Featured Article from Software Licensing

Let’s face it: artificial intelligence is cool. Since we first encountered Isaac Asimov’s robots or Arthur C. Clarke’s HAL-9000 in fiction, we’ve imagined all sorts of wonderful (and some dark) scenarios about what would happen when computers began to “think” for themselves. Today, of course, artificial intelligence is not science fiction, and many of us have encountered it on smartphone apps, virtual assistants and chatbots. The question enterprises need to ask themselves now, though, is “How do we make money on AI on a large scale?”

According to a recent article by Jesus Rodriguez writing for CIO through the IDG Contributor Network, the path to monetization at scale is a combination of different factors including leveraging well-established assets like user or customer community as the main distribution mechanism; expanding the technology across different distribution channels; providing a compelling value proposition for prospects to become buyers; building network-effects into products so paying customers can attract other paying customers, and an appeal to either large enterprises or a mass of consumers.

There is evidence of a lot of optimism when it comes to monetizing AI as evidenced by the number of large technology players either building it or buying it through acquisitions. IBM, Microsoft, Amazon, Apple (News - Alert), Facebook, and Google have all been releasing solutions in the AI space. Most notably, IBM has its Watson supercomputing platform, Microsoft has its Cortana offering, and Facebook is going large by licensing Facebook Messenger technology. It’s Google (News - Alert), however, that more industry analysts are watching.

“In the short term, Google has a bit of an edge and more options to quickly monetize A.I. at scale,” wrote Rodriguez. “Google has been investing in A.I. across different technology areas such as mobile, cloud, DeepMind, and even chips, [and] we can’t underestimate the power of Google Search as a distribution channel.”

Then, of course, there is Google’s self-driving cars, which use a variety of technologies, not the least among them artificial intelligence. But it may be the Internet of Things (IoT) that really picks up AI and runs with it. And, as Mark Jaffe of Prelert wrote recently for Wired, IoT simply won’t work without artificial intelligence.

“IoT will produce a treasure trove of big data – data that can help cities predict accidents and crimes, give doctors real-time insight into information from pacemakers or biochips, enable optimized productivity across industries through predictive maintenance on equipment and machinery, create truly smart homes with connected appliances and provide critical communication between self-driving cars,” he wrote.

As more devices and sensors become connected to the Internet of Things, it will take computer intelligence to sort out the data and determine what’s relevant and what’s not. It’s also likely the only way to make the kinds of connections to prevent failures and improve efficiencies that IoT technology is valued for. Simply put, finding intelligence in machine data requires the intelligence of a machine that can learn.

The place where AI meets IoT to cut through the noise of unfathomable quantities of data and make connections is one of the places where AI will find the most value on a large scale.