MIT Sloan Management Review Fall 2019

86 MIT SLOAN MANAGEMENT REVIEW FALL 2019 SLOANREVIEW.MIT.EDU

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provide ubiquitous data, unlimited connectivity, and massive processing power. Savvy companies are

converting all this capacity into digital offerings: information-enriched solutions wrapped in seamless,

personalized customer experiences.

Successful digital offerings are created at the intersection of what technologies can deliver and what

customers want and will pay for. That point of intersection, however, has proved to be elusive. To find it,

companies must experiment repeatedly, cocreate with customers, and assemble cross-functional devel-

opment teams — and the insights gleaned along the way must be shared internally. This article explores

how several of the nearly 200 companies the authors studied have built and exercised these capabilities.

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How Algorithms Can Diversify the Startup Pool

Morela Hernandez, Roshni Raveendhran, Elizabeth Weingarten, and Michaela Barnett pp. 71-78

Some venture capital (VC) firms are starting to realize how much gender

bias can skew their decisions about which startups to fund — and the

negative impact that can have on their portfolios and investments. They

are embracing algorithms, artificial intelligence, predictive analytics, and

other quantitative, data-driven approaches in an attempt to making

smarter, fairer funding decisions. But so far, the effectiveness of these

tools remains an open question.

So the authors have set out to examine the extent to which such tools

and technologies really do help level the VC playing field for female entre-

preneurs. On the basis of their emerging findings, they are cautiously

optimistic. In this article, they explain how biases tend to creep into VC

decision-making when investors size up their prospects according to fac-

tors like fit and likability, why investors rely so heavily on gut instinct, and their tendency to passively

wish for a more diverse pool of candidates. The authors then describe some of the data-driven

approaches firms are using to tease out those biases, explore the opportunities and challenges of

algorithmic decision-making, and offer concrete recommendations that VC firms can use to mitigate

bias in the profiling of entrepreneurs who seek capital for startups. The goal: to help VC firms make

less biased, more quantitative investment decisions that serve both the firms themselves and the

entrepreneurs who need their funding.

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