Empowering AI Product Teams in the ever-evolving industry
How to empower AI Product Managers to create AI-Powered products
Starting with a simple example of how AI-powered products transformed aspects of our lives in different industries - In the heart of Silicon Valley, a small startup faced a daunting challenge: transforming a traditional health monitoring app into an AI-powered predictive health advisor. This journey began with a simple realization - the potential to revolutionize patient care by leveraging machine learning to predict health issues before they become critical.
But how this transformation to AI-powered products affected Product teams and what changed on our traditional product development cycle when we were introduced to AI driven products?
Product Roles play a pivotal role in driving innovation, particularly when it comes to creating cutting-edge AI-driven products. Our focus should be on using artificial intelligence, deep learning, or machine learning to: enhance, improve, create, and shape products. “AI product role” doesn’t directly imply AI as the central technology in an application, service or product, however by using AI in your features you can improve the performance of your application, service or product. To empower them for success in this challenging landscape, it is essential to provide those roles with the knowledge and skills to navigate the complexities of building, mastering, and crafting next-generation solutions including technical skills.
The tech industry requires a holistic approach that covers the entire product development lifecycle, from ideation to user experience refinement, where in more details it will be explained on three critical areas:
How to create/build AI-driven Products from concept to execution
Building an AI product or a product that uses AI on its features, like any other, is to clarify what is it that we are building. Instead of treating this similar to our regular software products we need a separate guide when dealing with AI, and it comes with its own challenge that is fast-evolving technology.
As with any other product or feature, product teams should start with a well-defined problem or opportunity that the AI product aims to address, in our case we should define our product’s value proposition without tying it with AI. Instead, ensure that the benefit your solution provides to the user compared to other competing ones makes a compelling case for AI.
Clearly outline the objectives of the AI product.
Choose the right AI model and algorithms based on the nature of the problem.
Gather relevant data for training the AI model.
Create prototypes to test the feasibility of the AI model.
Design an intuitive UI (user interface) that seamlessly integrates AI features.
Conduct thorough testing of the AI model and overall product functionality.
Build a system for continuous improvement that updates the AI model based on evolving requirements.
Start learning AI Models, fundamentals, strategies and metrics: In the guide before we mentioned including AI models on the building processes, but why is it important for product teams to upgrade technical skills and what exactly they should be focused on?
Product Teams should develop a strong understanding of various AI algorithms and ML:
2.1 Supervised learning & Unsupervised learning
Both supervised and unsupervised learning are a subcategory of machine learning. Supervised machine learning relies on labeled input and output training data.
Classification and regression are part of supervised learning:
Classification: A classification problem’s output variable is an assigned category, for example some “oranges” in a basket containing different types of fruit.
Regression: A regression problem’s output is a continuous real value, for example optimized product prices based on past sales data.
Unsupervised Learning - If labeled data isn’t available, product teams must feed the learning algorithm unlabeled data. This process is called unsupervised learning, and the resulting functions identify the latent structures within the unlabeled data.
Clustering: The algorithm finds patterns in unlabeled and uncategorized data. For example, the algorithm might identify a group of customers who purchase oranges and share demographic features.
Association: The algorithm creates relationships between variables in large databases by establishing association rules. For example, the algorithm could uncover what other products are popular with customers who purchase oranges.
2.2 AI Model Metrics
Product Managers should start to be familiarized with key metrics of AI Models such as accuracy, precision, recall, F1 score, and ROC-AUC.
Accuracy: This metric measures the proportion of correctly classified instances out of the total instances evaluated. It is calculated as the ratio of the number of correctly predicted instances to the total number of instances.
Precision: Precision focuses on the relevance of the model's predictions. It measures the proportion of true positive predictions (correctly predicted positives) out of all positive predictions made by the model.
Recall (Sensitivity): Recall measures the ability of the model to correctly identify positive instances from all actual positive instances. It is also called sensitivity or true positive rate (TPR).
F1 Score: The F1 score is the harmonic mean of precision and recall. It provides a balance between precision and recall, especially when there is an imbalance between the classes.
ROC-AUC (Receiver Operating Characteristic - Area Under the Curve): ROC-AUC is a performance measurement for binary classification problems and it represents the area under the ROC curve, which plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. An AUC value closer to 1 indicates a better model performance.
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These mentioned metrics provide different insights into the performance of a machine learning model, and the choice of which metric to prioritize depends on the specific requirements and characteristics of the problem being addressed.
Products in real life that use AI models and metrics on their features:
Both Amazon and Netflix include User Engagement Metrics on their platforms like Click-Through Rate (CTR) that measures the percentage of users who click on recommended items. Dwell Time that measures the amount of time users spend interacting with recommended items.
Companies like Amazon and Netflix also use A/B testing and other experimental methods to evaluate the impact of recommendation algorithms on key performance indicators (KPIs) such as customer retention, average order value, and overall revenue. By continually monitoring and optimizing these metrics, they can enhance the effectiveness of their recommendation systems and ultimately improve the user experience.
Crafting Next-Generation User Experiences and exploring AI UX Spectrum
From chatbots and virtual assistants that provide real-time assistance to predictive algorithms that adapt to user behavior, the possibilities within the AI UX spectrum are vast and transformative. Crafting next-generation user experiences involves an intricate process that integrates advanced technologies with user-centric design principles, ultimately shaping what is known as the AI UX spectrum. This spectrum represents a dynamic space where AI and user experience intersect, offering a wide array of tools and methodologies to enhance digital interactions.
It's about leveraging AI's capabilities to understand user behaviors, preferences, and needs, and then tailoring the UX (user experience) accordingly.
Some tips for product teams that enhance the products’ shape on the AI Spectrum:
Start to uncover AI UX frameworks, including design thinking and the Double Diamond model.
Implement personalization features to enhance user engagement and satisfaction.
Understand the spectrum of AI UX, from assistive and adaptive AI to fully autonomous systems.
Integrate AI-driven enhancements such as predictive analytics, conversational interfaces, and computer vision.
Strive for a balance between innovation and usability in AI-powered features.
Map the entire user journey, identifying touchpoints where AI can add value.
Prioritize ethical design by ensuring transparency, fairness, and inclusivity in AI-driven features.
Key Takeaways
The interdependent relationship between AI and product managers is shaping the future of digital experiences by navigating the intricacies of integrating AI into the core fabric of product management.
As Product Teams not only should we adapt in the AI revolution but we should be orchestrating it.
It is important to understand the technical sides of AI products and make your product team collaborate with AI expert roles.
Learn the AI fundamentals, AI Models and AI Metrics.
Manage the risks to avoid pitfalls - Incorrect ML problem framing, not having good data to train your models, and iterating too slowly will lead us to pitfalls.
Stay updated on the latest advancements in AI research and applications.
With the necessary skills and knowledge, PMs can be positioned as key drivers of innovation in the rapidly advancing field of AI technology.