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How Should Artificial Intelligence Be Regulated—if at All?

by James Heskett

Some AI pioneers say the technology could be a risk to humanity, and some governments have taken steps to rein it in. But who should set the rules and what details must they consider? asks James Heskett. Open for comment; 0 Comments.

Can AI and Machine Learning Help Park Rangers Prevent Poaching?

Re: Brian L. Trelstad

Globally there are too few park rangers to prevent the illegal trade of wildlife across borders, or poaching. In response, Spatial Monitoring and Reporting Tool (SMART) was created by a coalition of conservation organizations to take historical data and create geospatial mapping tools that enable more efficient deployment of rangers. SMART had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching. Then a new predictive analytic tool, the Protection Assistant for Wildlife Security (PAWS), was created to use artificial intelligence (AI) and machine learning (ML) to try to predict where poachers would be likely to strike. Jonathan Palmer, Executive Director of Conservation Technology for the Wildlife Conservation Society, already had a good data analytics tool to help park rangers manage their patrols. Would adding an AI- and ML-based tool improve outcomes or introduce new problems? Harvard Business School senior lecturer Brian Trelstad discusses the importance of focusing on the use case when determining the value of adding a complex technology solution in his case, “SMART: AI and Machine Learning for Wildlife Conservation.”

Free Isn’t Always Better: How Slack Holds Its Own Against Microsoft Teams

by Lane Lambert

What will it take to win the collaboration app wars: massive scale or a loyal following? A case study by David Yoffie digs into the intense competition between Microsoft Teams and Salesforce's Slack.

Developing Resilience on the Path to Becoming a CEO

Re: Tsedal Neeley

As a Black female CEO, Shellye Archambeau is no stranger to adversity. Now she faces her most critical leadership decision. The software company she leads, MetricStream, is losing customers, hemorrhaging cash, and struggling to make payroll. Harvard Business School professor Tsedal Neeley discusses Archambeau’s leadership style and the importance of developing resilience, particularly when managing through a crisis, in her case, “Shellye Archambeau: Becoming a CEO.” Open for comment; 0 Comments.

IT Job Wages Are No Longer 'Exceptional'

by Rachel Layne

Wage growth in IT jobs has moderated following the dot-com boom, according to new research by Ruiqing Sam Cao and Shane Greenstein. Open for comment; 0 Comments.

Digital Labor Market Inequality and the Decline of IT Exceptionalism

by Ruiqing Cao and Shane Greenstein

The experience in five cities accounts for almost all the wage inequality in IT wages in the US between 2000 and 2018. Overall that brought IT wages closer to STEM wages.

Lessons from IBM in Nazi Germany

Re: Geoffrey G. Jones

Geoffrey Jones discusses his case study, "Thomas J. Watson, IBM and Nazi Germany," exploring the options and responsibilities of multinationals with investments in politically reprehensible regimes. Open for comment; 0 Comments.

The Transformation of Microsoft

Re: C. Fritz Foley

In early 2015, Microsoft CFO Amy Hood and the rest of senior leadership faced a fundamental choice. Was the company ready to invest in long-term growth at the expense of some short-term profit? Professor Fritz Foley discusses how executives thought through the tradeoffs. Open for comment; 0 Comments.

Overcoming the Challenges of Selling Brand New Technology (Hey, Need a 3-D Printer?)

by Michael Blanding

Selling technology that is new to the market involves tricky tradeoffs around prospect targeting, channels, and tactics. Frank Cespedes makes the point with 3-D printers. Open for comment; 0 Comments.

What Should Mark Zuckerberg Do?

by James Heskett

SUMMING UP: Facebook doesn't necessarily need a better data-privacy policy, James Heskett's readers suggest. Instead, Mark Zuckerberg needs a new business model. Open for comment; 0 Comments.

Different Strokes for Different Folks: Experimental Evidence on Complementarities Between Human Capital and Machine Learning

by Prithwiraj Choudhury, Evan Starr, and Rajshree Agarwal

This study contributes to scholarship on how adoption of machine learning tools will shape knowledge worker productivity. Among its implications for managers, it suggests that complementarities between prior skills and technology will determine the productivity of workers using AI tools.

Will Machine Learning Make You a Better Manager?

by Michael Blanding

Big data...artificial intelligence...Internet of Things. These technologies have taken their shares of the headlines the past few years, but now machine learning is the buzz. Mike Teodorescu explains how it is changing the lives of consumers and businesses. Open for comment; 0 Comments.

Faber-Castell Doubles Down on the Pencil

Re: Ryan L. Raffaelli

Some products are just harder to innovate than others. The pencil, for instance. Yet, Faber-Castell has been turning them out for more than 200 years, adding improvements such as color and cosmetic products to the mix. In this podcast, Ryan Raffaelli discusses how older products can become young again. Open for comment; 0 Comments.

Amazon Web Services Changed the Way VCs Fund Startups

by Michael Blanding

Starting companies is becoming so quick and cheap that venture capitalists have shifted strategy funding entrepreneurs. Now, more startups get backing—but they have to prove themselves in a hurry, according to research by Ramana Nanda and colleagues. Open for comment; 0 Comments.

Without Immigrants, We Wouldn't Have Google

by Shane Greenstein

What is the secret sauce of US commercial success? It’s the contributions of immigrants, like Google co-founder Sergey Brin, who have made huge contributions to the technological and economic success of the United States, argues Harvard Business School Professor Shane Greenstein. Open for comment; 0 Comments.