Determining the value of an AI product isn’t easy to determine off the bat. Especially if you’re a team just taking up a product focus. You’ve gotten used to the idea of building - but the questions keeps popping up:
"Where should I begin?"
"How do I approach a broad and complex problem?"
“How can we ensure this innovation remains relevant in the future?"
If you’re asking these, you’re not alone. Many businesses run into these questions at various levels from leadership to the teams building an AI product(s). It hints at a larger difficulty we face: how to increase the value of the AI products we build.
For non-product managers, this can be a uphill struggle - we’re so used to building that seeing it as product can be hard. Product focus is critical if we want to create models that have high ROI.
Product mindset is absolutely essential in transforming a technically proficient machine learning model into a valuable business asset. An accurate ML model, on its own, won't cut it; it must also possess user-friendly features that make its insights not just understandable, but actionable for the business.
AI products must do the following three to be effective:
Find the Essence Behind AI Problems
Defining the Problem Space
Future Proofing Your AI Product
Lets describe each in more detail.
1. Find the Essence Behind AI Problems (and products)
When it comes to tackling problems with AI products, it's not just about spotting pain points. We need to understand them, determine the root cause, and know if we can address it with the resources on hand.
There’s the process of scoping and discovering what they’ll actually be used for. This isn’t easy, since most teams and product managers are building the boat as they’re driving it.
It’s easy to believe that we can build the model out of the box, with a wish list of product features and data we have. This is an approach I see far too often, since the user can be cut out of the picture. We build first, and ask questions later.
We need to start with the user first, then capture the product features we’d like to AI model to enable. This lets us source the data to build the actual model and design a product that addresses the multiple solutions.
Overall, there are three steps to uncovering the ‘why’ behind AI products:
find the user.
capture product features
source or augment the data.
Let’s illustrate this with an example. Imagine you're a data scientist working on a ride-sharing app, and your mission is to figure out how to get more users to use the carpooling feature.
Here's how you can approach it, using the framework above:
Step One: Identify and Understand the User
For the question of boosting carpooling usage, the key focus is on the users. You need to pinpoint exactly who these users are.
You might:
Dive deep into their behavior within the app.
Explore what these users really need when it comes to ride-sharing and carpooling.
In this context, users could be folks looking for that perfect balance between cost and convenience, choosing carpooling as the Goldilocks option between public transit and solo rides.
Step Two: Spot the Key Features
Features are the bits of data your model learns from. They form the essential hypothesis behind a model. So, you've got to identify the features that matter in understanding user behavior:
Look at core features like price sensitivity, flexibility with time, and preferred travel hours.
Don't forget about non-core features like age, gender, past ride history, and the places users frequent.
These features help build a solid data framework that your ML model can use to understand what users really want.
Step Three: Get More Data and Analyze
To build a strong ML model and useful AI product, you need plenty of data. The key here is more isn’t useful - its the quality of data that we use, that fits the need.
Keep an eye on user behaviors and interactions to gather data.
When data is scarce, think about creating synthetic data or even running user surveys for insights.
Use the data to segment users based on their behavior, which can give you different perspectives on their needs and preferences.
By turning the problem into a data-driven question, it becomes something like: "How can we entice users, especially those who are super conscious about prices, to try out or consistently use the carpool feature?"
Transforming the problem like this helps you get to the heart of it, making the challenge much more manageable. With ML models trained on this data, you can develop strategies that not only answer the original question but do so based on solid user-centric data and predictive accuracy.
If we cannot identify or understand the user, the scope of our product may be too broad. Or our opportunity discovery processes may need revision. It ends up with possible solutions, but no product. This is a question you’ll have to ask yourself not once, but several times.
In a nutshell, this approach uncovers the essence of the problem, streamlines solutions, and ensures that your strategies are backed by data, making it easier to tackle those complex, user-oriented challenges in ML product development.
2. Define the User Problem Space
But all the definition in the world, it really doesn’t matter unless you understand the users. Ambiguity arises from broad challenges, since users (and us) have such a broad range of problems to solve. Building a ML model alone doesn’t make an AI product.
So, what to do?
Transforming machine learning (ML) models into AI products involves a critical step: refining broad challenges into specific, data-driven issues.
We need to define the user problem space. We can do this by breaking down complex challenges into smaller manageable tasks and understand how these tasks fit into existing processes.
It means thinking in terms of user flows, use cases, decision points and journeys. An effective solution should be tailored for each user’s needs while being easy to implement and maintain on an ongoing basis
Let's imagine a scenario that asks:
"How can we boost watch time on our video streaming platform?"
