The mathematical corporation: A new leadership mindset for the machine intelligence era
How can you most effectively use machine intelligence to drive strategy? By merging it in the right way with the human ingenuity of leaders throughout your organization. Stephanie Beben shares insights from her work with pioneering companies, government agencies, and nonprofits that are successfully navigating this partnership by becoming mathematical corporations.
Talk Title | The mathematical corporation: A new leadership mindset for the machine intelligence era |
Speakers | Stephanie Beben (Booz Allen Hamilton) |
Conference | Strata Data Conference |
Conf Tag | Big Data Expo |
Location | San Jose, California |
Date | March 6-8, 2018 |
URL | Talk Page |
Slides | Talk Slides |
Video | |
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: