After a decade in software development, I made what many considered an unusual pivot: I moved into marketing. I have been asked why leave the perceived prestige of engineering for the “softer” world of marketing?
The truth is, I have always been fascinated by behavioural psychology; the why behind user actions, not just the what. My passion for analytics and statistical modelling hadn’t diminished; I’d simply found a more compelling dataset: human behaviour. Marketing has become a perfect application for them.
Today, armed with AI coding assistants such as Cursor and Copilot, I don’t have to choose between being a developer or a marketer. I can finally be both.
The Developer’s Eye on Marketing Problems
The marketing world is full of unsolved technical problems that were considered too small to justify developer resources. Tasks that would save hundreds of hours annually weren’t being automated because they didn’t merit a sprint. Insights that could transform strategy weren’t being captured because the ROI calculation never quite worked out for formal development.
So I started building solutions myself. Chrome extensions for analysing competitor pages. Tools for summarising content and creating audio overviews. Automation scripts for tasks that marketing teams had simply accepted as manual necessities.
Each tool took hours to build but have saved me weeks of work (admittedly, I do get this wrong sometimes and automate tasks that would’ve been quicker just to have done manually).
When BothWorlds Collide
Having lived in both worlds gives me a unique perspective on what’s happening with “vibe coding”; the practice of building software by describing what you want in natural language rather than writing traditional code.
For example, recently I needed to analyse competitor content across multiple sites. Rather than writing JavaScript from scratch, I opened Cursor and typed: “Create an analysis that extracts the main value proposition from any webpage, identifies the emotional triggers being used, and scores the content’s readability.”
The AI didn’t just generate code; it asked clarifying questions about scoring methods and psychological principles. Within an hour, I had a working tool that would have taken days to build traditionally.
It’s reminiscent of pair programming, though not without flaws. Despite occasional setbacks, the speed at which you can write code is remarkably superior when you understand what’s being developed. I could immediately request additional statistical analysis features without tickets or sprints; just immediate evolution based on real insights.
Whilst you can achieve results quickly, I must emphasise the importance of understanding what you’re doing and what outputs to expect before letting the AI generate results.
The Compound Effect of Technical Marketing
Start with something simple. Maybe you’re tired of manually checking competitor social media. You describe to Cursor: “Build a tool that checks these five Twitter accounts every morning and summarises what they posted about.” The AI helps you build it, and there you have it, a prototype for a simple daily competitive intelligence app.
But then you notice patterns in the data. So you go back to your AI assistant: “Add sentiment analysis to this tool and track how their tone changes around product launches.” Now you’re not just monitoring; you’re understanding behavioural patterns.
Each iteration teaches you something new. The tool evolves based on what you discover, creating a feedback loop between technical capability and marketing insight. This compound effect accelerates both your understanding and your ability to act on that understanding.
Beyond Single-Channel Thinking
The traditional marketing stack forces us into silos. Your email tool doesn’t talk to your social media scheduler, which doesn’t connect to your SEO platform. But when you can vibe code, you can build the bridges yourself.
Here’s a practical example: I wanted to understand how our blog content performed across all channels, not just in isolation. I described to GitHub Copilot: “Create a script that pulls data from Google Analytics, matches it with our email campaign results, and correlates it with social media engagement for the same content pieces.”
The AI helped me build exactly that. Not a perfect enterprise solution, but a working tool that gave me insights no off-the-shelf product provided. The key insight? You don’t need to build everything. You just need to build the connections between everything.
For marketers reading this, start by identifying where your data lives in isolation. What insights are you missing because tool A doesn’t talk to tool B? That gap is your first vibe coding project.
Building Reputation Through Building Solutions
One unexpected benefit of vibe coding: it transforms how you build authority in your field. Instead of writing another “10 Tips” article, you can share actual tools that solve real problems.
When I publish a Chrome extension that helps marketers, I’m not just demonstrating knowledge; I’m providing immediate value. People can install it, use it, and benefit from it today.
But here’s the key: you don’t need to build complex tools. Start with simple solutions to common annoyances. A bookmark organiser for marketing resources. A calculator for social media posting times across time zones.
Getting Started with Vibe Coding
Here’s a practical roadmap for marketers who want to start vibe coding:
1. Choose your tool: Cursor and Copilot are the main options (However, there are others such as Lovable and Windsurf. Pick a tool, familiarise yourself with it and stick with it).
2. Start with something simple: Chrome extensions or small HTML pages don’t require complex setup or hosting, you can quickly see the instant results of your work.
3. Describe, don’t code: Write what you want in plain English. “Create a button that copies all email addresses from this page” is better than trying to write JavaScript.
4. But be specific: Imagine you are talking to a developer. The more information you can give them, the better the outcome will be. Include context about your end goal, not just the immediate task.
5. Save your iterations: Don’t learn this the hard way. AI can go dramatically wrong, and you can feel like you’re right near the finished product, only to have it implement a bug that becomes really hard to fix. You’ll wish you’d have rolled back just to the previous version.
6. Share your builds: Even simple tools can help others. Sharing also creates accountability and feedback loops that improve your skills. Consider opensourcing simple utilities on GitHub.
The beauty of vibe coding is that you’re always just one conversation away from a solution. You don’t need to spend months learning to code (But I definitely recommend learning the basics). You need to refine your skills in learning to describe problems clearly.
Moving Forward
The convergence of AI and accessible development tools is democratising solution-building. Not everyone needs to participate, but those who do will find How AI-Powered Development Tools Are Reshaping Marketing Intelligence 4 themselves solving problems faster than ever before. You will end up saving not just your time but money along the way.
And hey, you might just have some fun along the way!
For marketing teams, this shift represents an opportunity to become more agile and self-sufficient. For individuals, it’s a chance to multiply your value by becoming the person who doesn’t just identify problems but builds solutions. The tools are here, the barrier to entry has never been lower, and the potential impact has never been greater.
About the Author: Jon Goodey is CEO at Indexify, bringing over ten years of software development experience to the marketing world. He publishes a daily newsletter on Marketing Intelligence and has developed multiple Chrome extensions that merge technical capability with marketing insight.
