What Business Leaders Can learn From Coders

The Convergence of Knowledge Work and Software Coding

A fundamental convergence is happening at an accelerated pace: the worlds of software development and everyday office productivity are merging. AI-native coding tools are not just helping developers write code; they are optimizing the creation of digital ‘artefacts.’ This is more relevant to your daily work than you might think.

Consider the daily function of a business professional. We coordinate people, effort, and resources often to produce specific artefacts: proposals, reports, strategic plans, and critical emails. The purpose of these artefacts is to drive action or change the state of the business. In essence, we are orchestrating workflows to produce a predictable outcome. This has a parallel to software development. While the technical skill sets have been distinct, the underlying purpose, the execution and scaling of knowledge work, is virtually identical.

For those without a software development background, it can be illuminating to view coding in a different light. At its core, software development is the creation of a set of documents (source code) that adhere to a perfectly logical and unambiguous set of rules. These rules seek to eliminate the potential for misinterpretation inherent in human language, allowing a machine to execute a predictable outcome. It is precisely this need to translate human intent into a machine-readable format that made software development the first and most fertile ground for Large Language Models (LLMs).

The core function of LLMs in coding is translating ambiguous human language into the precise syntax a machine understands.

Leaders, Take a Lesson Here

So, what does this mean for a business leader?

It means this same translation capability is rapidly being embedded into your own toolset, from spreadsheets to presentation software. The AI will be there to translate your teams intention into new artefacts and automated workflows. The primary bottleneck will no longer be technical execution, but the scope of your ambition. The limiting factor will be an inability to reimagine what you and your team can achieve with these new capabilities.

This is why I study the adoption of AI in software development: not to become a coder, but to understand the mental models of developers. I am learning how they think, manage complexity, and ensure products are shipped reliably. Leaders should be doing the same.

Soon, you won't need to write code to orchestrate complex digital work. But this capability will be wasted if your thinking remains anchored in today's workflows. The challenge is to start thinking like a developer: systemically, logically, and with an eye toward automation and scale.

It Seemed Kooky...For a Few Weeks

As a practical example of this convergence, I am writing this article not in a word processor, but in an AI-powered code editor called Cursor. I am experimenting with the tools of tomorrow to change how I work today, and the initial results are revealing.

[Edit: Since I first wrote this article the convergence has accelerated. All of the major vendors are landing 'coding tools' on desktops; coding tools that can do far more than 'code', coding tools that are enabled with agentic capabilities - meaning the work of artifact creation is getting automated at the output layer, not just through inputs from Large Language Models. Using Cursor, or Claude Code, or OpenAI's Codex, or Google's CLI for Gemini or similar, for Knowledge Work, and not just software coding is very quickly becoming not so 'Kooky'.

Getting onto the Front Foot

To navigate the path forward, leaders need a framework for thinking, not a feed of tech announcements.

If you are focused on separating the signal from the noise and understanding the strategic implications of AI, I invite you to connect and explore how growing your AI Literacy can shift you to a proactive posture on AI risks and opportunities.