My AI journey: from a parking ticket to my own mobile app

Beating a parking fine with AI

A few years ago I came back to my car and found a parking fine tucked under the wiper - £60, reduced to £35 if I paid quickly and quietly. I was adamant it had been issued wrongly - there was not a single sign telling me I could not park there. Before I lodged the appeal, though, I wanted to be sure I actually had a case. So, I handed ChatGPT the evidence - the signage, or the lack of, the exact location I had parked, the time printed on the ticket - and asked it whether I had grounds to challenge. Within seconds, it told me I did. I was flabbergasted at how quickly it had taken all the information, and come to a decisive, unequivocal response.

I then asked ChatGPT to draft an appeal, in the driest, most procedural English that was befitting to a local English council. And what came back, within a blink of an eye, was a small masterpiece of bureaucratic nonsense: a reference to the relevant traffic regulation order, a calm paragraph about inadequate signage, not one word of the outrage I actually felt. I read it back, changed nothing and sent it. A few weeks later I received an email stating my parking appeal had been successful. £35 saved, and a slightly smug realisation - this tool could support me in a field I knew little about - and save me some money along the way.

AI in the workplace

When GenAI tools such as ChatGPT, Gemini and Copilot arrived in the workplace, it felt like everyone discovered the tools within the same week. The first wave of workplace use was small and a little sheepish: checking the tone of an email before hitting send, or pasting a 40-message thread in and asking for the 3 things that actually mattered. Then the meetings started writing themselves. You would leave a Teams call and the intelligent recap was already waiting - AI meeting notes, recommended tasks, a speaker timeline of who said what - no frantic scramble to type up before the next meeting started. Low stakes, high frequency, but a genuine breakthrough all the same - spellcheck had finally found its older, much smarter sibling, the one who read the room, drafted the reply and tidied up after you.

Once I became confident and comfortable using GenAI for the daily tasks, I explored more deeply and started pulling commercial data out of the messy stuff too. A raw Excel export of customer data that would have cost me an afternoon of pivoting - Copilot gave me all the context I needed within a minute. A 30-page strategy PDF document - summarised, cross-checked, interrogated - turned into something a stakeholder could not wave away. A key responsibility as a product leader is being able to aggregate qual and quant data and turn it into a compelling strategy with a clear story and business case. GenAI did not replace that responsibility, it sped up the thinking behind it - the analysis arrived quicker, the commercial rationale landed faster and stakeholders got an answer instead of a promise to follow up. What it could not do was tell me what was actually right for the business now and going forward - that still relies on experience and know-how.

That know-how, and the judgement to use it, mattered most in the room where the budget gets decided. Stakeholder buy-in has always been half evidence and half storytelling, and these tools quietly improved both - the evidence arrived faster and the story landed sharper. I started walking into leadership and senior stakeholder sessions with the analysis already done and the obvious counter-arguments already mapped. The value had moved from "can you help me shape this?" to "have you seen this?".

Not every experience in the workplace has been a good one. I once asked a team member to share a draft email with me, so I could proof it before they sent it on to senior stakeholders, only to realise, reading it, that it had been written entirely by AI. I gave clear amends to match the relevant tone and tailor to the audience, to keep it specific. What came back was not my amends, but 2 or 3 more AI-generated options which again did not apply to the audience in question. The tool had done the easy part, generating text, but unfortunately was unable to apply context or judgement.

The agentic AI era... and my own 0-1 app

Although the above was mind-boggling in terms of its capabilities and its speed of output, the agentic AI era has surpassed anything I thought possible. Agentic AI is not a single tool that answers and waits, it is a different way of working entirely. The tools I have used to date bridge the gap between design, engineering and product - they discover, plan, design, build, test and iterate. I provide the guardrails, the goals and the outcomes, not a task or an individual prompt. I use them to turn rough ideas into proof of concepts, and proof of concepts into tangible, working product I could iterate myself, with the team and put in front of stakeholders within a week.

It has changed how I think about the product manager role itself. The T-shaped product manager, or perhaps the full-stack product manager, is what this is heading toward: someone comfortable being responsible for the design, the code, the engineering and the product itself, not just briefing the people who do each. If you sit across any of these disciplines today and have a half decent grasp of the other 2 disciplines, you are in a great position to become a full-stack product person with the support of agentic AI.

So I put the above instinct into practice and built an app myself. Authorling is a kids' story-making app: pick a world, a character and the weather, and it generates a personalised, illustrated short story with fill-in-the-blank words, calibrated to the English school curriculum. It is a genuine 0-1 product - more than 200 stories, built with React Native and Expo, tested through TestFlight. I shaped the plan in Claude planning mode, designed the screens and the flow and used agentic coding to build it, through many, many iterations to an MVP. Authorling is currently in beta testing - so if you're interested in checking it out, drop me a message or visit the Authorling web page here: https://www.livewand.com/authorling.

Building Authorling taught me the thing I keep coming back to. When an agent can ship a feature in an afternoon, engineering stops being the bottleneck and judgement takes its place. Every idea is cheap to try, so the question of "how long will this take?" is now redundant and instead full focus is on: "will this add customer and business value?". That is a product problem, not a technical one - the same discipline that had me check whether I actually had a case for a parking ticket appeal before deciding to take further action.

Conclusion

Looking back, the constant across all of it - the parking ticket, the strategy decks, Authorling - has not been the tools, it has been judgement. GenAI made me faster at gathering evidence and building a case; agentic AI made me faster at building the thing itself. But knowing whether I had a case, whether a stakeholder was actually convinced, whether a feature was worth shipping - that was never the AI's call to make, it was mine.

What has been your own experience of AI in the last few years? And what does the AI of the future look like to you?

I have many more topics I'll be covering in my blog so keep a look out for those and in the meantime check out Authorling!