Updated: 7th January 2020
AI vs ML
Technology is evolving faster than ever before. There are so many trends to stay on top of and things you need to know right now, so how can you keep up? That’s where we come in. This page covers nearly everything you need to know about artificial intelligence (AI) and machine learning (ML). We also dive into how you can use AI and ML to deliver the best-in-class service your customers deserve. You’ll leave smarter. And your customer experience will be more inspired. We swear.
Artificial intelligence (AI) is a wide-ranging branch of computer science that aims to replicate or simulate human intelligence in machines. Think of it as an umbrella of sorts. The goal of AI is to build computer systems capable of mimicking human tasks that require intelligence. AI requires free cognitive thought (i.e. a human brain)... and clearly that doesn’t exist yet. When we talk about AI today, we’re really talking about machine learning. We feed patterns into computers and those computers find mathematical matches to those patterns like they always have.
There are many technologies that fall underneath the broad umbrella of artificial intelligence, with machine learning (ML) being one. ML is an application of AI that gives systems the ability to learn and improve with each experience, relying heavily on data and natural language processing. ML accomplishes specific tasks by processing large amounts of data, recognizing patterns, and adjusting the response. ML offers top-notch emotion detection and data analysis capabilities, including customer sentiment analysis and speech analysis. A common use case for ML is the implementation of smart bots, also known as chatbots.
NLP is a subfield of linguistics, computer science, information engineering, and AI that centers around the interactions between computers and human languages. Simply put, NLP is what happens when computers read language. NLP uses natural language understanding (NLU) to turn text and voice into structured data that can be understood by machines, enabling certain tasks to be taken off of human hands. Smart bots utilize NLP and NLU to process and analyze natural language data, review data and determine which questions to ask and what prompts to deliver. Today’s bots understand human language--both written and spoken--to identify customers, understand their needs, respond to requests, and route them to the appropriate person when necessary within your contact center or business.
Still confused? Don’t worry.
Still interested? We’ve got more. We know, there’s a lot of jargon. Fear not.
You can find the answers in out Ultimate AL and ML Glossary
And next week you can watch our CEO, Cameron Weeks, explain the key concepts of AI and ML in a 3-minute video.
If you missed DestinationCRM’s Megatrends to Watch in 2020 webinar on January 22nd, tune in on-demand now. The panel of experts explore all kinds of tech stuff, diving into the world of AI, ML, and so much more.
What do companies want to get out of machine learning? A Gartner report found the top three use cases to be: reducing company costs (38%), generating customer insights and intelligence (37%), and improving customer experience (34%).
Gartner estimates that in 2018, 25% of all customer interactions were automated through AI and machine learning. With 90% of companies now planning to deploy AI within 3 years, this number is expected to grow to 40% by 2023. Even more fascinating is the fact that 15% of all customer service interactions will be handled COMPLETELY by AI by 2021 -- an insanely high increase of 400% from 2017.
So, is it worth the investment? It sure looks that way; ML is definitely one key to delivering exceptional customer experience, and stellar self-service is something customers really want. Many of them don’t want to talk to your agents as much as you don’t want them to have to! (Sometimes the best customer experience doesn’t involve another person at all.) But you don’t need to bite off more than you can chew... this can be done in steps. And there are things you can start doing right away to address the need for ML in your customer experience (CX) strategy.
How do you know if you’re focusing on the things that will bring you the biggest return on your technology investment? AI/ML are most commonly discussed in the context of CX with a focus on the customer-facing side, like the use of chatbots to front-end customer interactions and taking care of repetitive tasks like information gathering and billing lookups. But what about the potential operational efficiency gains you can get when you use ML to streamline internal processes like training and scheduling?
For example, what if your machines could learn how agents perform on their good days vs. their bad days using sentiment analysis and natural language understanding? Imagine knowing, based on this data, that on good days Bob can handle 1 call and 3 chats simultaneously. On a day he’s lagging because he worked the late shift the night before, however, he’s really only able to do 2 chats and no simultaneous calls if he’s to do them well. Knowing this, you could have smart machines route interactions to Bob according to the kind of day he’s having. No manual provisioning. No headache. Just a happy Bob and all of Bob’s happy customers.
Sign you up, right?
Ready to see what all this new technology can do for your contact center and customer experience? First, ask yourself what you’re trying to accomplish. Where do you envision ML fitting in your organization, in your contact center, and within your current processes? You’re here because you need the right tools for your agents and your employees.
As always, we welcome you to try it out yourself by signing up for 5 users on us.
Questions? Give us a shout.