AI in the Software Development Cycle in Practice: Reflections from Design, Frontend, and Leadership.
A Lovable Experiment.
When we decided to experiment with the lovable AI, Lovable; the goal was simple: see if AI could help us move faster from idea to interface. What followed was a useful but sobering experience that revealed both the promise and the limits of AI-assisted product building.
Design Perspective: Working with Lovable (Matilda)
Our design team’s perspective, working with Lovable was an interesting but mixed experience. It showcased the speed and potential of AI-assisted design, but also revealed what happens when strong design systems aren’t in place.
What worked well:
Lovable made it easy to generate quick layout ideas and wireframes, especially for SaaS workflows that typically require multiple states and screens. It accelerated early exploration and provided a structure to discuss with the team. The speed and automation were valuable in the early brainstorming phase.
What didn’t work well:
Most generated designs violated core design principles such as hierarchy, spacing, and consistency. Accessibility and contrast ratios were often off, and interaction logic felt random rather than system-driven. Without integration to our design tokens or style guide, outputs lacked brand alignment and required heavy correction in Figma.
What could have been better:
If Lovable could be trained or prompted using our existing design system and component library, it would have produced more coherent results. A feedback loop between AI generation and manual refinement in Figma would make it genuinely powerful for scalable design systems like ours.
Frontend Perspective: Working with Lovable (Ibrahim)
From our frontend team, Lovable currently feels more like a prototyping tool than a development solution.
What worked well:
The UI generation is impressive: clean layouts that look good and communicate intent quickly. It’s excellent for visualizing concepts and aligning stakeholders early without heavy coding.
What didn’t work well:
Under the hood, the generated code often struggles with structure and maintainability. Integrating it into an existing production codebase risks introducing technical debt and bugs over time. The generated React code also clashed with our Vue.js environment, reducing developer experience and reusability.
What could have been better:
Lovable would benefit from allowing developers to define existing project standards, folder structures, and UI libraries (Vue, React, Angular, etc.) before generation. Optional Figma exports for code review and design parity would also help. These changes could make Lovable more than a visual playground: a genuine bridge between prototype and production.
Context, Constraints, and Reality
Maybe this wasn’t Lovable’s fault: maybe it was ours.
Would the outcome have been different if we had given the designer and engineer uninterrupted time to explore it deeply on their own? Probably.
But in a scale-up environment, where every sprint is tightly scheduled, that kind of open-ended exploration comes at a real cost. The reality is that teams have to deliver. Experimentation windows are narrow, and tooling must prove its value fast.
There’s also the privacy and IP concern. Giving an external AI platform access to internal design systems, codebases, and component libraries isn’t a trivial decision, especially when proprietary assets are involved.
Still, Lovable did something meaningful: it helped us prototype faster, get feedback earlier, and iterate with clearer context. That acceleration, even if imperfect, shortened our learning cycle.
The takeaway is simple: Lovable isn’t ready to sit inside a SaaS level production workflow, but it’s a strong ally in early ideation. The challenge and opportunity lie in bridging that gap between AI speed and production rigor.
In the end, we settled on using Lovable primarily as a rapid prototyping accelerator: a way to get early structure, flow, and alignment without overinvesting in code or pixel perfection. Once the prototype captured the right intent, we handed it off to our design team as a blueprint, enabling them to layer in Wowzi’s platform-specific requirements, brand system, and usability refinements. This approach let us benefit from Lovable’s speed while ensuring the final output met our design standards and integrated seamlessly into our established workflows.
In short:
Lovable gave us speed but not fidelity, direction but not depth.
If it evolves to safely understand team context, framework diversity, and real-world code discipline, it might just change the game.
 | A guest post by
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Regarding the topic of the article, this practical reflection on AI integration in design develepment perfectly articulates the current dual nature of AI tools. The emphasis on how much more effective AI would be when grounded in roboust design systems and component libraries truly highlights where the next phase of development should focus for scalable and coherent outputs.