Is AI a silver bullet?
Most of the time, it's not.
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Previously, we laid the groundwork for AI product strategy.
Once that's in place, it’s time to get real: when does AI make sense to implement?
The truth is, many proposed “AI use cases” miss the mark.
As AI Product Managers, our job isn't just about AI; it's about solving real-world problems at the intersection of customer and business value.
Here's a reality check: most of the use cases that land on my desk aren't suitable for AI.
That doesn't mean they're worthless, just that AI isn't the right solution.
PMs must understand AI's capabilities, risks, and limitations, rather than viewing it as a silver bullet.
Beyond the Hype
Rule-based, traditional ML, and hybrid solutions still deliver immense value.
It's about choosing the right approach given the use case.
AI vs. Traditional Software
Traditional
Predictable, deterministic, and reliable for consistent inputs and outputs (think tax calculators).
Maintenance requires bug fixes and feature enhancements, and the development lifecycle is well-defined.
AI
Data-driven, probabilistic, and requires continuous refinement.
AI can adapt to new data patterns but it's also prone to data and model drift; AI demands rigorous monitoring and necessitates a deep understanding of the data landscape.
Development starts with data acquisition and requires preprocessing, model training, and evals.
Human feedback loops arise out of necessity.
Data quality, privacy, and fairness are paramount in responsible AI solutions.
As PMs, we need to remove AI from our job titles and focus on solving customer pain points that bring value to the business.
Stop chasing the hype and start delivering value.
“What have you done for me lately?”
