Is Artificial Intelligence an Intelligent Move for Businesses?

If there’s one thing I’ve learned over the past 30 years selling technology to enterprise businesses, it’s that excitement over new and emerging technologies will always, always outpace the understanding of how those technologies can improve your business.

Time and time again, I’ve witnessed as CTOs and IT Directors trip over themselves rushing to get the hottest new technology implemented and burning budget on buzzwords a few years out from making any kind of meaningful impact on their bottom line.

But for successful enterprise businesses, ‘buzzwords’ aren’t a line item in the budget, nor should they be. And whether it’s blockchain or the Internet of Things or robotics, no business should be throwing money at any tech solution without having a clear understanding of what problems the technology will solve.

But there isn’t a technology that’s more misunderstood today than Artificial Intelligence—it’s a term that’s thrown around a lot by technology consultants with dollar signs in their eyes or lazy bloggers looking to capture search traffic to describe everything from data and analytics to business intelligence to standard reporting. The current trend to attribute everything to Artificial Intelligence does a great disservice to the actual capabilities that AI can and will provide, and may actually sour companies on the technology before it truly realizes its potential.

In this blog series, I’m going to be demystifying AI for business leaders—explaining the current concepts and applications, looking at the business problems enterprises think they can solve with AI, and finally whether AI is even the right solution for the problem you’re trying to solve. But first, let’s start here: what is Artificial Intelligence, exactly?

‘Artificial Intelligence’ is, at its core, training machines to replicate the functions of the human brain—giving them the opportunity, not only to parse large amounts of data, but to iteratively learn from new data sets and react appropriately to new stimuli. Over the past few decades, AI has evolved to cover a number of different disciplines, each of which could, feasibly, be applied in your business—depending on the problems you’re looking to solve.

Natural language processing (NLP): Natural language processing is focused on improving communication between humans and machines, by analyzing human language inputs (either via direct messages, online comments, or searches) and providing outputs (either analysis or generating a response.) Think of voice assistants like Microsoft Cortana or Siri, or the voice technology underpinning voice-controlled devices like Amazon Alexa or Google Home—these services use NLP to analyze, parse, and react contextually to voice cues.

Vision systems: Vision systems are capable of analyzing and interpreting visual images, such as aerial photographs, medical imaging, or product labels. Facebook’s facial recognition photo tagging is an example of a vision system.

Machine learning: Machine learning, at its core, is the process of getting computers to learn and act like humans by responding to variable data inputs. Rather than explicitly programming computers to provide a specific answer, algorithms are applied to layers of data that map variable inputs to variable outputs, and gives the computer the opportunity to learn progressively each time they execute a task.

AI planning and scheduling: The planning and scheduling branch of artificial intelligence primarily applies to autonomous machines such as industrial machinery, autonomous cars, or robots. To be able to successfully interact with the physical world, these devices must be able to observe the world through different types of sensors and perform actions based on those observations.

Before you take on the enormous expense and overhead of AI configuration and deployment, you need to ask yourself—is this really what you’re looking for? Don’t buy the buzzword. Buy the benefit. Do you need Natural Language Processing or Machine Learning to solve a business problem? Will implementing Vision Systems or AI scheduling make you more competitive ? Or are there existing, less buzzy technologies that can help you sooner, for cheaper?

Follow along as I’ll answer these questions and more in our next post.