The head of human resources said to me, “We need to become more agile. We’re not lean enough. I want to see our culture shift to ‘fail fast, fail often.’”
It was a great moment. For me at least.
In my head, I was playing buzzword bingo, and with the use of “Agile,” “Lean,” and “fail fast, fail often,” I had just scored a perfect game. But it’s a game I was not looking to win.
When leaders do not fully understand or appreciate a term, the result can have the opposite effect of what they wish to achieve.
On Sept. 15, 2008, a credit crunch turned into a full-blown crisis when New York-based investment bank Lehman Brothers collapsed. The global recession that followed is still too fresh in many people’s memories to be considered history. But 10 years on, the state of the financial system suggests that the crisis has been relegated to the history books for many in the industry.
In 2018, Wall Street is enjoying another heyday. Bonuses for bankers have returned to pre-crisis levels, profits for commercial banks are at a record high, the stock market is in its longest bull run in history, the US economy is humming, and deregulation and tax cuts rule the day in Donald Trump’s administration.
New Bankrate research shows that Actuarial Science is “the most valuable” major you can study in college out of 162 total, with a whopping average income of $108,658 to go along with an unemployment rate of only 2.3%.
Wondering how the site arrived at these results? The methodology was multi-layered, but the company evaluated the latest information featured in the U.S. Census Bureau American Community Survey, among other points. The majors had “labor forces of at least 15,000 people,” and the number of grads with “a higher degree” was also considered.
This fall, 19.9 million college students will be traveling to college campuses across the United States to start a new school year. There are over 4,000 colleges and universities in the United States, but Harvard Business School professor Clayton Christensen says that half are bound for bankruptcy in the next few decades.
Christensen is known for coining the theory of disruptive innovation in his 1997 book, “The Innovator’s Dilemma.” Since then, he has applied his theory of disruption to a wide range of industries, including education.
In his recent book, “The Innovative University,” Christensen and co-author Henry Eyring analyze the future of traditional universities, and conclude that online education will become a more cost-effective way for students to receive an education, effectively undermining the business models of traditional institutions and running them out of business.
Machine learning software and artificial intelligence have come a long way since their inception – and is only continuing to intensify. Taking over many industries, AI is swiftly changing the way professionals go about their business. So, what does this mean for marketing?
Artificial intelligence (AI) has come a long way since its inception. The rise in AI-powered marketing is taking the load off many marketers, and delegating to machines, allowing marketers to refocus their efforts onto marketing that matters and giving marketers more time to address any challenges that come their way.
If you are unhappy taking orders from your human boss, you might be more inclined to take orders from robots, according to a new survey.
The AI, machine learning, and data science conundrum: Who will manage the algorithms?
There seems to be a large gap between the way people are using artificial intelligence (AI) at home and at work. Although almost three quarters of us use AI in our personal life, only six percent of HR professionals are deploying AI and only one in four (24 percent) of employees are currently using some form of AI at work.
Forbes Insights research shows that 65% of senior transportation-focused executives believe logistics, supply chain and transportation processes are in the midst of a renaissance—an era of profound transformation. But of the most visible forces of change, perhaps none carries more potential for innovation and even disruption than the evolution of artificial intelligence (AI), machine learning (ML) and related technologies.
AI, ML and associated technologies promise to enable leaders to focus IoT and myriad other data feeds on achieving greater optimization and responsiveness across the whole of their logistics, supply chain and transportation footprint.
The government is pumping funds into research, education and innovative projects, as Chinese tech firms flourish and investors and venture capitalists flock.
With rising production costs, an ageing population and shrinking return on investments it is clear why China’s economy has shifted from labour-intensive manufacturing to an innovation-driven paradigm in just a few years.
Today, Huawei is the largest telecommunications equipment manufacturer in the world and JD.com, Tencent, Alibaba and Baidu are among the world’s top 10 internet companies in terms of revenue. These companies, and the new tech-based businesses seeking to emulate their success, have all benefited from the “innovation ecosystem” China is developing.
So what are the key elements that make up this ecosystem and have enabled China’s economy to rapidly climb the value chain?
Research shows that office workers cannot concentrate at their desks.
Have you seen any of these gimmicky office designs? Candy dispensers in conference rooms. Hammocks and indoor treehouses. Tech companies tend to be the worst offenders with the startup favorites: beer taps and table tennis.
Maybe there is fun for a moment when the candy bar drops — but does all that money spent on gimmicks deliver anything meaningful for the people who work there?
Fifteen years after Billy Beane disrupted Major League Baseball by applying analytics to scouting, corporations are rewriting the rules of recruiting.
The online games were easy–until I got to challenge number six. I was applying for a job at Unilever, the consumer-goods behemoth behind Axe Body Spray and Hellmann’s Real Mayonnaise. I was halfway through a series of puzzles designed to test 90 cognitive and emotional traits, everything from my memory and planning speed to my focus and appetite for risk. A machine had already scrutinized my application to determine whether I was fit to reach even this test-taking stage. Now, as I sat at my laptop, scratching my head over a probability game that involved wagering varying amounts of virtual money on whether I could hit my space bar five times within three seconds or 60 times within 12 seconds, an algorithm custom-built for Unilever analyzed my every click. With a timer ticking down on the screen . . . 12 . . . 11 . . . 10 . . . I furiously stabbed at my keyboard, my chances of joining one of the world’s largest employers literally at my fingertips.
More than a million job seekers have already undergone this kind of testing experience, developed by Pymetrics, a five-year-old startup cofounded by Frida Polli. An MIT-trained neuroscientist with an MBA from Harvard, Polli is pioneering new ways of assessing talent for brands such as Burger King and Unilever, based on decades of neuroscience research she says can predict behaviors common among high performers. “We realized this combination of data and machine learning would be hugely powerful, bringing recruiting from this super-antiquated, paper-and-pencil [process] into the future,” explains Polli, sitting barefoot on a couch at her spartan office near New York’s Flatiron District on a humid May morning, where about four dozen engineers, data scientists, and industrial-organizational psychologists sit behind glowing iMacs.