Can an Algorithm Teach Leadership?

HBR IdeaCast

Harvard Business Review

Leadership, Entrepreneurship, Communication, Marketing, Business, Business/management, Management, Business/marketing, Business/entrepreneurship, Innovation, Hbr, Strategy, Economics, Finance, Teams, Harvard

4.4 • 1.9K Ratings

🗓️ 24 May 2012

⏱️ 15 minutes

🔗️ Recording | iTunes | RSS

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Summary

Marcus Buckingham, founder of TMBC and author of "StandOut."

Transcript

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0:00.0	If you work with early career professionals, my colleagues at
0:03.8	HPR have a great new podcast for you. It's called New Here. Think of it like the
0:08.4	Young Professional's Guide to Building a Meaningful Career on your own terms.
0:11.9	Share New Here with the Young Professionals in your life. a meaningful career on your own terms.
0:12.8	Share new here with the young professionals in your life.
0:15.9	Listen for free wherever you got your podcasts.
0:18.6	Just search new here. Welcome to the HBR Ideacast. I'm Julia Kirby. Today I'm joined by Marcus Buckingham, best-selling
0:37.0	author of First Break All the Rules, Now Discover Your Strengths, and most recently, Stand Out, the Groundbreaking New Strength Assessment
0:46.2	Marcus, it's great to have you here today. Welcome
0:49.1	Thank you for having me Julia. So the name of your article in the June edition of Harvard Business Review is
0:56.5	Leadership Development in the age of the algorithm. Why don't we first
1:01.6	just tease out the meaning of that? What do you mean by the age of the algorithm?
1:06.0	Well, we're living in a world where most every content provider you can think of, from people providing ads to people feeding you music content or book
1:17.0	content or film content anyone providing content the first question they're asking is, who are you? So Facebook knows all
1:25.8	about who you are and figures out what you like, what you don't, what you're into and then
1:30.2	delivers to you ads that fit you. Spotify does the same with music.
1:34.1	Netflix, before they do anything,
1:36.0	they ask you a whole bunch of questions
1:37.3	about what movies you've seen and what movies you've liked.
1:40.2	And as a result of that, it builds an algorithm which then suggests that you'll probably like these movies.
1:46.0	So everywhere you look in the consumer world, content providers have realized that they've got to figure
1:51.6	out who you are before they start
	...

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