Collaborating with Data Science—Aligning AI with Business Goals
Building AI-powered products isn’t just about understanding the technology—it’s about bridging the gap between product goals and technical execution. To get AI right, product leaders need to collaborate effectively with data scientists. It sounds simple, but in practice, these two groups often speak different languages, and misalignment can lead to failed AI projects or wasted resources.
In this article, we’ll explore how product leaders can work hand-in-hand with data scientists to ensure AI initiatives are aligned with business goals, using real-world examples and actionable strategies.
Build a Common Language Between Teams
Imagine this: you, as the product leader, walk into a meeting with your data science team and throw around terms like "user engagement" and "customer retention." Meanwhile, the data scientists start talking about "algorithm accuracy" and "model precision." Both teams are working toward the same goal, but they’re speaking different languages.
One key to successful AI collaboration is creating a common language. At Spotify, for example, product managers and data scientists had to learn how to communicate when developing the recommendation engine behind Discover Weekly. The product team wanted to increase user engagement, while the data team needed to measure how well the algorithm was predicting what users would enjoy.
By focusing on shared goals—getting users to discover more music—they were able to merge these priorities. Product leaders focused on defining what “success” looked like for the user, while the data team figured out the best technical approach to deliver it.
Actionable Tip: During your next meeting with the data team, make sure you're framing conversations in terms of shared outcomes. Instead of focusing solely on technical metrics like accuracy or precision, talk about how these metrics tie back to your business goals. Ask questions like, “How does this model help us improve customer retention or enhance the user experience?”
Define the Problem, Not the Solution
One of the most common pitfalls in AI projects is when product leaders try to dictate the solution to the data team. The truth is, as a product leader, your job is to define the business problem—not how it should be solved. Let the data experts figure out the technical execution.
A great example of this approach comes from Netflix. When they set out to improve their recommendation engine, the product team didn’t tell data scientists what algorithm to use or how to approach the problem. Instead, they defined the problem: how do we help users discover shows and movies they’ll love? They then gave the data science team the freedom to explore different models and methods. This led to the creation of their famously successful recommendation system.
Actionable Tip: When working with your data science team, clearly define the business problem. Whether it’s increasing user engagement, driving sales, or improving customer support, focus on the “why” behind the initiative. Trust the data team to experiment and find the best technical path forward.
Establish Metrics for Success
Metrics are critical when it comes to aligning product and data teams. Without clear, agreed-upon measures of success, it’s easy to get lost in the technical weeds or focus on the wrong outcomes.
Take Facebook’s News Feed as an example. Early in its development, the product and data teams struggled to agree on what success should look like. Some teams wanted to prioritize engagement (likes, comments, shares), while others thought time spent on the platform was a better indicator of success. Ultimately, they agreed that a combination of engagement metrics and user satisfaction surveys would provide a fuller picture of how successful the feed was at keeping users engaged without causing burnout.
Actionable Tip: Sit down with your data team and agree on clear success metrics that tie back to business goals. Are you looking for increased customer lifetime value? Reduced churn? Improved conversion rates? By defining these metrics up front, you’ll avoid misalignment later on.
Iterate with Flexibility
AI isn’t a one-and-done kind of project. Models need to be trained, tested, and continuously improved. This is where a culture of iteration and flexibility becomes essential. What works today might need tweaking tomorrow, and product leaders need to understand that AI models evolve over time.
For example, when Amazon first introduced its recommendation system, it wasn’t perfect right away. The company worked with data scientists to continually refine the algorithm, testing different models and collecting feedback on its performance. By iterating and adjusting based on real-world data, Amazon was able to build a recommendation engine that today drives a significant portion of its revenue.
Actionable Tip: Encourage a culture of experimentation. Let the data science team test different models and be prepared to pivot if something isn’t working as expected. Building AI is often a process of trial and error, so the more flexible and iterative your approach, the better.
Conclusion: Aligning AI with Business Goals Through Collaboration
AI projects succeed when product leaders and data scientists work together with a shared understanding of the business problem, clear success metrics, and a culture of collaboration. It’s not about product leaders dictating the solution or data scientists working in a silo—it’s about finding a common language and building AI solutions that align with real business goals.
Start by focusing on the problem you want to solve and build from there. Define metrics that matter to both the business and the user and remain flexible as models evolve and improve. By fostering this kind of collaborative environment, you’ll not only align AI with your business goals but also create more impactful, user-centered products.
Next up, we’ll explore how to create AI roadmaps that address the unique challenges of AI lifecycle management and ensure long-term success. Stay tuned!
