MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review. For years, organizations have been using artificial intelligence to automate manual tasks and improve products and services. But as real-world use cases for AI multiply, so too do the ethical implications of simulating human intelligence in machines. Today, AI-powered chatbots help to screen job candidates, facial recognition systems keep workplaces safe from intruders, and sophisticated AI algorithms predict market trends and emerging customer demands. These examples deliver benefits ranging from increased productivity and employee well-being to competitive gains.

MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review. "How AI Changes the Rules: New Imperatives for the Intelligent Organization" is a 2020 global report from MIT SMR Connections and SAS that explores the changes that leaders must prepare for to successfully implement trusted AI. The most advanced AI practitioners are more likely to have established new technology governance practices, lead the way in confronting AI ethics issues, and be able to explain their AI decisions to stakeholders. Click on the image to view the full-size version.

Download a new guide that will help you close the machine learning skills gap in your organization. Complete the registration form to receive a complimentary PDF download of "Transform Your Workforce With Skills for Machine Learning," courtesy of Amazon Web Services. In this new manager's guide, you'll get a framework for action on closing the machine learning skills gap by developing the existing capabilities within your company. You'll learn how to assess your current workforce's skills and identify gaps; identify individuals with promise for data science roles; build data literacy across your organization; and explore diverse options for training in machine learning skills. Download the full guide now and start upskilling your workforce.

Deriving more value from analytics and emerging technologies like artificial intelligence starts with trust, simply because data collected for analytics must be trusted. Customers and partners that share data must trust that it's safeguarded and used appropriately from collection through storage and to how it's applied. And once insights emerge from applying analytics to the data, individuals throughout the organization must understand the care given to data management so that they trust those insights -- and use them -- to make decisions and ask new questions. Our global survey of more than 2,400 business leaders and managers provides insight into organizations' activities in each of these key areas and identifies where recognized best practices are becoming more mainstream and where they may still be exceptional. It found that respondents who have advanced their analytics practices to incorporate AI-based technologies such as machine learning and natural language processing work in organizations that do the most to foster data quality, safeguard data assets, and develop cultures of data literacy and innovation.

Putting machine learning into production in the enterprise is not easy: Many organizations are struggling to implement the technology at scale. But it is possible to make the process of building, scaling, and deploying enterprise machine learning solutions repeatable and predictable. Join Tom Davenport, President's Distinguished Professor of IT and Management, Babson College; Alex Breshears, senior product manager, Production Machine Learning, Cloudera; and Abbie Lundberg, business technology analyst, Lundberg Media for a discussion of the specific challenges enterprises face in machine learning and how they can create an end-to-end, factory-like capability. The content was created by the speakers of this event. The MIT Sloan Management Review editorial staff was not involved in the selection, development, or broadcast of this event.

Often, It's Not about the AI Narrowly focused task-and domain-specific AI has been applied successfully for more than 25 years and has produced immense value in industry and government. It doesn't lead directly to artificial general intelligence (AGI), but it does have real problem-solving value. It is useful to note that many of the reasons some otherwise meritorious AI applications fail have nothing to do with the AI per se but rather with systems engineering and organizational issues. For example, the domain expert is pulled out to work on more critical projects; the application champion rotates out of his or her position; or the sponsor changes priorities. A system may not make it past an initial pilot test for logistical rather than substantive technical reasons.

They are held on a Saturday and Sunday, with a tutorial or two on the preceding Friday. This year the workshop was preceded by two days of tutorials: an introductory tutorial on Thursday and a more advanced tutorial on Friday. The tutorials were held at the University of Michigan's Advanced Technology Lab and at the workshop site. There were 37 talks this year as well as a discussion session with 57 attendees. Seven sites made one presentation, sometimes involving multiple researchers.