Machine learning (ML) and artificial intelligence (AI) weren’t always the hot buzzwords they’re known as today. But over the past decade, business leaders and entrepreneurs have come to realize the importance of these applications and technologies for their businesses to become — or stay — relevant.
“AI and machine learning was not sexy,” said Jesus Mantas, Managing Partner and General Manager of IBM Business Consulting, who wrote his academic thesis about machine learning some 26 years ago. “It was considered fringe then. You didn’t bring it up at parties.”
Now, the concept and importance of machine learning had its own symposium at UNC-Chapel Hill’s Kenan Center on Friday. The event brought together academic, policy and business leaders to discuss and learn about the realities of machine learning in the modern business world.
The Rethinc. Machine Learning Symposium was hosted by the Kenan Institute of Private Enterprise and Infinia ML, the Durham-based startup that provides machine-learning solutions for business and government clients. The two entities have formed the Rethinc.ML initiative to help develop real-world cases for machine learning in business.
The symposium kicked off with a reception and then moved into introductions by the CEO of Infinia ML, Robbie Allen, who is also an adjunct professor and research fellow at UNC’s Kenan-Flagler Business School. After the introductory remarks, Reetika Fleming, the Research Director of Insurance & Smart Analytics for HFS Research, spoke of the current state of machine learning in businesses.
Fleming gave advice to business leaders on how to infuse machine learning to their company’s best competitive advantage. She also gave insight into a survey she helped conduct that revealed businesses had some mis-perceptions about adopting machine learning. That’s particularly true when it comes to managing potential dislocation to employees, given that automating processes with machine learning often makes some workers superfluous.
“It’s interesting that when we ask companies what they plan to do with their workforce when enhancing machine learning, a confusing two-thirds think they’re going to retrain their employees,” Fleming said. “Unfortunately this is unlikely because these are highly technical skills.”
However, she also gave advice on how companies should develop their corporate strategy and workforce into six dimensions that includes tech, data, talent, methodologies, culture and outcomes.
Keynote speaker Scott Frederick, partner and head of business development and federal at New Enterprise Associates (NEA) — one of Silicon Valley’s oldest and largest venture capital firms — explored the flipside of machine learning by looking at the current state of the innovation ecosystem through the lens of the venture industry.
Frederick emphasized the importance of technology and disruption, which is putting many decades-old companies out of business. A Fortune 500 company used to have a lifespan of over 50 years, but now, “the average lifespan of a Fortune 500 company is 16 to 17 years,” Frederick said.
Historically, the world’s largest and most stable companies didn’t have a high turnover rate, which points to a shifting paradigm in what the market values. Frederick said that AI and machine learning aren’t going anywhere, and it’s important that more mature companies make an effort to stay relevant — or they’ll be consigned to the corporate graveyard by their more entrepreneurial brethren.