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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? /$rby Emma Martinho-Truswell --$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.
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