From Risks to Opportunities: Exploring AI in Product Management

Posts, Artificial Intelligence, Growing with Community

By: Evelyn Chou, Growth Product Manager

Almost all technologists in the past few weeks seem to be raving about the changes at OpenAI, from a triumphant Dev Day to turbulent staff shuffling. Investors, media, founders, and hiring managers all have eyes on the future of artificial intelligence and the potential disruption this new era of machine-driven evolution could have on the workplace. For the Women In Product community, there is just as much excitement, if not more inextricable, from the buzz around OpenAI. 

Over a month ago, the community had its first AI learning series with a dream team of guest speakers: Marily Nika, Lisa Huang-North, Tina Nguyen, and Yana Welinder. The learning cohort was composed of  PMs (at all levels), from those who run data pipelines at work to enthusiasts eager to embed AI into their day-to-day. The series covered three main themes: AI use cases, risks, and analytics. As a moderator, I experienced first-hand how extensive the application could be—from the metaverse to fraud detection.

You can outsource a function, but you can’t outsource risk

Risk is the polar opposite of AI; the former feels dry and mundane, and the latter is shiny and disruptive. This was my tunnel vision before completely being wowed by Tina and Lisa’s extensive knowledge and utmost humanity in their expertise. From trendsetter AI startups to legacy businesses, inherit risks, defined as errors or omissions in any business operations, exist regardless of the AI technology. GenAI introduced new waves of risks, including data inaccuracy, cybersecurity, and intellectual property infringement, and not enough rigor is put in place to mitigate these risks. The product manager’s role is illuminated by the need to identify, assess, and mitigate risks throughout the AI product development cycle, from setting application context, building / training AI models, to output and deployment. 

Intersection between AI PMs and generalist PMs

What is the difference between a generalist vs. an AI PM? In Marily Nika’s session, we learned about the difference in solving the right problem. From day-to-day use cases such as transforming the semantic images to photos used in self-driving cars to content localization (it was fascinating to see David Beckham speaking nine different languages in his Cure Malaria campaign). And the opportunity to build and train your model has never been easier with tools like Teachable. With all the tools at our disposal, the trajectory of meaningful AI product use cases boils down to hypothesis, testing, and the agility to learn and pivot.

Accessible product data for all, no code required

Instigated by the ChatGPT API launch earlier this year, Product Analytics companies are also in a race to build and expand their features to help product managers, designers, and UX researchers to better understand the customer journeys. Yana walked us through how Kraftful, an AI Product Analytics company she founded, empowers collecting survey and user feedback data, enabling feature requirements integrated into the embedded model, and then moving into a vector database for mapping. The end-to-end connection, whether from app stores, Jira, or customer support tools like Zendesk and Gong, is going to be handy for feedback summaries and syntheses.

The AI learning series for the Women In Product community has been an eye-opening journey, delving into the realms of AI use cases, risks, and analytics. The sessions with esteemed guest speakers shed light on the intersection between AI product managers and generalist PMs, emphasizing the importance of identifying, assessing, and mitigating risks in AI product development. Furthermore, the series highlighted the accessibility of product data for all, showcasing the evolving landscape of product analytics and the empowerment it brings to product managers, designers, and UX researchers. As we navigate this new era of machine-driven evolution, the series has equipped us with valuable insights and tools to embrace the potential disruption and drive meaningful AI product use cases.

About the author

Evelyn Chou, 2023 Women In Product Speaker

Evelyn Chou

Evelyn Chou is a Growth Product Manager with over eight years of experience. Since the pandemic, she’s spoken and moderated at the Women In Product conference, forming connections with over 100 incredible female product leaders in our community! She expresses that it’s been an exhilarating journey of shared insights, empowering discussions, and a celebration of the remarkable talent that defines our Women In Product network.