I combine hands-on development with coaching to help organizations move from AI experiments to real business results. As a CTA certified coach, I guide technical leaders through practical adoption and help teams avoid the pitfalls that derail most AI initiatives.
In my exploration of AI coding tools, I've discovered that while they promise productivity gains, they can also introduce challenges in the software delivery lifecycle. By measuring code quality, batch size, and delivery, rather than just adoption rates, we can gain valuable insights. I share five key metrics derived from GitHub's API that can help teams identify problematic development patterns early on.
In my recent experience, I faced the challenge of creating a clear interface guideline for a third-party platform while dealing with legacy code. By leveraging AI assistance, I was able to streamline the process and produce comprehensive documents for four SDKs in just one day. This approach not only clarified our requirements for potential vendors but also enhanced our project's efficiency.
At the Monet Venice exhibit at Brooklyn Museum. The paintings only make sense when viewed from far enough back that technique gives way to the feel of mist, light, and water. I got that in gap tooth patches as crowds threaded by at touching distance from the canvases - losing forest for trees.
My first thoughts in any leadership role is who is my team. Who are my partners. Who are my advocates. Who can I really trust to do their job well, to focus on collective success, and be candid with me. That’s everything. Full stop.
I believe there is space for an AI assisted coding practice that aids in the prep, research, planning of a coding task, the completeness review and retrospection of the completed task but does not generate the code and tests itself.
The space would work for master developers working in high value, highly sensitive or safety spaces. For more junior devs and as part of every developers ongoing skills development as needed to keep our craft and minds sharp.
Is someone working up this practice? An ai assisted plan, human do, ai assisted check and act?
Recognize talent, value experience, develop human potential. You know, for the AI’s sake.
AI enables us to…
Replace software engineers with people expert at building, enhancing, and maintaining useful, safe, reliable, and cost efficient software.
Replace designers with people expert at crafting usable, consistent, and delightful experiences.
Replace product managers with people who understand why features exist, what customer problems they solve, and can validate they return on investment.
I.e. software engineers, designers, and product people.
AI is reducing the need for people who hack code, sling components on a page, and ensure the spreadsheets run on time.
And increasing the need for people who are genuinely good at their respective roles.
So, again, maybe we should recognize talent, value experience, develop human potential by investing in mentoring and training. ‘Cause AI.
People are working on disciplined practices to improve the code produced in collaboration with ai agents. People send me links to projects all the time and I thank you for that.
I’d also appreciate links to any research quantifying the impact of these practices on delivery speed and code quality.
I believe that undisciplined code generation leads to significant issues like code duplication and large batch sizes, which ultimately slow down delivery. As coders, we must actively engage in the coding process, focusing on analysis and execution to minimize code cloning and enhance incremental testing. Our unique human insights are essential in navigating complex codebases and ensuring effective project outcomes.
A practical PDCA framework for accountable AI code generation. Structure sessions in phases: agent-led analysis and planning (compatible with plan mode), test-first code changes, validation against goals, and targeted retrospectives to refine prompts. Apply these agile disciplines to maintain quality and control over the code you ship.
The race to adopt AI code generation is a sustainability crisis that Agile practitioners are uniquely positioned to solve. Here is a disciplined Plan-Do-Check-Act (PDCA) cycle. Each step has a distinct purpose and builds upon the prior ones.
Use AI tools to retrieve the conceptual design of legacy software and reduce the toil of lengthy up-front design.
Selected posts from my earlier blog, where I wrote about agile transformation, leadership, and building software teams.
I’m doing most of my writing these days as @kenjudy on LinkedIn. It’s where people can find it, right now. More personal things I put on @kenjudy at mastadon.social. Related posts: HICSS-42 Six questi...
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