AI in Decision Making: Lessons from the Investment World
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AI promises to disrupt industries by providing management with an ability to drastically improve company-wide decision making. As a guide for implementing AI to improve decision making, it’s useful to look to investing, where the difference between a good decision and a bad one is billions of dollars. Each day investors must sort through endless amounts of data to settle on what they believe is the best investment. Below we’ll explore two investment companies that provide management teams in all industries a framework for making better decisions.
At an AI & Venture Capital (VC) event in January 2020 Madison Elkhazin of Georgian Partners spoke about how her VC firm has not only taken the approach of investing in AI-driven companies, but have developed internal AI capabilities as well. This approach allows a two-fold boost in Georgian’s investment strategy:
- They can use their AI capabilities to identify high-quality, high-potential companies that align with their investment mandate
- They have the ability to provide their AI expertise to portfolio companies, resulting in the potential for a greater return on investment
Elkhazin told the crowd that Georgian was one of the first VC firms to develop this approach and it has become table-stakes in the industry as other firms rush to follow. Looking at Georgian’s strategy, they clearly have an advantage. Being able to use large amounts of data and computing power to sort through potential investments faster than any number of human analysts is incredibly valuable. Warren Buffet says that the success of Berkshire Hathaway is attributed to their ability to immediately turn down investment opportunities that do not align with their mandate. This allows them to focus all of their time on the few opportunities that do. Good decisions are made when management can quiet the noise, limit the universe of potential options, and dig into the details of a limited number of high-quality possibilities.
Another investment company that has a track-record of great decision making is Bridgewater Associates- widely considered the most successful hedge fund in the world. In a 2017 Ted Talk, Bridgewater Founder Ray Dalio spoke about the role of radical transparency as part of their corporate culture. Each employee is expected to give candid and direct feedback to every other employee at Bridgewater. They are even encouraged to provide Dalio direct feedback. He tells the story of receiving an email after a meeting where an employee rated him a ‘D’ on his contributions to the meeting. Dalio says this not only ensures that everyone is able to receive and adjust to feedback, but also creates space where ideas can be properly challenged.
These two methodologies- narrowing the scope of possibilities and radical transparency- are valuable ideas that management can combine to develop a potent AI Strategy. Management must not only aim to make better decisions through the use of AI, but also provide an environment in which the algorithmic outputs can be challenged. To move above the rest, companies will need to focus on finding experts within their industry who also possess strong understanding of the technical details of AI. These individuals can identify where an AI model falls short and challenge its outputs the same way that Ray Dalio encourages employees to challenge him.
As AI becomes table-stakes not only in VC but across all industries, the competitive advantage begins to diminish. Companies who focus their efforts on hiring a new type of leader, one who deeply understands both industry and AI, will set themselves up for the returns previously only enjoyed by the Bridgwaters and Georgians of the world.
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Mitchell Johnstone
Director of Strategy
Mitch is a Strategic AI leader with 7+ years of transforming businesses through high-impact AI/ML projects. He combines deep technical acumen with business strategy, exemplified in roles spanning AI product management to entrepreneurial ventures. His portfolio includes proven success in driving product development, leading cross-functional teams, and navigating complex enterprise software landscapes.