7 Elements of AI Product Strategy
A playbook for building ML powered products, teams and businesses
Building and selling machine learning (ML) products is hard. The underlying technology keeps evolving, requiring organizations to constantly be on their toes … and the rulebook on what makes for successful and profitable products is still being written, making for uncertain outcomes.
Over 5+ years of working with machine learning enabled products, we at Semantics3 have had to deal with many challenges stemming from the nascency of the industry. In this article, I’ve drawn from these experiences to distill a set of considerations for proactively anticipating and dealing with these vagaries. Being deliberate about these considerations has helped us align our products for long-term success.
The ideas below cover general trends; there will, of course, always be exceptions to norms.
#1 — What is the role of AI/ML in your product?
Quadrant A (Top Left): Standalone Black Box Products with ML Models at the Core
You’re selling a black box ML model, where customers determine how best to utilize the intelligence on offer (e.g. transcription services like Amazon Transcribe or Google Speech-To-Text).
If your product falls into this category, carefully think through the quirks of all the inputs that customers may send your way.
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