instead of working inside a structured no-code platform, users prompt an AI model to generate code directly. The AI writes the code for websites, applications, APIs, databases, and interfaces from natural language instructions. Vibe coders are still working directly with code; it’s just generated for them. They will still need help from someone with technical knowledge to make the software they’ve coded secure, add essential functionality for apps (like authentication), and scale or debug apps.
Best for:
For prototypes, experiments, and small projects (especially things like static web pages), this approach can be very effective. It is less effective for business-critical tools or anything dealing with sensitive data.
Tools:
- Lovable
- Bolt
- Replit
- Base44
- Claude Code (partially)*
- OpenAI Codex (partially)*
- Gemini CLI (partially)*
*Note: While these tools are valuable to engineering teams, they are built more for technical users and thus aren’t truly considered “citizen development” tools since less technical team members will struggle to use them effectively.
EMBED: https://www.glideapps.com/blog/vibe-coding
AI business software platforms
AI business software platforms combine natural language prompting of AI with additional abilities, support, and easier-to-use interfaces specifically suited to creating internal software for work.
Business applications eventually require authentication, permissions, integrations, governance, reliable data models, maintenance, and ongoing updates. As more employees rely on an application, these considerations become increasingly important.
This is where AI-assisted citizen development platforms differ from pure AI coding tools. Rather than simply generating code, they provide structure around the application itself. AI helps users build faster, while the platform manages permissions, data, workflows, security, and scalability.
Best for:
Building internal tools and systems for work, especially those that deal with private business data.
Examples include:
- CRMs
- Operations dashboards
- Inventory systems
- Field service apps
- Customer portals
- Approval workflows
- Business agents
Tools:
- Microsoft Power Apps with Copilot (partially)*
- Salesforce Agentforce + AI app generation (partially)*
- Retool AI (partially)*
*Note: GlideOS is a new category of platform that sits adjacent to AI engineering rather than competing with coding tools. It would be considered “AI-native” because AI is central to how applications are created and evolved, rather than being an assistant layered onto a traditional low-code environment like the other four platforms listed.
Successful citizen development starts with a cultural shift
In many ways, AI marks the maturity of citizen development, moving it from a useful technique to a critical capability that businesses will need to master to stay competitive.
The organizations that benefit most will be the ones that can enable the people closest to their operations to continuously improve how work gets done. The most important thing those companies can do to be successful is create a cultural change within their organizations that empowers employees to start generating ideas for improvement, acting on those ideas, and sharing what they’ve built with their teams.
Employees who might previously have been intimidated by software development can now start with an AI-generated foundation and refine it over time. Rather than focusing on how to build every component, they can concentrate on how the application should support the business process.
AI changes more than just speed; it makes more workers confident enough to start creating their own tools.
