Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Why Great Products Still Fail: Updated Lessons for 2026 and Beyond: Understanding the Hidden Blind Spots in the Age of Data, Analytics, and AI

Why Great Products Still Fail: Updated Lessons for 2026 and Beyond: Understanding the Hidden Blind Spots in the Age of Data, Analytics, and AI

Sunn, Stephan S.

Paperback

Business General

ISBN13: 9798187912773
Publisher: Independently Published
Published: Jul 18 2026
Pages: 78
Weight: 0.23
Height: 0.20 Width: 6.00 Depth: 9.00
Language: English
In an era defined by unprecedented access to data, advanced AI, and rapid innovation, this book confronts a troubling paradox: despite having more tools and information than ever, great products continue to fail at alarming rates. Rather than attributing these failures to technological shortcomings, the author argues they stem from hidden organizational and cognitive blind spots. The book fundamentally challenges the assumption that more data leads to better decisions, revealing how metrics often create false confidence and obscure the real reasons products don't gain traction. This 2026 edition revisits the publication we first released on KDP.Amazon.com. While the thematic foundation remains unchanged, the case pool has grown substantially. Yet even with today's more sophisticated AI tools, increased capability hasn't prevented us from encountering-and repeating-similar errors.

The book systematically identifies eight critical blind spots that derail even the most promising products, from solving problems customers don't urgently need solved to the silent friction that causes customers to abandon products without explanation. It explores why companies mistake polite validation for genuine market demand, how dashboards create an analytics illusion that substitutes data for true insight, and why workflow integration often trumps feature superiority. Crucially, the book addresses the unique challenges of AI-powered products, including automation fatigue, the difficulty of establishing user trust, and the phenomenon of silent AI failures that go undetected by traditional monitoring systems.

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