Pepper, the humanoid robot, holds a tablet device at a tech fair. Krisztian Bocsi | Bloomberg | Getty Images

Creating some artificial intelligence programs is easy, but turning them into successful businesses is a challenge, experts said this week in Singapore at the annual Innovfest Unbound tech conference. Improvements in computing power and better availability of data have sped up developments in AI, with large corporations, start-ups, universities and governments all getting involved. Earlier this year, the International Data Corporation predicted that worldwide spending on cognitive and AI systems will grow to $52.2 billion in 2021, up from about $19.1 billion this year. Since a lot of the basic computer codes used to create AI are widely available, it's easy for developers to come up with programs that can perform certain tasks: For example, a chat bot can take over the basic tasks done by a customer service representative or a program can be developed to scan through hundreds of medical images for irregularities that can save doctors a lot of time. But that also means many companies are using the technology in areas that do not really require an artificially intelligent program.

Hard to make money

"I would argue that AI is overkill for the majority of use cases," Drew Perez, chief executive officer at Adatos, said during a panel discussion and added that the benefits of AI are not instantaneous in many cases. His start-up uses AI to study satellite and drone images of agricultural lands to assess things such as tree counts, soil conditions and plant health. "At the end of the day, if you think about it, it has to have a return on investment." Perez told an audience that, most AI programs today are in the "lab phase or innovation phase." He explained that even if there are the right conditions — including having the right amount of computing power, sufficient data, the right mix of talent and a culture that readily embraces AI — profitability is not guaranteed. "You might go for a whole year and find out at the end that, for a million dollars, I'm going to make a hundred thousand [dollars]," he said, explaining that short-term plays in the stock market, for example, could likely give a better return on investment. Unless a company is able to figure out an application for their AI program that can bring in "hundreds of millions, it's just a lab experiment," he said. Monetizing an AI program is only a problem if it fails to address an important problem, according to Steve Leonard, founding chief executive officer at Singapore-based start-up accelerator SGInnovate. "Some people say, 'Oh, I'm having a hard time monetizing.' For me, the first question is what led you to work on that problem, so if you're having a hard time monetizing, it must mean that you didn't have a problem that was sufficiently painful for somebody to take an action," Leonard told CNBC.

Data can be expensive and hard to get

While building an AI program may not be a significant barrier, companies need a lot of high-quality data to train that algorithm. That, according to Leonard, is both a challenge and an opportunity. It's an opportunity because large volumes of data can be used to train and improve what those AI programs can do in a more efficient manner. For example, an algorithm could study millions of medical scans of the human brain to learn about irregularities and automatically detect them in future images. But the challenge is to make sure people feel comfortable with using that data, Leonard said, noting that making the information anonymous is oftentimes beneficial. He explained that a government's ability to provide high-quality data is defined by its citizen' comfort and appetite to share that information. Another challenge is in cleaning up data sets they can then be used to train AI programs. Two years ago, IBM estimated that the cost of poor-quality data was about $3.1 trillion a year in the United States alone. Bad data can be expensive because cleaning it up usually takes a lot of time and effort and so that makes it harder for companies to be immediately profitable.

Regulatory balancing act