AI Product Management: Overcoming the 3 Barriers to Impact

Feb 28, 2022 · Austin, United States of America

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Machine Learning projects frequently stall out. Studies suggest up to 80% of ML projects fail to achieve business goals. For a technology that's bound to change the world, it's incredibly difficult to work with. And the stakes are getting higher. No longer is it simply okay to "do AI". Data teams are increasingly accountable for concrete business impact.

This has created an opportunity for Product Managers to introduce product practices into Data Science and Machine Learning feature development teams. The field of Data Product Management is growing rapidly and it's an exciting, high-impact space. The stakes are high, but the PM skillset is perfectly suited to the industry's current-state challenges.

Whether you work with ML every day or are trying to break into the field, we'll have plenty of insights to share. We'll talk about the top barriers to high-impact and how Product Managers can use their unique skills to serve their customers and drive high-impact for the business.

You'll leave with...
-Deeper ML Insights
-What Machine Learning is and what can it do with it
-Why ML projects have a "high mortality" rate
-Why user's struggle to adopt ML features more than traditional features

Practical Takeaways
-How to manage stakeholder expectations
-How to increase ML feature user adoption
-How to pick high-impact ML use cases
-Career Insights
-How to break into Data Product Management

Can't wait to see you there!

Hi, my name is Josh Tong, Principal PM of Machine Learning at CognitOps . I spend my days working alongside our data science team to build Machine Learning features to modernize Warehouse Operations -- a critical stage of every global supply chain. I've spent the last 10+ years working in VC-backed startups and found my niche building data products for Supply Chain. I also write regularly on LinkedIn about building data products that drive high-impact.

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