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Scaling AI Adoption in Engineering (A Practical Framework for Leaders)

List Price: $49.99
SKU:
9798341666856
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Expected release date is Dec 29th 2026
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  • Product Details

    Author:
    Peter Bell
    Format:
    Paperback
    Pages:
    275
    Publisher:
    O'Reilly Media (December 29, 2026)
    Imprint:
    O'Reilly Media
    Release Date:
    December 29, 2026
    Language:
    English
    ISBN-13:
    9798341666856
    Weight:
    16oz
    Dimensions:
    7" x 9.19"
    File:
    TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20260429163341-20260429.xml
    Folder:
    TWO RIVERS
    List Price:
    $49.99
    Country of Origin:
    United States
    Pub Discount:
    60
    Case Pack:
    20
    As low as:
    $42.99
    Publisher Identifier:
    P-PER
    Discount Code:
    C
  • Overview

    AI is already reshaping software development—but most companies are stuck in pilot mode, unsure how to move from experiments to meaningful transformation. This book offers engineering leaders a clear, pragmatic framework for adopting and scaling AI across the entire software development lifecycle.

    Author Peter Bell draws on years of experience building CTO communities and insights from interviews with top engineering leaders to deliver a hands-on guide for navigating the real challenges of AI adoption: aligning teams, evolving org structures, revisiting hiring strategies, and managing risk. With in-depth case studies and battle-tested heuristics, you'll learn how to connect GenAI tools to actual outcomes—without falling prey to hype or paralysis.

    You won't just explore what's possible; you'll walk away with a roadmap for operational change that balances innovation with pragmatism.

    • Design and lead a GenAI adoption strategy that fits your org's needs and constraints
    • Build experimentation pipelines that uncover what works
    • Rethink hiring, org design, and compliance in the age of AI
    • Communicate your AI vision with confidence to both engineers and executives
    • Create a culture of experimentation that's resilient, responsible, and results-focused