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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:
- expected return to the business (e.g. can you tie this to business-level metrics)
- problem statements (e.g. are you solving a pain point or offering a delighter)
- the opportunity cost of pursuing this (e.g what other user or customer problem you’re giving up by committing your time to this)
- engineering costs (e.g. how long this will take, what are some of the technical challenges, GPU cost)
- risks (e.g. is this feasible, data privacy, legal compliance)
These will help you to think about whether this is something worth investing months of effort into. As AI/ML projects generally have longer timelines than other software projects, make sure you do this with rigor so that your team gets to work on what matters most to the business.
