Li Deng has been working with artificial intelligence and machine learning for nearly a decade.

When Lori Beer, the recently-installed chief information officer of J.P. Morgan's corporate and investment bank, started out in 1989, technology was much more of a man's world than it is today. More often than not, she was the solitary woman surrounded by team of male nerds. Fitting in, she says, is not the best thing to do. "My advice would be to stop trying to be like one of the guys," she says. "I spent years being the only woman on predominantly male technology teams, but the best thing you can do as a woman in technology is to stay focused on the value you can bring as an individual. Tech teams need diversity of perspective."

AI is having a huge impact on trading, wealth management and asset management. When I first graduated from Stanford, I'd tell people I have a degree in artificial intelligence and they would reply, "so, you are a science fiction writer?" I would then explain that I was an engineer who studied linguistics, cognition, statistics and algorithmic problem-solving. Today, AI is hot topic, and the entire financial industry is trying to find ways to leverage it. After a decade in quantitative finance, I decided to return home to AI and build a new kind of wealth management firm – even different from other robo-advisers.

Forget siloed quant teams, overworked developers or the superiority of the human mind, there's one big impediment to artificial intelligence really taking off in financial services – the current staff. If AI is to gain traction in financial services, it will need to vanquish the army of financial services professionals standing in its way. At least, this is the conclusion of Raffaele Savi, a managing director and head of developed markets within BlackRock's scientific active equities team. "People are problematic, machines are easy," he said, speaking at the Newsweek conference on artificial intelligence and big data last week. "People who understand finance and economics don't think the same way as people who know a lot about computing and machine learning. We need to work together as an industry. AI is where the bulk of alpha will be on a longer term horizon."

Machine learning is leading to innovation in algorithmic trading, among other areas. Xi Chen, who got his Ph.D. in machine learning from Carnegie Mellon University's School of Computer Science, is an assistant professor of information, operations and management sciences at New York University's Stern School of Business. He was recently named to the Forbes 30 Under 30 list. Chen featured in the science rankings, but he's working on machine learning products that will disrupt the financial services industry. First of all, modern machine learning techniques will lead to more accurate predictions of the future prizes and trends in financial services.

Citadel is building its team in London. Christian Hesse, who until this month was working in a hybrid sales trader and quant role at Deutsche Bank, just joined the hedge fund as a quantitative researcher. Hesse has a PhD in Cognitive Science from Birmingham University, which is not unusual for a quant. More interestingly, however, he spent the first seven months of his banking career in 2009 working as a'scientific employee' at Deutsche Bank's secretive Quantitative Products Laboratory in Berlin, which is focused on machine learning projects. As well as building in equities, and hiring from Goldman Sachs to populate the new team, Citadel has acquired Citi's Automated Trading Desk Division – which was a pioneer of high frequency trading – and has also been hiring quant talent for this division.