PaaS for AI Development — And Why AI Development Differs from Traditional Software Development

June 12, 2025

The Challenge of Scaling AI Solutions

After building over 600 AI solutions, one thing has become clear: the biggest challenges in AI development are not what many assume. Contrary to popular belief, infrastructure is not the bottleneck. Companies like NVIDIA, Google, and OpenAI offer powerful and often affordable compute infrastructure.

Instead, the most pressing issues are:

Lack of skilled human resources, not engineers to program, but people who understand the domain, data, and can use AI
Lack of a streamlined development process
Lack of purpose-built tools and software for AI

Solving these is fundamental if we’re serious about creating a thriving AI economy.

AI Development vs. Software Development: Art vs. Engineering

Software development is largely engineering-driven—it thrives on structure, specifications, and well-defined milestones. AI development, on the other hand, is closer to an art form. It’s experimental, iterative, and highly dependent on data quality and collaboration.

A few key distinctions:

Unstructured Progress: Traditional software follows linear workflows. AI often doesn’t. Model training, data preparation, and iteration are non-linear, making it harder to define precise timelines.
Uncertain Outputs: AI projects may begin without a clear sense of what “success” looks like, especially in novel domains. The path to value is discovered, not predefined.
Cross-functional Collaboration: AI demands tight collaboration across teams—data scientists, domain experts, and engineers—due to data bias, ethical concerns, and evolving goals.
Creativity in Data: In many AI projects, especially where primary data is lacking, teams must innovate around data sourcing and labeling. Creativity becomes a core skill.

Like Art, AI also needs human involvement. Thus at Omdena, we define AI development with the three C’s: Collaboration, Compassion, and Consciousness—emphasizing the human-centered approach required for responsible and impactful AI.

Why Existing Tools Fall Short

We assumed project management tools like Jira and Trello would suffice. They didn’t.

PM Tools (e.g., Jira, Trello) lack support for AI’s research-heavy, iterative workflows.
Code Collaboration Platforms (e.g., GitHub) are great for code, but they don’t provide visibility into how that code connects to project milestones or value delivery.
Developer Training Platforms (e.g., MOOCs) provide isolated knowledge but fail to integrate learning into real-world execution.

AI development needed its own platform—a Platform as a Service (PaaS) designed for the unique demands of building AI solutions at scale.

Building our own PaaS for AI Development

We needed a platform that addressed key gaps:

1. Transparent Code-to-Task Mapping

In traditional tools, tasks exist separately from code. We asked: Can we build a system where every line of code is linked to a specific task or deliverable? This would allow:

Real-time visibility into project status for both teams and clients
Early detection of bottlenecks
Objective measurement of task progress—not based on subjective updates

Even if the system flags 90% of issues correctly, human reviewers can handle the rest. Machines handle scale; humans apply judgment.

2. Smart Talent Matching

In Omdena, we don’t just execute AI projects—we incubate talent from the grassroots. Over time, we’ve gathered rich data on individual growth and performance. Our insight: Motivation and learning agility outperform static knowledge.

Our platform identifies individuals not just with the right skills, but with the right attitude and growth potential.

3. Automated, Continuous Feedback

Traditional code reviews happen periodically. But what about the time between those reviews? Our AI agents provide continuous, real-time feedback on code quality, helping developers improve as they build, not after the fact. This accelerates learning and ensures outputs meet enterprise-grade standards.

The Result: Smart Project Execution with Full Transparency

So we ended up building our platform, which includes:

Lets go into the features in more details:

Sprint-based feedback loops
Code quality and refactoring checks
DevOps monitoring
AI-assisted task tracking
Automated onboarding and role assignment
Executive dashboards for visibility

PS: You can try the demo here: https://www.omdena.com/ai-platform

These features enable:

Faster identification of blockers
Better client trust through transparency
More time spent on innovation, less on admin work

The missing piece: Continuous Learning for Capacity Building

As identified in the beginning, one of the key challenges in AI is the lack of skilled humans. We had to build the platform where training must be dynamic and can:

Onboard and train contributors continuously
Embed learning into live projects
Use AI agents to suggest code improvements in real time
Allow engineers to upskill through real-world feedback, not just theory

This integrated learning system ensures that engineers don’t just contribute—they grow.

Final Thoughts

AI development is fundamentally different from traditional software engineering. It requires new processes, tools, and mindsets. At Omdena, we’ve built a PaaS tailored for this new paradigm, where collaboration, transparency, and learning are not afterthoughts, but the foundation.

If we want to unlock the full potential of AI innovation, we must invest not just in infrastructure, but also in human systems that support how AI is actually built.