Analyzing the future – Thomas Davenport

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.

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Every leader’s guide to the ethics of AI – Tom Davenport and Vivek Katyal

Fellow, MIT Center for Digital Business, Tom Davenport

From the MIT Sloan Management Review 

As artificial intelligence-enabled products and services enter our everyday consumer and business lives, there’s a big gap between how AI can be used and how it should be used. Until the regulatory environment catches up with technology (if it ever does), leaders of all companies are on the hook for making ethical decisions about their use of AI applications and products.

Ethical issues with AI can have a broad impact. They can affect the company’s brand and reputation, as well as the lives of employees, customers, and other stakeholders. One might argue that it’s still early to address AI ethical issues, but our surveys and others suggest that about 30% of large companies in the U.S. have undertaken multiple AI projects with smaller percentages outside the U.S., and there are now more than 2,000 AI startups. These companies are already building and deploying AI applications that could have ethical effects.

Many executives are beginning to realize the ethical dimension of AI. A 2018 survey by Deloitte of 1,400 U.S. executives knowledgeable about AI found that 32% ranked ethical issues as one of the top three risks of AI. However, most organizations don’t yet have specific approaches to deal with AI ethics. We’ve identified seven actions that leaders of AI-oriented companies — regardless of their industry — should consider taking as they walk the fine line between can and should.

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A 2019 forecast for data-driven business: from AI to ethics – Tom Davenport

Fellow, MIT Center for Digital Business, Tom Davenport

From Forbes

It should come as no surprise that 2018 continued to mark another year in the progression of data adoption in business.  Companies are pushing forward with efforts to become increasingly data-driven.  Firms are investing in transformation initiatives to establish a “data culture” within their organizations.  Early adopters are focused on data-driven business innovation.

As we look ahead to 2019, we reflect on a year of accomplishments and emerging areas of focus – from AI through Ethics (listed alphabetically)

  • AI/Machine Learning—AI continued to grow in popularity over the past year, becoming well-institutionalized within many large enterprises. We argued in a previous post, however, that too many companies employed AI pilots and prototypes, and not enough firms had implemented production deployments. As with analytics, the use of AI is increasingly being democratized through automated machine learning (AutoML). Several contributors to KD Nuggets’ review of AI and ML trends for 2019 suggested that AutoML would become more popular over the next year. It will make machine learning models easier to create for business analyst types, as well as dramatically increasing the productivity of data scientists—that is, if they can be persuaded to use it. We also predict that deep learning, which has been the fastest-growing and most popular AI technology over the past several years, will continue to advance in power and prevalence for several years. However, we also expect that deep learning will increasingly be augmented by other approaches to AI. NYU professor Gary Marcus has argued, and we agree, that artificial general intelligence—or even generally useful AI—will have to employ various techniques beyond deep learning in order to be successful.

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