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001			22736080
003			OSt
005			20250610123353.0
008			220723s2023 mau b 001 0 eng
010			_a 2022030290
020			_a9781647824433 _q(paperback)
020			_z9781647824440 _q(epub)
040			_aMH/DLC _beng _erda _cDLC _dDLC
042			_apcc
050	0	0	_aHD30.2 _b.H325 2023
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245	0	0	_aHBR guide to AI basics for managers / _cHarvard Business Review.
246	3		_aHarvard business review guide to AI basics for managers
246	3	0	_aAI basics for managers
246	3		_aArtificial intelligence basics for managers
264		1	_aBoston, Massachusetts : _bHarvard Business Review Press, _c[2023]
300			_axiii, 252 pages ; _c23 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aHBR guides
500			_aIncludes bibliographical references and index.
505	0		_tThree 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? / _rby Emma Martinho-Truswell -- _tWhat Every Manager Should Know About Machine Learning : A non-technical primer / _rby Mike Yeomans -- _tThe Three Types of AI : First, understand which technologies perform which types of tasks / _rby Thomas H. Davenport and Rajeev Ronanki -- _tAI Doesn't Have to Be Too Complicated or Expensive for Your Business : Focus on data quality, not quantity / _rby Andrew Ng -- _tHow AI Fits into Your Data Science Team : Get over the cultural hurdles and avoid exaggerated claims / _ran interview with Hilary Mason -- _tRamp Up Your Team's Predictive Analytics Skills : Three pitfalls your team needs to avoid / _rby Eric Siegel -- _tAssembling Your AI Operations Team : A top-notch model is no good if your people can't connect it to your existing systems / _rby Mark Esposito, Terence Tse, Takaai Mizuno, and Danny Goh -- _tHow to Spot a Machine Learning Opportunity : What do you want to predict, and do you have the data? / _rby Kathryn Hume -- _tA Simple Tool for Making Decisions with AI : Use the AI Canvas / _rby Ajay Agrawal, Joshua Gans, and Avi Goldfarb -- _tHow to Pick the Right Automation Project : Invest in the ones that will build your organization's capabilities / _rby Bhaskar Ghosh, Rajendra Prasad, and Gayathri Pallail -- _tCollaborative Intelligence : Humans and AI Are Joining Forces : They're enhancing each other's strengths / _rby H. James Wilson and Paul R. Daugherty -- _tHow to Get Employees to Embrace AI : The sooner resisters get onboard, the sooner you will see results / _rby Brad Power -- _tA Better Way to Onboard AI : Understand it as a tool to assist people rather than replace them / _rby Boris Babic, Daniel L. Chen, Theodoros Evgeniou, and Anne-Laure Fayard -- _tManaging AI Decision-Making Tools : Humans still need to be involved : This framework will help you determine when and how / _tby Michael Ross and James Taylor -- _tYour Company's Algorithms Will Go Wrong : Have a Plan in Place : An AI designed to do X will eventually fail to do X / _rby Roman V. Yampolskiy -- _tA Practical Guide to Ethical AI : AI doesn't just scale solutions - it also scales risk / _rby Reid Blackman -- _tAI Can Help Address Inequity - If Companies Earn Users' Trust : A case from Airbnb shows how good algorithms can have negative effects / _rby Shunyuan Zhang, Kannan Srinivasan, Param Vir Singh, and Nitin Mehta -- _tTake Action to Mitigate Ethical Risks : It starts with three critical conversations / _rby Reid Blackman and Beena Ammanath -- How No-Code Platforms Can Bring AI to Small and Midsize Businesses : Three features to look for as you consider the right tool for your company / _rby Jonathon Reilly -- _tThe Power of Natural Language Processing : NLP can help companies with brainstorming, summarizing, and researching. / _rby Ross Gruetzemacher -- _tReinforcement Learning Is Ready for Business : Learning through trial and error can lead to more creative solutions / _rby Kathryn Hume and Matthew E. Taylor.
520			_a"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"-- _cProvided by publisher.
650		0	_aArtificial intelligence.
650		0	_aManagement _xTechnological innovations.
650		0	_aBusiness enterprises _xInformation technology _xManagement.
650		0	_aIndustrial management.
650		0	_aSuccess in business.
710	2		_aHarvard Business Review Press, _eissuing body.
776	0	8	_iOnline version: _tHBR guide to AI basics for managers _dBoston, Massachusetts : Harvard Business Review Press, [2023] _z9781647824440 _w(DLC) 2022030291
830		0	_aHarvard business review guides.
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