AI/ML Product Tips
One of the challenges of shipping AI-enabled experiences is the shift of product management practices. As this is a fairly new domain, most product teams are not well-equipped to handle the end-to-end delivery of an AI/ML project. This leads to a lot of frustrations, quality issues, and worse, decreasing stakeholder’s buy-ins in the organization. Hence, the followings share some of the key tips distilled from my own experiences building and shipping AI/ML projects.
Assess if it’s worth investing AI/ML
One of the biggest mistakes is overestimating the potential business return an AI/ML project can bring without prior validations to be certain of its impact. While there is no single rule to approach this, most AI/ML projects generally fall into the following buckets:
- drive business revenue (e.g. e-commerce product recommendations)
- move key metrics to drive growth (e.g. LinkedIn’s people recommendations driving virality);
- increase the stickiness of product (e.g. Gmail’s Smart Compose, LinkedIn’s Smart Reply)
- build a moat for the company (e.g. Gmail’s Smart features);
- strategic alignment
To ensure it’s a problem worth investing months of engineering effort, it’s important to consider the following factors:
