Fractal Analytics
Successful AI Strategies: A CEO's Perspective
Fractal Analytics

Successful AI Strategies: A CEO's Perspective

Fractal Analytics Academy
Pranay Agrawal

位教师:Fractal Analytics Academy

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
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深入了解一个主题并学习基础知识。
4.5

(13 条评论)

中级 等级

推荐体验

9 小时 完成
灵活的计划
自行安排学习进度

您将学到什么

  • Explain key components for driving desired outcomes from AI

  • Explain strategies for error reduction and accuracy improvement in AI

  • Gain insights into optimizing organizational effectiveness in AI adoption

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作业

10 项作业

授课语言:英语(English)

了解顶级公司的员工如何掌握热门技能

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Leadership Strategies for AI and Generative AI 专项课程 专项课程的一部分
在注册此课程时,您还会同时注册此专项课程。
  • 向行业专家学习新概念
  • 获得对主题或工具的基础理解
  • 通过实践项目培养工作相关技能
  • 获得可共享的职业证书

该课程共有5个模块

Welcome to this module where we will unpack "What defines the success of your AI development?" In this module on AI adoption, you will delve into a comprehensive understanding of defining clear AI objectives, drawing insights from real-world examples such as Google Glass and Netflix. You will explore the crucial alignment of AI initiatives with business goals, taking into account market dynamics and ethical considerations. Through the lens of user-centered design and the DVF framework, you will craft purpose-driven AI strategies tailored to organizational needs. You'll also gain insights into engineering aspects, including scalable data pipelines and the critical role of high-quality data. By embracing design thinking principles, empathy, and iterative prototyping, you will learn to translate visions into intuitive AI solutions. The module concludes with actionable steps for implementation, emphasizing collaboration, monitoring, and continuous refinement

涵盖的内容

4个视频5篇阅读材料2个作业1个讨论话题

Welcome to this module on the synergy of AI, Data Engineering and Design Thinking. This module explores the crucial elements of AI adoption for achieving desired outcomes. By dissecting the equation Results (R) = AI × E^2 × D^2, participants uncover the pivotal roles of Data Engineering and Design Thinking. Data Engineering establishes robust data pipelines, enabling efficient processing and scalability crucial for AI functionality. Design Thinking ensures user-centric solutions, aligning AI applications with user needs through intuitive design and empathetic understanding. Through real-life examples and core principles, participants learn to orchestrate a harmonious blend of AI, engineering, and design, unlocking tangible value and fostering innovation. The module equips participants to champion AI implementations that drive sustainable business growth and user satisfaction.

涵盖的内容

7个视频3篇阅读材料2个作业

Welcome to "Addressing Errors in AI." Begin with a look back at Siri's early days, where amusing glitches highlighted AI's challenges. Discover why diverse and accurate data are crucial for AI success and how poor data quality can derail projects. Learn proactive strategies for ensuring data quality through governance and monitoring. Next, delve into the vital role of computing power in AI. Explore the hardware and software driving AI advancements, from GPUs and TPUs to frameworks like TensorFlow and PyTorch. Lastly, uncover the transformative power of AI technology, including deep learning and Generative Adversarial Networks (GANs). Understand how these technologies emulate human functions and enhance AI's capabilities. In conclusion, grasp the importance of reducing AI errors through advanced computing, quality data, and cutting-edge technology. As a business leader, understanding these nuances is key to leveraging AI's potential and fostering trust within your organization. Join us to unlock the true potential of AI!

涵盖的内容

4个视频2篇阅读材料2个作业

Welcome to this module on Organizational Effectiveness in AI. This module explores the key elements driving successful AI adoption within organizations, focusing on the equation OE = T × C × G, where Organizational Effectiveness (OE) is determined by Talent (T), Culture (C), and Governance (G). You will delve into the importance of recruiting skilled AI talent, fostering a culture of experimentation and learning, and establishing robust governance frameworks to ensure ethical AI deployment. By understanding the interconnected nature of these components, you will gain insights into achieving organizational effectiveness in the AI era.

涵盖的内容

4个视频1篇阅读材料2个作业

Welcome to this module on successful AI implementation. In this module, we delve into the intricacies of AI adoption with a focus on two pivotal strategies: Narrowly Focused AI and Human-Centered Design. Through real-world examples like Digi Mart's customer support AI and a retail giant's AI-driven personalization, we explore how organizations can harness AI to streamline processes and enhance user experiences. We also discuss the importance of strategic decision-making in AI implementation, comparing the risks and benefits of Decision-Forward and Decision-Backward approaches. This comprehensive exploration equips business leaders with insights and tools to effectively integrate AI into their operations, aligning technological advancements with organizational goals and user needs.

涵盖的内容

3个视频1篇阅读材料2个作业

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位教师

授课教师评分
2.7 (7个评价)
Fractal Analytics Academy
Fractal Analytics
23 门课程104,745 名学生
Pranay Agrawal
Fractal Analytics
1 门课程1,126 名学生

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Fractal Analytics

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