It's a common but vague challenge, much like scaling user growth or meeting Key Performance Indicators (KPIs). Tackling such issues without a clear plan can drain resources and hinder product development.
Here's a systematic approach:
Data Dive: Start by delving into user data and platform interactions. Identify key factors that influence watch time.
Defining the AI Challenge: Apply thinking to break down the vague problem into precise, data-backed queries:
What content engages teenage users the most?
How can we keep adults watching a series after the first episode?
...and so on.
Model Crafting & Training: Develop and train ML models using user data to predict behaviors and preferences. Tailor models to different user segments to understand diverse motivations and actions.
Deployment & Ongoing Monitoring: Once the models are live, closely monitor their real-world performance. Analyze how accurately they predict and impact user behavior. Continuous adaptation is essential.
So, by turning our vague problems into clear, actionable ones, we reduce the chance we build a AI solution that becomes development time sink and a cost center. Our solutions become direct, implementable, and, importantly, verifiable against real-world outcomes.
By converting vague problems into clear, actionable tasks, we reduce the risk of creating AI solutions that drain development time and resources. Our solutions become direct, implementable, and verifiable against real-world results. And a foundation to define KPIs from.
Best of all? We can iterate, once we have those KPIs. Iteration and improvement are the core of all great products. And all great products have a benchmark to improve upon.
This approach ensures a confident journey through the complexities of AI product development, always centered on data-driven and user-focused practices. It's a practical method for navigating the frequent vagueness of the AI product landscape.
3. Future Proofing Your AI Product
The greatest products value lies in their timelessness.
Every AI product management invariably devises solutions when issues or needs emerge. Skillful AI product management resides in the ability and efficiency to genuinely resolve those issues.
Its a key factor if you are looking at an AI product. This is critical when considering an AI product. The aim is not just to address immediate use cases as they come, but with practice, to weave frameworks that cater to multifaceted scenarios.
This mindset takes AI product managers and lead data scientists out of a reactive tactical mode to a strategic one. We’re not just building a AI product that solves the immediate need of the business.
Just as the first iPhone laid the foundation for the evolution of smartphones, evolving with each generation, we are building AI products that provide a canvas for future innovation, ever refining and improving upon their initial promise.
Being able to be ahead and future proof relies on key points:
Swift Problem Identification: The capacity to quickly discern issues, followed by a meticulous exploration into their essence, forms a critical component in solution development. Implementing ML models allows for a rapid, data-driven examination of extensive datasets, offering insights into trends and potentially highlighting problem areas with enhanced accuracy.
Delving into the Problem’s Core: Machine Learning extends its functionality beyond mere identification, providing a deeper understanding of user behaviors and patterns. This, in turn, assists in revealing the fundamental reasons behind noticeable issues and facilitating a more nuanced approach to solution formulation.
Enhancing Solution Development: While traditional product managers might occasionally struggle with targeting effective solutions, those employing ML models can ensure their strategies are substantiated by data, user-centric, and notably precise.
Building a Sustainable Competitive Edge: Continually adopting an ML-oriented approach not only amplifies problem-solving capabilities but also creates a distinct, challenging-to-replicate competitive advantage.
Incorporating differentiation, advantage, and a competitive edge, ML models stand out as a central competitive element for data science teams.
It represents not merely an asset but a durable, forward-looking toolkit that potentially propels career trajectories and maintains relevance amidst changing technological climates.
In a broader context, deploying ML models transcends current problem-solving, infusing the product management journey with a future-ready, adept, and continually pertinent problem-solving capability. This ensures that the strategies and solutions developed are not only relevant today but remain robust and applicable in future scenarios.
Final Thoughts
This of course, is all just a rough outline of how to add value. It goes deeper than this in many cases and can vary by industry or even team.
But I feel that its a good starting point for new data science teams, AI product managers, and lead data scientists trying to get the most of their ML model development.
We are often building the car while we are driving it. Product mindset and frameworks help the efficiency, iteration, quality, and even innovation of what we can build.
They help set the foundation for AI strategy and provides key positions to pivot from. Which is critical as the competitive, tech, and organizational environment changes.
Use your domain knowledge, use case interviews, and opportunity discovery processes to define the problem space. So many AI initiatives and products hinge on effective problem definition, problem space, and looking to the future.
By understanding how to approach a problem you can start building a great AI-powered product.
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