Credit Suisse Labs CEO on Using AI/ML to Build Smarter Products (Part 3)

Typically, I struggle to teach basic literacy about AI/ML products. For example, people come to me saying: “I want to do this task – when is this product able to do this task?” We then tell them that the product will be able to do the task real soon, we are trying to do our best. In many verticals, what is important isn’t the machine learning – but the data itself. So, conversations around the fundamental and intrinsic value of the data are often more fruitful. 

The Role of a Good PM in AI/ML Products

Right from the beginning, the PM needs to have “nerd cred.” In other words, the product manager must understand enough to talk about the capabilities possible. In addition, they need to bring the capability-centric view to users when discussing their needs. It’s important to have product managers get involved with how one measures the performance of AI logarithms. Typically, one can measure performance in a mathematical way or in terms of its business or product objectives. The latter is more important and valuable – but also more difficult to measure.

To protect the measurement you have to do a few things. Firstly, build in your head a holistic view of how the model fits into the larger business or product system. Second, you need to understand the things that might go wrong with the data as it interacts with the model. Third, you need to talk with data scientists about the pure mathematical scoring of the model and its decisions. Fourthly, get an understanding of the product and the user consequences about those decisions.

Final Lessons and Takeaways for AI/ML

The biggest lesson, for me, is that data matters more than the algorithm. The other big lesson is understanding how to instrument early so that the process can get smarter through time. Finally, there are a few myths I would like to address concerning Machine Learning and AI. I’d like to bust the myth that it is easy. I will not bust the myth that it will change everything. However, I will bust the myth that it will change everything for the better. We need to be smart about how and where we apply this technology. 

Click here for Part 1

Click here for Part 2

About the speaker
Jacob Sisk Credit Suisse Labs, CEO Member

Jacob Sisk is the CEO of Credit Suisse Labs - leading a team responsible for leveraging new technologies to build new businesses or refresh old ones in a way that benefits the company at society-at-large. Prior to joining Credit Suisse Labs, he worked as a product executive at Capital One Payments as its lead data scientist. In addition, Jacob worked as a research scientist at Yahoo and Thomson Reuters - and served as Principal at Infoshock for more than a decade. Jacob holds an MBA from UCLA and currently lives in the San Francisco Bay Area.

Provide your rating for this post
If you liked this post, please use the buttons to the left to share it with a friend or post it on social media. Thank you!

Leave a Reply

Read more

Use Cases For AI/ML In Society

Learn about the best use cases for utilizing AI/ML tech to improve society-at-large from Credit Suisse Labs CEO Jacob Sisk.

Credit Suisse Labs CEO on Using AI/ML to Build Smarter Products

Credit Suisse Labs CEO Jacob Sisk defines AI/ML attributes - similarities/differences and how they make an impact on product development.

Use Cases For AI: Someday vs. No Way

Understand opportunities and limitations for AI solutions by evaluating how input data is organized and target product audiences

/ Register for Free

Don’t be left behind in your career. Join a growing community of over 500K Product professionals committed to building great products. Register for FREE today and get access to :

  • All eBooks
  • All Infographics
  • Product Award resources
  • Search for other members

Coming soon for members only: personalized content, engagement, and networking.