AI and I built a tool in :15. What does that mean for you?
Lessons from the trenches
Quickly find first-degree contacts on LinkedIn with this app, built entirely by AI
I just built a nifty LinkedIn Connection Tool in 15 minutes, with zero manual coding, using Claude AI's Artifact feature.
Business & technical leaders might have a range of strong reactions to this: concern, excitement, and skepticism. Let me give you a peek at the tool, share my key takeaways from the experience of building it, and lay out some recommendations for both leaders and developers.
My key takeaways
People’s roles will change. The AI didn't just code – it suggested improvements and alternatives, which we’ve traditionally gotten from human feedback loops. Even given that, however, it’s important to remember that AI is an expression of how we’re taught to think about software development. It will not create original thought or principles, but it will work blindingly quickly to build and optimize based on foundational human input.
How can you use this to improve the way customer requirements are discovered and vetted?
How can you put this to use to help developers to develop a more empathetic perspective of their work: moving from code-only to collaborative development?
How might this reshape your hiring, training, & development processes?
This should help us re-imagine not just rapid prototyping but the lifespan of tools. AI solved a specific business problem in minutes, not days. I didn’t need a developer and I went from Idea -> Shipping during the course of a cup of coffee. No PRDs required.
How could this accelerate your product development?
How does that change how you think about tactical projects and “small asks”?
Ethical AI must be paired with human guidance. I didn’t tell Claude to obey LinkedIn’s terms of services; it just did. But not every AI tool will be built with that in mind.
How will you evolve your Data Governance policies to include how data are accessed and used with this new generation of tools?
Do you have a “Code of Ethical Coding”, ensuring that Intellectual Property is respected, code is unbiased, and developers retain accountability for the code created under their purview?
Implications for leaders
All teams must balance the rush to innovation with ethical diligence
Technical leaders must reinforce foundations and principles more than ever
Engineering leaders should expect to hire & train differently and staff projects with new roles
Product leaders will need to re-think how customer feedback is given and received
Want to learn more?
To learn more about how I built the tool, you can:
Read this not-too-technical explanation, here
Read a summary of the process, published here
Reproduce what I did, starting at https://claude.ai and following this transcript
The code + transcript is available, here.
Want to stay up to date on AI’s impact on business transformation? Let's connect.
Thank you to Alexander Williams and Jason Shaeffer for their feedback on early drafts of this article