Team AI vs. Team DIY: What product and engineering leaders need to know about AI-powered coding

In early 2025, we conducted an internal experiment driven by a clear and timely challenge: how to reduce the time and cost of delivering high-quality software while maintaining development standards and minimizing risk.

Team DIY wrote code manually using traditional workflows.

Team AI used AI tools to guide development through prompting and refinement.

Two teams. One product. Same scope, same stack, same timeline.

What we found reshaped how we think about speed, scale, and the role of software teams in an AI-enabled future.

Learn more: https://moduscreate.com/insights/ai-t...

Timestamps:
00:00 Introduction
00:50 AI experiment overview
01:52 Current state of agentic AI tools
03:13 AI’s impact on the product development cycle
04:05 Collaborating with AI agents
05:43 Prompt engineering and communication
07:05 Problem-first thinking
08:38 AI’s rapid change and future expectations
12:06 What we built with AI
13:34 Integrating AI agents into applications
16:53 Selecting an AI model
18:16 What we learned about AI
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