Not all AI projects need data scientist and AI engineers.

One of the most common mistakes business leaders make in their AI project is getting the wrong team to build.

Most business leaders think AI projects need data scientists or AI/ML engineers. But most agentic AI projects actually need software architects or platform engineers.

There are two types of AI projects:

Type 1 - True AI Projects 🔹 Goal: Train or fine-tune models, experiment with data. 🔹 AI’s Role: Core to the solution. 🔹 Key Challenge: Model performance, data quality, and training at scale. 🔹 Team Needed: Data scientists and AI/ML engineers.

Type 2 - AI Application Projects 🔹 Goal: Build AI-powered applications like RAG chatbots or AI agents. 🔹 AI’s Role: Mostly an API call. 🔹 Key Challenge: System design, scalability, retries, queues, and orchestration. 🔹 Team Needed: Software architects and platform engineers.

Most enterprise projects are Type 2, where the challenge isn’t the AI itself but designing a scalable and resilient system architecture.

If you’re a business leader in charge of AI transformation, ensure you know which project you have and get the right team.

How can you tell? Here’s a quick checklist: 🔹 Are you training or fine-tuning a model? Data scientists and AI/ML engineers. 🔹 Are you just consuming APIs? Software architects and platform engineers. 🔹 Does the project need a scalable architecture, retries, queues, or orchestration? Software architects and platform engineers.

Getting this right early can save months of wasted effort.

Have you seen this mistake happen in your organization? What other common pitfalls have you observed in AI projects?

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