The Low-code Ai Maturity Model: Leading Responsible Ai Transformation With Microsoft Power Platform - Brossura

Jeffery, Steve

Brossura

ISBN 13: 9798868817298

Casa editrice: Apress, 2025

Riassunto

Written from the perspective of an experienced IT expert, this book leverages a maturity model framework to guide organizations through each stage of adopting, securing, scaling, and tracking the value of low-code AI responsibly and efficiently. Unlike other works that focus only on AI’s technical aspects, this book boasts a practical roadmap for responsible AI adoption and provides a clear, structured maturity model that guides organizations from foundational steps to advanced, responsible AI practices.

In a world where organizations are under pressure to innovate quickly and responsibly, this book provides a structured approach, addressing essential elements such as data management, governance, ethics and compliance in low-code AI environments. Each chapter represents a maturity level, from foundational to optimized, offering readers insights, practical steps and examples to support their journey.

The Low-Code AI Maturity Model takes a comprehensive look into the transformative potential of low-code and AI.

What You Will Learn

• Identify, assess and mitigate risks to ensure stable and reliable AI deployments
• Implement robust governance, aligning with compliance standards and ethical principles at each maturity stage
• Evaluate the real business value and ROI of AI projects, empowering data-driven decision-making

Who This Book Is For

IT leaders, decision-makers, and technology enthusiasts interested in the transformative potential of low-code and AI

Su questo libro

Informazioni sull'autore

Steve Jeffery has been an IT professional since 1996, with a career that spans hardware, software, consulting, and architecture. His journey started in hardware, evolving to software roles in the early 2000s, and ultimately leading to his current role as Principal Program Manager in Microsoft’s Power CAT team. During the past 13 years at Microsoft, he has gained extensive experience working directly with large enterprise customers, guiding them in adopting Microsoft’s Power Platform and helping them overcome the complex challenges of integrating new technologies at scale.

One of his most significant contributions has been to Microsoft’s Center of Excellence (CoE) Starter Kit and the Business Value Toolkit—resources that have empowered organizations globally to deploy, manage, and drive value from low-code and AI solutions. As a recognized expert in low-code AI, he has a deep understanding of the practical and strategic elements required for successful adoption, from technical implementation to cultural alignment.

Dalla quarta di copertina

Written from the perspective of an experienced IT expert, this book leverages a maturity model framework to guide organizations through each stage of adopting, securing, scaling, and tracking the value of low-code AI responsibly and efficiently. Unlike other works that focus only on AI’s technical aspects, this book boasts a practical roadmap for responsible AI adoption and provides a clear, structured maturity model that guides organizations from foundational steps to advanced, responsible AI practices.

In a world where organizations are under pressure to innovate quickly and responsibly, this book provides a structured approach, addressing essential elements such as data management, governance, ethics and compliance in low-code AI environments. Each chapter represents a maturity level, from foundational to optimized, offering readers insights, practical steps and examples to support their journey.

The Low-Code AI Maturity Model takes a comprehensive look into the transformative potential of low-code and AI.

You will: 

• Identify, assess and mitigate risks to ensure stable and reliable AI deployments
• Implement robust governance, aligning with compliance standards and ethical principles at each maturity stage
• Evaluate the real business value and ROI of AI projects, empowering data-driven decision-making

Dettagli dell'edizione

• Editore: Apress
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 13: 9798868817298
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 350
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania