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    Generative AI Transformation Blueprint: Byte-Sized Learning Series, #3
    Generative AI Transformation Blueprint: Byte-Sized Learning Series, #3
    Generative AI Transformation Blueprint: Byte-Sized Learning Series, #3
    Ebook149 pages1 hour

    Generative AI Transformation Blueprint: Byte-Sized Learning Series, #3

    By I. Almeida

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    About this ebook

    This practical and concise guide provides senior decision-makers with a clear, accessible roadmap for harnessing the power of generative AI, enhancing innovation, and boosting business outcomes. 

     

    Drawing on insights from AI-enabled business transformations in diverse sectors, it presents a validated strategic approach. This blueprint not only outlines best practices but also showcases pioneering use cases, integrating them into a cohesive framework for practical implementation. This scenario-based approach helps leaders understand where and how to apply the practices outlined.

     

    Spanning across areas from strategic alignment and talent development to ethical governance and sustaining a competitive edge amid relentless underlying progress, it delivers clarity for charting an optimal Generative AI roadmap.

     

    This book is designed for busy executives and can be read in less than two hours. For a more in-depth exploration of Generative AI for business, check out our book "Introduction to Large Language Models for Business Leaders: Responsible AI Strategy Beyond Fear and Hype."

     

    Who this Book is For

     

    The core audience comprises senior executives like CEOs, Transformation advisors, strategic planners, technology heads, product leaders or functional unit heads keen on harnessing generative AI for a competitive edge but needing authoritative counsel consolidating recent lessons into a crisp actionable package to aid planning.

     

    Key Topics Covered

     

    • Understanding Generative AI: What it is, key capabilities and applications, strengths and limitations.
    • Strategic Alignment: Mapping generative AI to business goals, prioritizing high-impact use cases, managing risks.
    • Talent and Skills: Developing in-house capabilities through upskilling programs, attracting and retaining external AI talent.
    • Technology Integration: Assessing IT infrastructure readiness, optimizing make vs buy decisions for AI solutions.
    • Implementation and Scaling: Pilot testing for viability, expanding validated applications through metrics-driven scaling.
    • Risk Management and Ethics: Governing biases and reliability issues, safeguarding data privacy and security.
    • Organizational Change: Securing leadership commitment, preparing the workforce to adopt new AI-powered processes.
    • Continuous Improvement: Quantifying value through KPIs, optimizing models through responsive feedback loops.
    • Future-Proofing: Investing in R&D for sustained innovation, building agility to adapt to rapid AI progress.

     

    Featuring use studies, business scenarios, practical frameworks, and research insights from top consultancies and AI leaders, this book delivers a visionary yet pragmatic roadmap for AI transformation. A must-read guide for any organization looking to leverage generative AI for competitive advantage.

     

    More Than a Book

     

    By purchasing this book, you will also be granted free access to the AI Academy platform. There you can view free course modules, test your knowledge through quizzes, attend webinars, and engage in discussion with other readers. No credit card required.

     

    AI Academy by Now Next Later AI

     

    We are the most trusted and effective learning platform dedicated to empowering leaders with the knowledge and skills needed to harness the power of AI safely and ethically.

    LanguageEnglish
    PublisherNow Next Later AI
    Release dateNov 30, 2023
    ISBN9780645977974
    Generative AI Transformation Blueprint: Byte-Sized Learning Series, #3
    Author

    I. Almeida

    I. Almeida is the Chief Transformation Officer at Now Next Later AI, an AI advisory, training, and publishing business supporting organizations with their AI strategy, transformation, and governance. She is a strong proponent of human-centered, rights-respecting, responsible AI development and adoption. Ignoring both hype and fear, she provides a balanced perspective grounded in scientific research, validated business outcomes and ethics. With a wealth of experience spanning over 26 years, I. Almeida held senior positions at companies such as Thoughtworks, Salesforce, and Publicis Sapient, where she advised hundreds of executive customers on digital- and technology-enabled Business Strategy and Transformation. She is the author of several books, including four AI guides with a clear aim to provide an independent, balanced and responsible perspective on Generative AI business adoption. I. Almeida serves as an AI advisory member in the Adelaide Institute of Higher Education Course Advisory Committee. She is a regular speaker at industry events such as Gartner Symposium, SXSW, and ADAPT. Her latest books show her extensive knowledge and insights, displaying her unique perspective and invaluable contributions to the field.

