Screen LibraryHBR guide to AI basics for managers /
"From product design and financial modeling to performance management and hiring decisions-artificial intelligence and machine learning are becoming everyday tools for managers at businesses of all sizes. But the rewards of every AI system come with risks-and if you don't understand how to...
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| Corporate Authors: | |
|---|---|
| Published: |
Harvard Business Review Press,
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| Publisher Address: | Boston, Massachusetts : |
| Publication Dates: | [2023] |
| Literature type: | Book |
| Language: | English |
| Series: |
Harvard Business Review guides
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| Subjects: | |
| Summary: |
"From product design and financial modeling to performance management and hiring decisions-artificial intelligence and machine learning are becoming everyday tools for managers at businesses of all sizes. But the rewards of every AI system come with risks-and if you don't understand how to make sense of them, you're not going to make the right decisions. Whether you want to get up to speed quickly, could just use a refresher, or are working with an AI expert for the first time, HBR Guide to AI Basics for Managers will give you the information and skills you need. You'll learn how to: understand key terms and concepts; identify which of your projects and processes would benefit from an AI approach; deal with ethical issues before they come up; hire the best AI vendors; run small experiments; work better with your AI experts and data scientists"-- |
| Item Description: | Includes index. |
| Carrier Form: | xiii, 252 pages : illustrations ; 23 cm. |
| ISBN: |
9781647824433 1647824435 9781647824457 1647824451 |
| Index Number: | HD30 |
| CLC: | F272.7-39 |
| Call Number: | F272.7-39/H339 |
| Contents: |
Three Questions About AI That Every Employee Should Be Able to Answer : How does it work, what is it good at, and what should it never do? / What Every Manager Should Know About Machine Learning : A non-technical primer / The Three Types of AI : First, understand which technologies perform which types of tasks / AI Doesn't Have to Be Too Complicated or Expensive for Your Business : Focus on data quality, not quantity / How AI Fits into Your Data Science Team : Get over the cultural hurdles and avoid exaggerated claims / Ramp Up Your Team's Predictive Analytics Skills : Three pitfalls your team needs to avoid / Assembling Your AI Operations Team : A top-notch model is no good if your people can't connect it to your existing systems / How to Spot a Machine Learning Opportunity : What do you want to predict, and do you have the data? / A Simple Tool for Making Decisions with AI : Use the AI Canvas / How to Pick the Right Automation Project : Invest in the ones that will build your organization's capabilities / Collaborative Intelligence : Humans and AI Are Joining Forces : They're enhancing each other's strengths / How to Get Employees to Embrace AI : The sooner resisters get onboard, the sooner you will see results / A Better Way to Onboard AI : Understand it as a tool to assist people rather than replace them / Managing AI Decision-Making Tools : Humans still need to be involved : This framework will help you determine when and how / by Michael Ross and James Taylor -- Your Company's Algorithms Will Go Wrong : Have a Plan in Place : An AI designed to do X will eventually fail to do X / A Practical Guide to Ethical AI : AI doesn't just scale solutions -- it also scales risk / AI Can Help Address Inequity -- If Companies Earn Users' Trust : A case from Airbnb shows how good algorithms can have negative effects / Take Action to Mitigate Ethical Risks : It starts with three critical conversations / The Power of Natural Language Processing : NLP can help companies with brainstorming, summarizing, and researching. / Reinforcement Learning Is Ready for Business : Learning through trial and error can lead to more creative solutions / |