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I am one of those product managers who didn’t fall into the profession; more that I gravitated to it. My background is in health data research and my ethos of wanting to get things done has meant I’ve picked up a range of skills over the years to get things done! Product management has felt like home to a lot of these skills.
For the past 2 years, I’ve led the development of AI health tech products in a time when there were no clear UK medical device guidelines and the market was unclear. I’ve been navigating this complex landscape and this blog is as much for me to make sense of it all as it is for others who've reached out for guidance.
As a product manager, you’re seeking to create a solution that not only meets user needs but also thrives in the market. This often involves assembling a team of experts: user designers, software engineers, data engineers, and data scientists. But in the early stages, you're often the de facto expert.
Developing an AI product is a multifaceted challenge:
Building trust in the data used to train the model is paramount. Even with off-the-shelf models, rigorous testing and understanding that the solution is doing what you think it’s doing is essential.
And let's not forget the importance of understanding the target users, their problems, and the broader context. User-centred design helps you navigate not only the user needs but also consider the market readiness.
This is where the shift from user-centred to human-centred design becomes crucial. How do you bring in meaningful engagement from the wider system where the product will sit?
In my next post, I'll delve deeper into these considerations, and how I explored balancing various perspectives and making informed decisions in the complex world of AI product development.
In the meantime, feel free to reach out: I'd love to hear your thoughts and experiences in developing AI healthtech products!
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