A number of threads are converging:
- Coding assistants like GitHub Copilot, Codeium, Cursor, and Replit Ghostwriter are integrated directly into IDEs – or in some cases, are the IDEs.
- Autonomous agents or agent environments that can generate and execute code, like Microsoft Magentic-One or Langflow .
- Application generation environments like Replit takes this even further, generating full applications from natural language prompts and deploying them with just a few clicks.
This paints a compelling picture of what’s next in application development – and it’s GenAI assisted.
Let’s be pragmatic though, this won’t replace massive, mission-critical ERP or CRM systems overnight. But the shift is already underway, and the impact on design, development, and delivery is real.
What is it?
Generative AI development is the practice of using GenAI to create functional software directly from natural language input. You describe what you need, and the AI takes care of the rest – writing code, executing and debugging it.
In some cases, building the application isn’t the end goal, it’s simply something an AI agent does along the way to accomplish a broader task. Large language models have already been quietly generating code in response to our prompts for some time, this is just the next step in that evolution.
This isn’t just no-code or low-code. It’s co-creation with intelligent agents – where natural language becomes the starting point for real, structured execution.
What does it mean from a business perspective?
This evolution has real implications for how we build software, and who gets to build it:
Upside
- Democratized Development: Lower barriers to entry allow citizen developers to create functional prototypes and tools.
- Faster Time-to-Market: Prototypes, MVPs, and internal tools can be developed in days, not weeks.
- Cost Savings: Reduced need for manual coding on routine tasks can free up time and budget.
- Innovation at Speed: Teams can test more ideas quickly, fail fast, and iterate often.
- Developer Upskilling: Developers become AI orchestrators, directing agents, not just writing every line of code.
- Smarter Design Processes: Wireframes become interactive, AI-generated mockups (even complete functional application components – are they really wireframes anymore?), shortening the gap between vision and execution.
Challenges to Watch
- Technical Debt: AI-generated code might not follow best practices or scale well and needs to be validated.
- Quality Control: New QA processes will be needed to validate agent-created code that is going to persist into applications.
- IP and Licensing Questions: Ownership of AI-generated code still seems to be evolving.
- Procurement Shifts: RFPs and vendor evaluations must adapt to AI-generated components. Checking in with vendors on whether their application contains GenAI created code and how their processes have changed to accommodate this.
- Governance Gaps: Organizations need clear policies on where and how AI generated code is used.
What do I do with it?
Now is the time to get hands-on, experiment, and prepare your team for this shift.
- Try it Today: Set up a Replit account or use Copilot in VS Code to build something small. See what’s possible.
- Integrate AI into Design: Use GenAI during early-stage workshops to create live fully functioning apps.
- Level Up Prompting: Prompt engineering is becoming a key skill, invest time in learning how to guide these tools.
- Redefine Team Roles: Identify where agents can augment current work and where humans still add the most value.
- Establish Governance: Create clear rules for reviewing, testing, and approving AI-generated outputs.
- Pilot Internally First: Test AI-assisted development on internal tools or low-risk projects.
- Monitor Quality & Security: Add new layers to your QA and DevSecOps processes to vet agent-generated code.
- Plan for Sustainability: Avoid a sprawl of throwaway apps by addressing technical debt and long-term maintainability early.
Generative agents are redefining how we design and develop software. This isn’t about replacing people, it’s about amplifying talent and reducing the friction between idea and execution.
Whether you’re a developer, designer, PM, or executive, engaging with these tools now puts you in a position to lead the next wave of innovation, not just react to it.
Let’s connect – I’d love to hear your thoughts and experiences.
Further Reading
6 AI trends you’ll see more of in 2025 (Microsoft)
#GenerativeAI #AIinDevelopment #GitHubCopilot #Replit #DigitalTransformation #PromptEngineering #FutureOfWork #TechLeadership