Patterns for AI adoption

• Verfasst von: Spanner, Andreas [VerfasserIn]
• Verfasst von: Ponnusamy, Ahilan [VerfasserIn]
• Titel: Patterns for AI adoption
• Titelzusatz: operationalizing machine learning across industry use cases
• Verf.angabe: Andreas Spanner and Ahilan Ponnusamy
• Ausgabe: [First edition].
• Verlagsort: Sebastopol, CA
• Verlag: O'Reilly Media, Inc.
• Jahr: 2025
• Umfang: 1 online resource (48 pages)
• Illustrationen: illustrations
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Intelligence artificielle ; Gestion
• Sach-SW: Apprentissage automatique
• Sach-SW: Prise de décision ; Informatique
• Sach-SW: Entreprises ; Informatique
• Sach-SW: Innovations ; Gestion
• Sach-SW: Gestion de l'information
• K10plus-PPN: 1924462133
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4718965292
• Lokale URL Inst.: Zum Volltext

Abstract

There's unprecedented pressure on business leaders to jump on the AI bandwagon and deliver the transformative results this technology promises. But few are aware of the range and suitability of AI solutions, and still fewer know how to best operationalize solutions according to their own organization's needs. This report puts decision makers on the right track when planning their AI adoption approach. Authors Andreas Spanner and Ahilan Ponnusamy bring a breadth of lessons learned rolling out and operationalizing AI across industries. You'll learn replicable patterns of practice that will help keep your AI initiatives grounded in the particulars of your enterprise, not in hopes or hype. The report also covers the vital pillars of AI, such as evolving data architectures, data management strategies, risks, and other key considerations for creating, deploying, and managing AI solutions.