Product managers often wonder how to approach AI and machine learning. Starting can feel overwhelming, especially for teams new to the field, as building ML solutions requires unique skills and processes.
The key is to start small with a low-risk, manageable project focused on learning. This approach builds foundational capabilities and sets the stage for scaling AI initiatives without disrupting operations.
In this post, I’ll outline my recommendations for starting with ML/AI effectively.
This post was written by Ben Dickson, a seasoned engineer, tech blogger, and mentor at our AI/ML Simulator for Product Managers.
Start with low-hanging fruit
The best approach is to start with “low-hanging fruit”—projects that are relatively simple to implement but offer clear and measurable business value.
Low-hanging fruit in AI/ML projects typically share these characteristics:
— Minimal disruption to existing workflows. These projects can be integrated seamlessly with your current operations. For example, you can use ML to automate one stage of a multi-step task that is usually done manually.
— Clear success metrics, making it easy to measure results from the outset. For example, it can be time saved per task or the amount of work completed in a specific time.
— A quick implementation timeline, allowing you to see value without long delays. Thanks to generative AI tools, the implementation timeline for many prototypes and proof-of-concept projects is shrinking.
Selection criteria for your first AI project
When selecting your first AI/ML project, focus on three key factors: data availability, process characteristics, and impact.
Data availability
Start with tasks where you already have a good amount of data, preferably clean and structured in a data warehouse. If not, look for areas where you have a lot of unstructured documents that hold a lot of value. In many cases, you can get a lot of value from raw documents or get them ready with minimal annotation.
If you have no data available, look for tasks where data collection or creation is easy and low-cost and can preferably be carried out by a small team. Ensure that the data you plan to use complies with privacy and regulatory standards.
Process characteristics
Look for processes that involve a lot of manual effort and are too complex for simple rule-based automation. If it’s a task that humans currently find repetitive but hard to codify, that’s a great sign that it could benefit from AI. Tasks that have clear input-output relationships with predictable patterns are ideal for machine learning.
Also try to choose a project that has minimal dependencies on other systems or teams, so you can control the process and deliver results quickly. To reduce the barriers to entry, you can choose tasks that can be improved without full automation. For example, even if the ML model can improve the speed of a task by 10–15%, it can have great value for your organization.
Impact
Ideally, your project should deliver a quantifiable ROI, through cost savings or time efficiency. But the main point of your first project should be to get comfortable with the process of building ML products. Adopting machine learning requires many support functions such as setting up data pipelines and model serving platforms, performance monitoring and model retraining. A small project will help you gradually experiment and build up those pieces before undertaking large-scale projects.
