A main part of product manager Russell Dias’ job is working out what parts of his company could benefit from an AI solution.Supplied
8.30am.I’d say product management is the art,science,and practice of making products successful. I lead Employment Hero’s suite of AI products,which means assessing our most pressing problems within the business and seeing if AI could be an impactful way to solve it.
A lot of my time is spent on pulling insights from customers,stakeholders,and data and writing out specifications for our R&D teams.
We work flexible hours and I work from home,so the commute is short! Once I’m at my desk I like to start my day by going through the usage data from the previous day to see how people are using what we’ve built.
At any given time,we typically run two to three large AI initiatives. This morning,I spend some time on a new feature called Hero AI. It helps employees of organisations to get answers to pressing questions about their company’s policies and allows their HR department to be a lot more efficient.
I also try to make time to read through every piece of customer feedback. It all helps me understand where our product stands and ensure that we are heading in the right direction.
10am.I catch up with our data scientist and engineering lead to discuss some experiments we’ve been running. Given a lot of what we build is non-deterministic and uses generative AI tech that is super new,there’s a lot of experimentation and evaluating we need to do.
I then spend a couple of hours working on a Product Requirements Document (PRD) based on our chat and writing it up into a detailed specification for our engineering team.
1pm.I typically eat lunch at my desk and read X (formerly Twitter),where I’ve created a number of lists of key people in generative AI to help me stay on top of new things happening in the space.
I go through my lists and see a tweet from Langchain (a tool that we use) that talks about a particular innovation in how they perform evaluations of models. I send it to one of our data scientists who is working on a similar problem.
2pm.We’re Australian made,but we also have engineers spread across the globe,so we often have engineering stand-ups in the early afternoon. Today I join one of the teams I’m collaborating with and directly contributing to,and we move through problems,blockers and questions.
4pm.Most of my afternoons are blocked off for any core problems I need to work through. Today,I’m looking at the evaluation pipeline we’re building out for Hero AI. We want to have a deeper understanding of how our generative AI handles questions and answers at scale and how that changes when we make any improvements to the system.
When building out generative AI infrastructure,it’s difficult to gauge how any change impacts the overall system,so we like to be super data-driven in understanding all our changes.
5pm.I have a newborn,so I have a hard stop at 5 pm - I’ll usually then go and play with him. Tonight,I’ll log back on later to have everything ready for a meeting tomorrow.
Previously I would have just continued working a bit later to finish the work,as I don’t like stopping halfway through if I’m on a roll. I can’t do that any more. I’m a super new dad,so I’m still trying to find the right balance here.
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