AI in Your Contact Center: Understanding the Basics: Page 2 of 4
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Automatic Speech Recognition and Text-To-Speech
ASR/TTS, which provides the ability for the machine to translate speech into text and, conversely, translate text into speech, has been in common use for decades. For example, I implemented my first ASR/TTS solution in 1995, for Johnson & Johnson. For that use case, we trained a machine to recognize 60,000 different component numbers to provide the company better accuracy on phone orders. The bulk of the ML we had to facilitate went into addressing duplicate component numbers and component labels that used sound-alike letters and numbers (B, 3, G, Z...). Within weeks, we achieved 99% accuracy in identifying specific components.
Nuance Communications' Dragon Naturally Speaking speech recognition software, an example of a commercial ASR/TTS tool, has been around since 1982. This article is partially written -- i.e., dictated -- using it. To start, the software uses ML in a concept it calls "training." Basically, it provides you with some documents to read and it learns how your voice sounds as you form words that it already knows. Further, it learns every time you use it, and has the ability to read your emails and stored documents to learn how you write. If you have back or neck trouble from leaning over a keyboard all day, I highly recommend trying it out. But a word of caution: It spells every word correctly, but that doesn't mean it's always the correct word.
Robotic Process Automation
RPA is the use of computer scripts either to move data or process transactions that require multiple computer systems or to create functions within a computer system that aren't native to that system. Enterprises started spending money on RPA a couple years ago. It differs from scripting in several respects, most notably that it uses ML; has a centralized repository of business rules; and, in most cases, has the ability to use NLP to improve its utility.
RPA can be particularly useful in addressing CRM-related work, or extra work, in contact centers. A perfect example is using RPA to eliminate the manual copying and pasting of case notes between CRM and ordering systems. RPA is less prone to error, and allows contact center agents to focus on more rewarding work.
With ML, a robot has the opportunity to improve itself by recognizing new products and processes introduced into a customer engagement environment. When a new product shows up on an electronic purchase order, for example, the robot might not know what it is; however, it will know that the content of the field is part of a taxonomy that identifies it as a product, not a name or address. This makes the ML governance process easier. Further, under the right conditions, the robot with ML can process the purchase order for a new product without requiring its rewrite as a script.
A repository of business rules is useful when multiple RPA processes handle similar data and move this data to similar or identical information system platforms. For example, knowing that a known entity like an address always goes in the same place can ease deployment of new RPA solutions.
NLP comes into play on a regular basis with RPA. For a simple example, consider that electronic purchase order again. Just one typo means there's a good chance that a simple script will fail and the order will require manual processing. NLP has the ability to use "stock" or programmed language elements to mitigate the impact of a typo. Further, you could use NLP to summarize notes or exceptions described in plain English on the purchase order. A bot can accommodate shipping terms, delivery requirements, and special requests, but a scripting tool will only be able to flag an exception for manual processing.
Continued to next page: AI-based analytics