Despite physicist Stephen Hawking's warnings that artificial intelligence “could spell the end of the human race,” AI projects may be entering a golden age of financial support. Between 2010 and 2014, private investment in AI grew from US$1.7 billion to US$14.9 billion, according to the Institute for the Future, a U.S.-based research organization.
AI is “in everything you use now,” says Angie Lienert, PMP, CEO of software development firm IntelliGenesis LLC, Columbia, Maryland, USA. The firm provides intelligence analysis and network operations services to the U.S. government. “It's in our cars, it's in our phones, it's on our computers.”
Artificial intelligence involves creating computers or software capable of tasks such as planning, reasoning, learning and problem-solving.
Between 2010 and 2014, private investment in AI grew from US$1.7 billion to US$14.9 billion.
Source: Institute for the Future
This year, investment in the field is projected to grow nearly 50 percent, bolstered by an influx of funds from tech giants including Google, Baidu, Amazon and Facebook. (Facebook recently announced it will open its first AI research center outside of the U.S. this year, in Paris, France.) While high-profile projects like Microsoft's emotion-detecting eyeglasses and JPMorgan Chase's dream of AI investing software garner media attention, the steadily decreasing costs of hardware required to develop AI projects means more market opportunities for smaller organizations. And that increases the need for project managers with an AI background.
Unique Complications
The deluge of AI projects has brought an increase in concerns about data privacy as a vast amount of information is collected from and about users.
Another big challenge is the amount of up-front research required to get AI projects off the ground compared to other technology projects, says Attila Bódogh, PMP, CEO of Xdroid, Budapest, Hungary. His firm uses artificial intelligence to develop speech and data analytics software used mainly in call centers. Although a crucial way for organizations to stay ahead of the tech curve, research can be costly and there's no guarantee it will pay off, he says. “There's uncertainty to the customer and to the project owners,” Mr. Bódogh says. “It's a great frustration.”
And because artificial intelligence is a new realm for many stakeholders, setting customer expectations can be tough, Ms. Lienert adds.
“Sometimes when you get into the nitty-gritty details, it doesn't quite take you where you expect it to,” she says. “Sometimes you have to tell your shareholders, ‘Look, this is the direction that we're going. We can continue down this road, but we found another area that's pretty interesting.’”
AI projects also must grapple with a scarcity of talent. As technological advances accelerate, finding team members with up-to-date AI expertise can be so tough that smaller companies like Xdroid opt to partner with research companies rather than hire on their own. Other firms, including IntelliGenesis, foot training costs themselves.
AI is “in everything you use now. It's in our cars, it's in our phones, it's on our computers.”
—Angie Lienert, PMP, IntelliGenesis LLC, Columbia, Maryland, USA
Whither the Worker?
External stakeholders are also concerned about how AI products will impact the job market. Sophisticated AI systems are working their way into traditionally white-collar fields including medicine, finance, marketing, journalism and law. Research from the University of Oxford estimates that as much as 47 percent of American jobs could be automated by 2033, a huge shift in the economy that would potentially leave millions unemployed. Some experts argue that AI won't so much obliterate the job market as disrupt it, forcing people to reinvent themselves.
Regardless of the impact AI projects eventually have on the workforce, some organizations and project managers have had to address employees’ fears. Successfully responding to this stakeholder challenge is tougher than building an algorithm that can predict human behavior, Ms. Lienert says. People need to “understand that we're not trying to replace people, that we're trying to get the software to the point where it can make [employees] more effective,” she says. “There's no reason that software and other things can't handle a lot of the menial tasks so that people can get to a higher level of analysis and efficiency.” —Christina Couch