Involvement and impact of AI in Product management: interview with Rutger de Wijs

Product Management Consultant Rutger de Wijs shares his view on why and how AI can be leveraged by Product Managers to increase the value of their products. The full transcript of the video can be found below the video. Feel free to explore the content of the interview in the format that best suits your preferences.

Introduction and General Insights

To kick things off, could you provide a brief overview of your background and how you became involved in the intersection of AI and product management?
At the beginning of my career (in the 2010s), I worked at an advertising tech startup as a BI Manager. There, I learned how SaaS platform product is built in an agile way and how to provide relevant insights and create data stories using SQL, Tableau, and a bit of R. Thanks to this, I was able to peek through the window about how our central media buying algorithm could be improved using Data Science.

From your perspective, how has the landscape of product management evolved with the integration of AI technologies in recent years?
Well, in very recent years, AI has come to mean Generative AI to many people including Product people. Although Gen AI can be a very useful tool, mostly from a role perspective, the world of AI is much bigger, and, I think, not yet fully exploited by the world of Product Management.

Industry Trends and Observations

As someone deeply immersed in the field, what developments do you observe in
how do businesses try to leverage AI to enhance their product strategies?
I see that almost every larger business has invested in Big Data, Advanced Analytics, and Data Science. But just like with the adoption of consumer products, there are early adopters on the one hand and Laggards on the other, and there are companies that are already creating value from data, such as Spotify, as a nice example. But I’ve read many reports that say most executives still struggle to create value from their data investments.
As a Product Management Consultant involved in Transformations, I see more and more companies putting next steps for leveraging data and AI on their roadmaps. However, I also see that there is still a gap between on the one hand data teams trying to push their work into the organization and on the business-side not pulling this amazing potential in.

Interesting… Are there specific industries or sectors where the use of AI is particularly noteworthy?
Every industry is benefitting from leveraging AI to some degree. Some examples with a lot of impact are the financial industry with fraud detection, the automotive industry with self-driving efforts and (social) media sector with personalized feeds.

The role of AI in Product Management

How do you see the role of AI evolving within the broader context of product management, and what opportunities does it present for professionals in the field?
I see AI becoming an integral part of Product Management as it is a powerful ingredient for making products more valuable for customers and the business. Better understanding AI and knowing how to use it as a tool can set Product people apart from those that don’t know how to use it.

Can you share examples of successful AI integration in product management that have caught your attention?
Earlier I mentioned Spotify. I really like how their music streaming product has evolved by infusing more and more intelligence into it. All across their product, they leverage data and AI to improve the user experience but also provide new features that make the app even more compelling to use, such as their ‘Discover Weekly’ playlist of thirty songs users have never heard before but would probably like based on insights from the entire userbase.

Challenges and opportunities

Transitioning to challenges, what hurdles do product people commonly face when incorporating AI into their products, and how can these be overcome?
A couple challenges exist, I think. One of the more essential ones is that many Product people do not consider AI as an important tool in their Product Management toolbox. The world of Data Science is too far removed from their regular focus. Another challenge is that even if you know there is such as tool as AI, many Product people have not received a manual for how to properly operate this tool.

Synergies between Product Managers and Technical teams

Let’s zoom in on that: the collaboration with Data Science teams. How should Product people collaborate with them to increase value in their products?
Well, I’d like to stress the word "collaborate" because creating value from Data Science is a joint effort. In most companies, Data Scientists are in a centralized team and are not part of the product team. In that case, as a PO you need to actively engage the Data Science team and include them from the beginning, which is ideation.
Moreover, it’s important to know that Data Science teams and Products teams have a different rhythm. Data Science is about continuous experimentation. Product development, on the other hand, is more about cutting complexity into manageable chunks. As a Product Manager, you should actively engage with the Data Science team and pull their work into your product iterations.

What way-of-working practices should we pursue to maximize the collaboration effectiveness?
At Xebia, we developed a framework for the logical chain from data to value. The components of the flow are Data > Predictions > Insights > Optimizations > Actions > Measurement > Value. In order to create value from data the PM should start at the end, from value, and together with the Data Science team work backward all the way to data. In this way the right type of data and data manupulation can be turned into value. As such, this framework functions as a guardrail for the experimental nature of Data Science.

Training insights:

Shifting towards educational initiatives, what motivated you to develop the "AI Powered Product Management” training?
In my work as Product Management consultant, I meet a lot of Product Managers that are not yet aware of or are intimidated by the tool of AI. I developed this training as part of the Xebia Academy to help such Product Managers add this powerful tool to their toolbox. In fact, I think learning how to work with this tool is an essential part of the Product Manager’s learning journey.

What components have you included in it?
The training consists of mainly two parts, first I explain the concept of AI and how all the buzzwords and jargon (such as the difference between Machine Learning, Neural Networks and Deep Learning) relate to each other and to creating value. The second part is about the process of generating value. The goal here is to make the AI tool ready for use the next day on their job.

Who is the envisioned audience for this training course?
So far I’ve been using the term Product people, because I think many roles related to Product Management or Development can benefit from this course. Besides Product Managers and POs, Product Leaders, such as HoP or CPO that can upgrade their toolbox, other roles connected to the value creation process, such as SM, Agile Coaches, RTEs can also benefit to help teams expand their toolbox.

How does the course balance providing theoretical knowledge and practical, hands-on experience for participants?
I myself enjoy attending trainings that provide both. On the one hand, understand the theory but then also get a chance to play with the theory on some practical examples or even your own use cases with the help of the trainer. This is also how I set up my training.

As participants complete the course, what transformations or insights do you hope they take away to enhance their day-to-day roles as product managers?
I hope they will have become comfortable with the AI topic and are enthusiastic to lead the collaboration with Data Science teams to enhance the value of their products using some proven approaches that they will have learned about.