The mathematical corporation: A new leadership mindset for the machine intelligence era

Description

In order to compete and win, companies of the future need to adopt a completely new business model that fully integrates people and computers. Humans must bring creativity and ingenuity to high-level cognitive tasks, while intelligent machines perform high-level work that complements human efforts. As a result, executives will have to rethink not only their employment models and the very nature of how work is done but also how they lead, create competitive advantage, and help their teams and companies evolve over time. Stephanie Beben shares insights from her work with senior leaders at pioneering companies, government agencies, and nonprofits who are implementing this cultural shift and becoming “mathematical corporations”—organizations that successfully partner human and machine intelligence to drive breakthroughs. You’ll learn strategies (pulled from case studies) for leveraging the human-machine partnership, which include considering complexity a boon, not a burden; acknowledging that the machine works better than the gut; accepting that machine models top mental models; understanding that solutions might not require logic; creating value by giving it away; looking outside your industry to create algorithms; and prioritizing imperfection and experimentation.

Case studies include:

• Ford Motor Company: CEO Mark Fields steered the company away from customer surveys (based on a sampling of reality) and toward the collection and analysis of sensor-collected data from all customers (capturing actual customer behavior). The result is actionable insight into everything from mobility, customer experience, and valued features to safety, emissions, and fuel economy.
• InterContinental Hotels Group: One of the largest hotel chains in the world, with 11 brands and nearly 5,000 hotels, used machine intelligence to review 18 million records to identify and predict complex customer behavior, allowing the company to individually tailor its customer rewards programs.
• GlaxoSmithKline: The pharmaceutical complany partnered with health information startup Epidemico to trawl Facebook and Twitter for mentions of adverse side effects. Social media mentions of adverse events in one year prompted the recall of one GSK product—and exceeded all those in the Food and Drug Administration database since its inception in 1968.
• Merck: The company optimized its vaccine yields by conducting a large-scale integration and analysis of five terabytes of data using 15 billion calculations and more than 5.5 million batch-to-batch comparisons. This allowed it to pinpoint the true culprits behind discarded batches in the manufacturing process.
• The US Census Bureau: The Census Bureau is implementing an innovative data-science-driven mobile application for the 2020 census that will reduce costs and eliminate thousands of hours of travel by enumerators.
• Tesla: Tesla’s vehicles include software that aids customers’ limited self-driving. Another set of software records all driver behavior, helping Tesla accumulate the data to build future full-service self-driving capability. Whereas it took Google six years to gather a million miles of actual self-driving car data, Tesla’s 70,000 cars produce a million miles of driver data every 10 hours. Who will succeed first in using the data for the next breakthrough?