Category: AI-Augmented Development
What are the biggest challenges of AI-Augmented Development?
Coding is no longer the biggest bottleneck. So what is the biggest challenge? Discover why organisations, and even colleagues, give different answers, and why that matters for overcoming yours.
Maximising AI Benefits in Software Engineering: In-Depth Strategic Overview
An in-depth overview of the key challenges and solutions to equip technical leaders and developers with a mental framework that maximises AI’s benefits. A call to action for the Tech and Software Development community
The case for an AI-Augmented Development methodology
Let’s examine what engineering teams, AI Champions and AI communities of practice need to explore, reflect on, and share. So they can apply new learnings across different teams and contexts, advance the craft, and scale AI-Augmented Development adoption.
Where AI Ends and Human Value Begins
Replacing developers with AI often leads to costly organisational mistakes. Discover how leading organisations leverage uniquely human strengths to get the best out of AI, and how engineers can future-proof their careers.
AI Coding Test-first Vs Test-after: pros & cons
Test-First is the clear winner of the pre-AI coding era. But how does AI-assisted development change the balance between Test-First and Test-After? Who is the winner now?
Coding with AI: 2 alternative philosophies
Broadly speaking, AI-coding techniques fall into two distinct and alternative philosophies, you don’t want to confuse or mix.
AI-assisted coding: how to ensure real productivity gains?
Recent studies show that the productivity impact of AI-assisted coding can even be negative. A “perception gap” can make developers feel more productive when their output drops. Here, I explore ways to pursue real positive productivity gains.
