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Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. Put another way, AI technologies are algorithms that attempt to mimic things that humans do. Machine Learning, on the other hand, is the science and engineering of giving computers the ability to learn without being explicitly programmed — algorithms that learn from data. AI and Machine Learning are often discussed in conjunction with one another, but it is important to note that not all AI techniques use Machine Learning, and Machine Learning is also used for other things besides AI, such as decoding genetic sequences.

One of the key applications of AI is to combine these technologies — speech recognition, natural language understanding, dialogue management and so on — to create Intelligent Assistants. Intelligent Assistants are interactive systems that can communicate naturally with humans and assist them in accessing information and completing tasks. Most of the Intelligent Assistants that people are familiar with today are consumer facing and somewhat general in purpose. Siri, Cortana, and Google Now, for example, are Intelligent Assistants that make it easier for a consumer using a phone, tablet, or other device to get things done. The majority of what you can do through these assistants was already possible through the graphical interface of the device, but the Intelligent Assistant enables a single point of entry to perform a broad range of different tasks. Much the same argument applies to enterprise care. Customers need to interact with a business to fulfill numerous different types of requests such as account access, billing, sales, troubleshooting, and so on. An enterprise Intelligent Assistant powered by AI technologies can provide a simple and consistent point of entry to access numerous different services that get lost in a complex graphical interface or the structured voice menus that are typical of interactive voice response systems (IVRs).

As we currently see with most traditional IVRs, automation is often used to route a customer to the right agent or queue. Moving beyond that, automation can also be used to handle self-service transactions. Here, the best place to start is with high volume transactions that already have the appropriate back office APIs that can be easily integrated with the AI solution. From there, companies should move into areas such as technical support, interactive troubleshooting, and interactive sales.

Machine learning in its simplest form will automate repetitive tasks. Data collection, sorting, entry and transformation can all be automated, saving your business crucial time and resources. In a more refined form, it’s able to tell you where and how your business is being successful, and make predictions regarding your businesses’ future.

Once the final model is chosen and implemented the results are tracked to monitor the performance over time and help you find patterns and trends to answer important business questions. Similarly to costs, the timeline depends on the results you are after. A simple machine learning programme designed to automate data processes could take a couple of weeks to develop, depending on the size of the development team, and would be able to provide results almost instantly. On the other hand, a system that works with structured data such as huge databases and some unstructured data for example social media could take multiple months to develop and test to find the most efficient machine learning algorithms to answer your business questions.