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      Book preview

      Generative AI Transformation Blueprint - I. Almeida

      1

      INTRODUCTION

      The Advent of Generative AI

      Artificial intelligence has advanced tremendously in the last decade from narrow domain-specific capabilities to more expansive, multi-functional systems that can synthesize novel artifacts like text, images and video with increasing sophistication. This new era heralded the dawn of generative AI.

      Unlike previous reactive AI systems designed for tasks like visual recognition or predictive analytics, generative models create completely new data patterned on training datasets. Groundbreaking examples include systems like DALL-E which generate striking images simply from text descriptions or the GPT series capable of crafting synthetic but cogent essays on arbitrary topics.

      Beyond their almost magical creativity, these emergent capabilities presage significant shifts across industries. As generative AI proliferates, competitive advantage will center on effectively leveraging its applications. However, doing so calls for prudent strategy stemming from insightful examination of suitable use cases, thoughtful approaches for scalable implementation and anticipation of risks from model opacity or data privacy threats.

      This transformation demands demystification for leaders navigating uncharted waters filled with hype, false starts and uncertainty about best practices.

      Demystifying Generative AI Transformation

      This guide provides senior decision-makers with a clear, accessible roadmap for harnessing the power of generative AI, enhancing innovation, and boosting business outcomes. Drawing on insights from leading consultancies and input from both established and rising leaders in the AI field, it presents a validated strategic approach. This blueprint not only outlines best practices but also showcases pioneering use cases, integrating them into a cohesive framework for practical implementation.

      Spanning across areas from strategic alignment and talent development to ethical governance and sustaining competitive edge amid relentless underlying progress, it delivers clarity for charting an optimal generative AI roadmap.

      Who this Book is For

      The core audience comprises senior executives like CEOs, strategic planners, technology heads, product leaders or functional unit heads keen on harnessing generative AI for a competitive edge but needing authoritative counsel consolidating recent lessons into a crisp actionable package to aid planning.

      How this Book is Structured

      The chapters provide end-to-end coverage beginning with foundational concepts, leading into implementation modules and culminating in sustenance best practices:

      Chapters 2-4 establish understanding, discovery mindsets and strategic alignment principles constituting base bricks for subsequent generative AI build phases.

      Chapters 5-8 guide technology, infrastructure and capability upgrades for pilot testing with protocols for systematizing scaling.

      Chapters 9-11 cement responsiveness and innovation elements needed for maximizing generative AI reliability and longevity despite external flux.

      Chapter 12 offers concluding thoughts on the road ahead.

      All chapters include sample scenarios and helpful frameworks and research, convenient for reference during planning.

      With expansive technological disruption on the horizon, this handbook delivers a visionary blueprint for leadership teams to harness generative AI as a catalyst for unprecedented progress. Let us turn the page and begin this transformative journey.

      2

      UNDERSTANDING AND AWARENESS

      Generative AI represents a seismic shift in artificial intelligence capabilities. Systems that were previously focused narrowly on specific tasks can now perform a much wider range of cognitive functions in an increasingly human-like manner. This poses both tremendous opportunities and complex challenges for organizations seeking to leverage these rapidly evolving technologies.

      As generative AI permeates across industries, business leaders require a foundational understanding of its potentials and limitations to chart an effective strategic course. This chapter aims to demystify key aspects of generative AI and establish best practices for continuous learning and benchmarking. With comprehensive understanding and awareness, organizations can make informed decisions to harness generative AI as a transformative driver of innovation and growth.

      Demystifying Generative AI

      What is Generative AI?

      Generative AI refers to a class of artificial intelligence algorithms capable of producing novel, high-quality artifacts with little or no human guidance. The term encompasses a range of techniques including generative adversarial networks, diffusion models, reinforcement learning, and transformer architectures.

      Unlike traditional AI systems designed for narrow tasks like classification and prediction, generative models can synthesize various kinds of data such as text, images, video, and audio that capture intricate statistical patterns from their training data. Prominent examples of generative AI today include systems like DALL-E for image generation and the GPT series for natural language processing.

      Capabilities and Applications

      The open-ended nature of generative models unlocks promising new capabilities across diverse domains:

      Natural language processing: Automated writing, conversational systems, language translation

      Computer vision: Image and video generation, editing media

      Drug discovery: Identifying potential therapeutic molecules

      Design: Generating logos, websites, industrial design blueprints

      Personalization: Customized marketing content, personalized recommendations

      Leading technology research firm Gartner predicts that by 2025, 70% of enterprises will use some form of generative AI to augment business operations, a significant leap from less than 5% in 2022.

      Strengths and Promise

      Strengths and Promise
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