The Difference Between Contact Center Automation and True AIFred Stacey
There’s a growing trend in call center technology that needs to stop: providers claiming to leverage true artificial intelligence (AI) only to restrict clients with rules-based methods that are actually just automation.
These terms may not seem all that different — but they most certainly are, especially if you’re paying for one while getting the other.
Luckily, we’re here to help clear up this common confusion. To practice, here are a few other comparisons that trip people up:
Great Britain: The geographical area comprising England, Scotland and Wales.
United Kingdom: Take Great Britain. Add Northern Ireland. Cheers!
Crocodile: Narrow, V-shaped snouts.
Alligator: Broad, U-shaped snouts.
Coke: Invented in 1886; most popular soda worldwide for decades.
Pepsi: What you drink when there isn’t Coke.*
Now that we’re warmed up, let’s dive farther further into separating automation from true AI.
COMMON CALL CENTER AUTOMATION TOOLS
Interactive Voice Response (IVR) Systems
Often providers will bill IVR – the automated telephony system that collects customer information and routes calls – as artificial intelligence. Not so fast. IVR systems require rules-based logic to determine next best actions, and they don’t self-optimize based on real-time factors.
For instance, a customer calls your support line and is greeted with a pre-recorded message, and a menu of options, such as “Dial one if you know your party’s extension.”
In this example, a true AI solution might ask if you know your party’s extension, and then be able to remember your answer for future calls, even pre-empting you to that extension.
IVR’s automate some of the work previously done by call center agents, thus saving time and boosting efficiency. But, nonetheless, they are not artificially intelligent.
Natural Language Processing (NLP) Systems
NLP systems, integrated to IVR solutions, have truly supercharged the ability to automate call center processes. Engagements that were formerly confined to touch-tone menus can now recognize common questions and contextual phrases across nearly every engagement.
Now, instead of listening to a directory of contacts, consumers can simply say, “problem with billing” and be directed to the correct agent.
While this is a huge improvement, it’s still not fair to call it true AI.
If an NLP prompt asks for your phone number and you say, “I think I’d prefer to communicate via email,” the system will likely default and ask the same question AGAIN.
A true AI solution would hurdle this obstacle and drive the engagement toward a resolution without defaulting among a rules-based structure.
Here’s how to know if it’s an automation solution:
TRUE AI TOOLS FOR CALL CENTERS
AI Agent Assistance
IBM and Google are two of the companies on the forefront of bringing AI call center technology into the mainstream, featuring AI assistants that understand complex help requests and can rapidly reduce response times.
Like Alexa or Siri, AI-voice assistants are being tailored specifically for industries and their customers.
AI call center agents are aiming high in capabilities, including sentiment analysis, which detects customer dissatisfaction or even sarcasm across call recordings and online reviews. No, the machines will not replace comedians nor humans for now. Realistic deployments of this are mostly being done at the largest companies, but some tech firms are working to scale these tools down to a more realistic price point for the upper mid tier market.
More realistic and adoptable tech in all segments are things like basic bots integrated with supervised learning, RPA tools to automate agent desktop applications and back office bots to convert unstructured data into structured data for formal processing.
Machine Learning & Predictive Analytics
When leveraging true AI, machine learning mines through mountains of customer data and produces faster, more responsive resolutions.
Because organizations rely on many systems to store customer data, AI providers have already launched solutions to connect disparate data and build predictive models for everything from improving medical diagnoses to thwarting cyber-bullying.
AI-based tools allow systems to learn from all collected data to inform future decisions.
For example, imagine purchasing a cup of coffee at 8:30am every day for a year. Now, one day, you randomly skip coffee and buy an energy drink at 4:30pm. In theory, an AI solution could signal a fraud alert due to the aberrant purchase, while an automated tool would require an “Energy drink = theft” logic programmed ahead of time.
Deep Learning & Neural Networks
This article does a great job of explaining neural networks, but the short definition: neural networks are brain models designed to recognize patterns and powerfully cluster/classify relationships across huge data sets.
Some Neural networks can detect voices, allowing systems to populate all relevant customer data to agents at the sound of a voice.
Neural networks can detect anomalies, allowing systems to recognize specific products or detect deficiencies from a simple image.
Neural networks stand apart due to their depth of insight. The deeper you layer a neural network, the more sophisticated they become.
Here’s how to know if it’s an AI solution:
This post originally appeared on the Cloud Call Center Search blog.
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