Fellow, MIT Center for Digital Business, Tom Davenport
NewVantage Partners just released its 7th annual executive survey on big data and artificial intelligence in large organizations. If you’re pulling for better data, analytics, and AI within companies, there is much to encourage you in this year’s survey. Many aspects of this important domain of business show improvement:
There was a higher participation rate in the survey than ever before, suggesting that more executives believe the topic is important.
90% of those who completed the survey are “C-level” executives—chief data, analytics, or information officers. A decade ago, only one of these jobs (the CIO) even existed.
92% of the respondents are increasing their pace of investment in big data and AI.
62% have already seen measurable results from their investments in big data and AI (a bit less than in 2018, but still pretty good).
48% say their organization competes on data and analytics. When Tom introduced this concept in a 2006 HBR article, perhaps 5% of large organizations would have said they did so.
Thomas W. Malone is the Patrick J. McGovern (1959) Professor of Management, a Professor of Information Technology
From The Wall Street Journal
Ask people about artificial intelligence, and the discussion will most often turn to jobs: which ones will be eliminated and which ones will be created.
But regardless of what happens to the number of jobs, there’s another question that is less often discussed but crucial for maximizing both productivity and employee morale: How is AI likely to change the structure of business hierarchies themselves?
The obvious answer may be that the management structure is likely to get more centralized and rigid. After all, AI will help managers track more detailed data about everything their subordinates are doing, which should make it easier—and more inviting—to exercise stricter controls.
This will no doubt be true in some cases. But look more closely, and I believe the opposite is much more likely to happen in many cases. That’s because when AI does the routine tasks, much of the remaining nonroutine work is likely to be done in loose “adhocracies,” ever-shifting groups of people with the combinations of skills needed for whatever problems arise.
The digital age is impacting all aspects of life, including the future of work. Technological innovations have the potential to transform the workplace and enhance productivity, but it will take proactive and thoughtful discussion to harness these innovations for social benefit.
To explore this further, MIT Sloan Experts is hosting the #MITSloanBrazil Twitter chat on August 21 at 9 a.m. ET (10 a.m. São Paulo) to discuss the topics and themes of the upcoming Future of Work Conference in Brazil.
The conference, which will bring together leading experts from business and academia, aims to highlight the ways in which artificial intelligence, automation and the changing economy are affecting the future of work. This issue is crucial in Brazil, where 12 percent of the country’s workforce is unemployed.
Join us on Twitter on August 21 at 9 a.m. ET (10 a.m. São Paulo) and follow along using the hashtag #MITSloanBrazil. Your comments and questions are encouraged! Simply include #MITSloanBrazil in your Tweets.
Fellow, MIT Center for Digital Business, Tom Davenport
From BizEd Magazine
The rise of data analytics is one of the hallmarks of 21st-century business. By the turn of the century, companies had been accumulating data in various transaction systems for several decades, and many desired to analyze the data to make better decisions. Their interest intensified in the early 2000s as they saw the great success of online firms from Silicon Valley, many of which were highly analytical.
In fact, during the mid-2000s, I conducted research showing that some companies were “competing on analytics”— that is, emphasizing their analytical capabilities as a key element of their strategies—and that those companies tended to outperform other firms in their industries. Information about analytics even made it into popular culture, especially through books such as Moneyball, which was also a successful movie. Both depicted the way the Oakland A’s of California built a winning baseball team through targeted data analysis.
Susan Silbey, Leon and Anne Goldberg Professor of Humanities, Professor of Behavioral and Policy Science, MIT Sloan School of Management
From LSE Business Review
As artificial intelligence (AI) and machine learning techniques increasingly leave engineering laboratories to be deployed as decision-making tools in Human Resources (HR) and related contexts, recognition of and concerns about the potential biases of these tools grows. These tools first learn and then uncritically and mechanically reproduce existing inequalities. Recent research shows that this uncritical reproduction is not a new problem. The same has been happening among human decision-makers, particularly those in the engineering profession. In AI and engineering, the consequences are insidious, but both cases also point toward similar solutions.
Bias in AI
One common form of AI works by training computer algorithms on data sets with hundreds of thousands of cases, events, or persons, with millions of discrete bits of information. Using known outcomes or decisions (what is called the training set) and the range of available variables, AI learns how to use these variables to predict outcomes important to an organisation or any particular inquirer. Once trained by this subset of the data, AI can be used to make decisions for cases where the outcome is not yet known but the input variables are available.