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
The Ai Operating Problem: A Practical Framework For Business Impact

The Ai Operating Problem: A Practical Framework For Business Impact

Paperback

Business GeneralManagement

Currently unavailable to order

ISBN13: 9798170652334
Publisher: Independently Published
Pages: 122
Weight: 0.66
Height: 0.26 Width: 8.50 Depth: 11.00
Language: English
The digital world echoes with the promise of Artificial Intelligence, hailed as a revolutionary force set to redefine business. Yet, for every headline-grabbing success, countless AI initiatives falter, proofs-of-concept gather dust, and ambitious projects fail to deliver meaningful return on investment. This chasm between AI's perceived potential and its operational reality is what 'The Ai Operating Problem' diagnoses. It's not the technology itself that's at fault, but a fundamental misunderstanding: AI is not magic to be bought, but an operational discipline to be meticulously built and integrated.

In 'The Ai Operating Problem: A Practical Framework For Business Impact, ' you will discover a structured approach to navigate the complexities of AI deployment. This book moves beyond abstract rhetoric, offering a practical framework-Apply, Measure, Pitfalls-designed to transform AI from an expensive distraction into a genuine asset. It delves into critical aspects such as achieving data readiness, identifying high-impact AI use cases, fostering human-AI partnerships, and seamlessly operationalizing models from lab to production. You'll learn to look beyond vanity metrics to measure true business impact, understand and overcome common adoption barriers, and navigate the intricate landscape of AI ethics and governance.

This comprehensive guide empowers executives, managers, and practitioners to approach AI with the rigor it demands. It provides the strategic clarity needed to define specific, measurable problems that AI can truly solve, preventing the common mistake of 'AI-washing' simpler automation tasks. By understanding the predictable AI failure patterns, you can proactively mitigate risks and ensure your investments yield tangible results. This book serves as a playbook for operators, equipping you to build robust AI capabilities that scale and deliver sustained value, rather than chasing fleeting 'what if' scenarios without grappling with the 'how' and 'at what cost.'

By embracing the insights within 'The Ai Operating Problem, ' you will be able to:

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