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Straight Talk On Artificial Intelligence and Machine Learning: How They Can Help Your Business

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Artificial intelligence (AI) promises unlimited potential for improving the way we do business and enhancing our overall quality of life. The term machine learning is frequently used as an equivalent for AI, but it actually represents a subset of its capabilities. What is clear is that AI is here to stay, and its use will continue to grow exponentially, especially in the area of Content Services. That’s why I am so pleased to have DocuWare President Dr. Michael Berger explain AI’s practical application in digital document management and workflows. His Forbes article, reprinted below, demystifies this often misunderstood, and sometimes downright baffling, technology.

–Joan Honig, Content Marketing Manager

Artificial intelligence (AI) and machine learning (ML) are big buzzy terms making more noise now than ever. These technologies are a big part of my everyday life, both as a tech leader and as a civilian, so I would like to share my experience on how I use them to further growth, retain staff and enhance the customer experience.

AI and machine learning are not the same

In order to highlight the true potential value of both, it is important to know how machine learning is different from artificial intelligence. Even though both terms are used interchangeably, they aren't the same, and breaking them apart helps to understand their unique importance.

iStock-1042692662Artificial intelligence is a blanket term that represents any type of technology that helps solve convoluted issues in a human-like manner. It performs tasks commonly associated with intelligent (human) beings and was developed to exhibit the characteristics of humans, including the ability to generalize, reason, uncover meaning and learn from mistakes.

Machine learning, on the other hand, is a subset of AI technology. It is the part that provides systems with the ability to automatically take in information and learn new information without being programmed. ML's primary point of emphasis is on the development of computer programs that can take data and use it to learn for themselves by creating self-learning algorithms.

Machine learning analyzes data then uses this “knowledge” to allow the machine to make the correct decision, thus eliminating or minimizing human intervention. This is a huge, time-saving value to any business! When the machine can use the data to step in and make the decision without needing human help, the end result is increased business productivity -- the machine can work faster and the humans can direct their attention to tasks that machines can’t do.

Getting started with machine learning

The first step in getting started with ML is to analyze the amount of data your organization gathers and the way you use it. So, for example, if your organization gathers data on a scale similar to a government agency or the health care and transportation industries, ML is the best tool to make sense of this information.

iStock-1072273650All businesses ultimately gather data so that it can be used for the betterment of the business, employees and customers. At my organization, the data we gather gives us information such as our customers access 75 GB worth of stored documents daily, the service industry is the fastest adopter of our product and 56% of our cloud customers use our workflow manager.

We gather and receive these vast amounts of data from multiple sources and must find the best way to mine it. This is where machine learning comes in -- it crunches the data, allowing for the identification of patterns, red flags and trends so that we can plan correctly and determine the most efficient way to operate moving forward.

But if your business doesn’t deal with big data, ML technology can still help by transforming manually heavy processes into lighter ones in order to offer a better customer experience. We all love those online forms that predict what we are going to type next? What a time-saving luxury it is not to have to punch the same keys over and over and over -- not to mention the reduction in human error. This type of “autoindexing” mimics previous behavior and data entered. It learns and makes knowledge-based suggestions. This is my favorite example of this technology at work for the business, it’s employees and customers. When customers have a good experience, they have positive things to say about an organization.

Using AI to sell intelligently and retain your employees

When used intelligently, artificial intelligence provides us with a deeper understanding of our customers even across different contexts and channels.

iStock-966248982AI can predict the product a customer is going to buy before they buy it. We all encounter this when we order something on Amazon. It is undeniably handy when the website “recommends” items to add to a cart. Every business owner with a product to sell should be using AI in this way.

Let’s take this one step further. AI has the capability to read signals and sense each customer’s unique intent to purchase, upgrade or cancel. Powered by real-time data, AI can even guide customer service and sales representatives to make the right offer at the right time. The human touch paired with human intelligence and powered by AI is a kind of technological magic that will help your company understand your customers better and help personalize their experience in Amazon-like fashion.

Employee retention is also a key where AI can thrive. HR professionals can use this technology to help them see who is planning on leaving. Companies such as IBM are using this knowledge to get in front of valued employees and negotiate, counteroffer and hopefully retain their services. Using technology to analyze the data points and determine who is a flight risk saves every business valuable time and money.

Using machine learning to transform a manual data entry process or AI to accurately predict a sale or retain a valued member of a team make up what in my experience offer the most return on investment when investing in this technology.

Editor's note: This article was originally published by the Forbes Technology Council.