Product School

5 Essential Skills for AI Product Managers

Charu Sareen

Author: Charu Sareen

October 31, 2023 - 8 min read

Updated: January 24, 2024 - 8 min read

AI is everywhere. An integral part of our daily lives, we encounter it in recommendation systems on platforms like Netflix, search engines like Google, and voice assistants like Amazon Alexa. While AI has significantly improved the quality of our experiences across various products and services, building AI products presents unique challenges for Product Managers. This makes understanding AI and developing skills for AI Product Management increasingly vital.

In this post, you’ll learn five essential skills for AI Product Managers to harness the potential of AI and create unique and valuable offerings in the product space as outlined by Amazon Product Lead, Charu Sareen.

Editorial note: This post is based on a talk on 5 Key AI and ML Product Management Skills by Amazon Product Lead, Charu Sareen, and contains additional insights and examples from the Product School team. You can watch the webinar in full above.

1. Defining the problem and business opportunity

In the process of solving a business problem with Artificial Intelligence (AI) or Machine Learning (ML), the first step is to determine whether an ML or AI algorithm is the right approach to address the problem

Machine learning algorithms excel at uncovering complex relationships and hidden patterns in datasets that consist of many interdependent variables. However, it's vital to acknowledge that machine learning and AI is not a universal solution for every problem. 

Some cases can be resolved effectively without resorting to ML techniques, particularly when the target value can be determined using simple rules, computations, or predetermined steps that don’t require data-driven learning.

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If you evaluate the specific problem at hand and determine it makes sense to use machine learning, the next consideration is the desired level of accuracy. All AI models strive for high accuracy, but what constitutes "high accuracy" can vary depending on the context. 

For example, consider the task of classifying emails as spam or not spam. In this scenario, you must weigh the importance of accurately tagging each spam email against the risk of incorrectly labeling a legitimate email as spam. These objectives can sometimes be in conflict, and your decision should align with the overarching business goals.

Therefore, you must carefully consider the trade-offs between accuracy and associated costs as part of your role as an AI Product Manager.

2. Leveraging the right data

When solving business problems with AI and machine learning, we encounter various challenges. The first challenge revolves around data accessibility. Do you have access to the data you need? 

Sometimes, the problem may not be the absence of data but rather its inaccessibility. When dealing with large companies, data availability can pose a significant hurdle. You might have ample data for customer information, but accessibility issues could prevent your model from utilizing it effectively.

Another critical consideration is whether you should use the available data at all. You might possess sensitive customer information, including details about their residences and bank accounts. The question is, should you leverage this data? A good rule of thumb is to ask yourself whether you'd want to be in the news for using this data. If the answer is no, it's advisable not to use that information. 

While your intentions may be to enhance the customer experience, customers may not share the same sentiment, and respecting user privacy is paramount. Ensuring that the entire data pipeline is secure is essential to prevent any data compromises.

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Data relevance is another challenge. Relevant and up-to-date data is crucial for accurate predictions, but not all data at your disposal may be pertinent to the specific problem you're attempting to solve. 

Addressing bias in data is another one of the critical skills for AI Product Managers. Bias occurs when machine learning algorithms produce anomalous outputs due to assumptions made during algorithmic development or biases in the training data. 

Consider a scenario where you build a model to prioritize resumés for interview selection. While this problem seems suitable for machine learning, you may have a biased dataset, as it only includes data from candidates who applied and progressed through the process. This omission introduces bias because you lack data from potentially qualified individuals who never applied. Ensuring a balanced dataset is essential to mitigate bias and improve model accuracy.

3. Gathering feedback effectively

In the context of machine learning, gathering customer feedback becomes even more crucial due to the probabilistic nature of machine learning systems. Probabilistic systems inherently require more data to enhance the quality of their outputs over time. The challenge lies in how to effectively collect this valuable data and create customer feedback loops.

One effective approach to gathering feedback is by embedding a feedback mechanism directly into the core customer workflow. To illustrate this, let's consider the example of Uber. Uber prompts its customers for feedback every time they log into the app. By doing so, Uber can accumulate a wealth of valuable customer signals. For instance, if a customer expresses dissatisfaction with the number of pickups, Uber can use this feedback to optimize its pickup algorithm. This approach exemplifies how incorporating feedback loops into the customer experience can lead to continuous improvement and enhancement of the user experience.

4. Leveraging AI to enhance user experience

When it comes to user experience in product management, we can categorize it into conscious and unconscious elements.

Conscious user experience refers to situations where users actively understand and interact with the interface in front of them. For example, users might be asked to select from a dropdown menu, click one of two buttons, or choose between different color options. In these cases, users consciously make decisions based on the provided interfaces.

However, there's another dimension to user experience, which is unconscious. In unconscious user experiences, users may make decisions without consciously realizing it. An example of this is when a specific option is highlighted more prominently than others, and users unconsciously prefer that option. Many AI systems influence user experiences in unconscious ways.

A practical illustration of this concept can be seen in search engines. Consider two users searching for the same word, "shoes." While one user is shown men's shoes, another is shown women's shoes. This difference is due to the AI tailoring the results based on the user's gender information, creating an unconscious experience change. 

As a product manager, you're leveraging user information to enhance their experience without requiring explicit input. However, it's crucial to consider what information you use to improve the customer experience and be mindful of potential privacy concerns and ethical considerations.

Another key aspect of designing user experiences with AI is to be opinionated. There can be multiple correct answers to a single user query. For instance, if a user searches for "couch," should the results display home decor ideas or different couch brands? The choice depends on the desired user action. If you're a retailer like Bed Bath & Beyond, you might prefer showing home decor ideas to encourage users to explore various products. In contrast, a search engine like Google may prioritize showing different couch brands to deliver more precise results.

To guide user actions effectively, you must design your interface to nudge users in the direction you want them to take. This involves simplifying the user interface and ensuring there are at most one or two clear actions for users to follow. By being opinionated and aligning the interface with your desired outcomes, you can enhance the user experience and achieve your product's objectives.

5. Effective collaboration with cross-functional teams

As a PM, you’re already accustomed to working with multiple stakeholders, but when dealing with AI products, this complexity increases significantly. In a traditional product management lifecycle, you collaborate with engineering, UX, customer success, marketing, and possibly sales teams to define requirements and iterate to build and ship the product.

However, when you're working on AI products, numerous new teams come into play. These teams may include data scientists, data engineers, machine learning scientists, machine learning engineers, applied scientists, and business intelligence professionals. This influx of stakeholders adds complexity to the product development process, as coordination and communication demands increase. The coordination time with these teams rises significantly, impacting the time required for product iterations.

In AI product development, if you receive feedback that necessitates changes to the AI models, the process can reset. This involves revisiting data requirements, addressing missing data, model building by the science team, and collaborating again with engineering for deployment. Therefore, more stakeholders, increased complexity, and longer iteration cycles are characteristic of AI product development.

The roles and teams involved may vary depending on the company's size, but the fundamental complexity remains. As AI continues to advance and specialize, these roles are likely to become even more specialized, making cross-functional communication skills for AI Product Managers even more important.

Updated: January 24, 2024

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