How I work… | Notion

I’m a product leader, designer and former engineer currently focused on Applied AI.

Previously, I led product teams at Tradify, Adherium, Bloomberg, and 12080.AI, and helped conceptualise, design and build products for Isobar, Kotahi, Mitey, AJ Hackett, News International, and AKQA among others. I have deep multi-domain expertise across product management, design leadership, user experience research and design, software engineering, and marketing.

If you’re looking for someone to innovate and lead your product, manage one or more cross-functional product teams, or advise on how AI tools and technologies can be integrated into your product and processes, I may be able to help.

My product philosophy includes:

Abstracting and considering the core problem

Start from first principles, habitualize the “The Five Whys”.

Not reinventing the wheel

Analogous problems have often already been solved in other industries - look and learn before you build. If you’re a startup, utilise established and trusted 3rd-party solutions to shortcut development time until you have found product-market fit, and have acquired enough capital and resource to optimise.

The time to pivot into AI is now

Companies that aren’t right now devoting resources to exploring how AI might augment or replace core product functions will be disrupted out of existence.

Using Job Stories, not User Stories

User stories and customer personas can be useful when considering the entire CX journey, particularly marketing and the buy decision. However, when it comes to product design and understanding users, I don’t find Agile user stories and personas to be particularly useful. The way people use (or want to use) software is based on their current goal, not their job role or personality type. I much prefer the JTBD framework (the Intercom / Bob Moesta version).

How personas and user stories fail is succinctly illustrated here.

Organisations should be product led, not sales & marketing led

Sales teams are important for Enterprise sales, and good Marketing is both crucial and difficult, but both departments jobs’ are much easier if the product is best-in-class. And the only way to achieve that is by being product led.

Being purely data-driven only works at scale

I’m in favour of a combined quantitative, qualitative, and intuition based approach, and you can uncover a lot just from user interviews and observational studies. Unless you have 50-100k+ users and a dedicated data science department, A/B tests are expensive, slow, and prone to error and misunderstanding. At smaller scales I would generally only use A/B tests for “big bets”.