It’s impossible to deny the influence technology has had on our daily lives for the past few decades, and now more than ever, automation and the use of artificial intelligence (AI) is ubiquitous. Big data and analytics are disrupting the most traditional of trades and spring-boarding new ones altogether.
Let’s start with the foundation:
What is Artificial Intelligence (AI)? There are many definitions, but at its core it involves machines that can think the way humans can. We are talking about computer systems that can do many kind of things that humans are already good at and beyond. Back in 1956 Dartmouth Summer Research Project on Artificial Intelligence was the birth of this field of research and it has taken all these years for us to start seeing some of that technology come to the market.
What is Machine Learning (ML)? Machine learning actually dates back to the middle of the last century. In 1959 it was defined by Arthur Lee Samuel as “Ability to learn without being explicitly programmed”. The idea of the machine that can learn on its own was invented. And Mr. Samuel even created a computer checkers application, the first program that was able to learn from its mistakes and improve its performance overtime. There are a lot of products we use every day that are powered by ML, but you might not even know it. Facebook deciding what to show you in your news-feed; Amazon is highlighting products you might want to purchase; Netflix suggesting movies you might want to watch. All these recommendations are based on predictions that arise from patterns from existing data.
What is Deep Learning (DL)? Deep Learning is a newer term, but it is very important one. It is where certain set of machine learning algorithms run on multiple layers and try to mimic the brain’s neural network to actually learn new domain with little or no human supervision.
What is Natural Language Processing (NLP)? NLP is an ability for a computer program to understand human speech, like the way it is written or the way it is spoken. NLP is a component of AI and DL, but it is a complicated field of itself. NLP essentially allows these systems to understand and process human input.
Quick fact: 8 out of 10 businesses have already implemented or planning to adopt AI as customer service solution by 2020.
Let’s get more practical and talk about the specific examples of AI/ML impact on Customer Service:
- Basic Repetitive Tasks – think password resets, account resets, etc. It’s all about freeing up the time of your team to handle more complex issues.
- Customer Engagement Automation – think about outsourcing T1 work to machines, basic interactions handled by chatbots
- Pattern recognition and prediction – think account usage proactive analysis and outcome predictions being delivered to the clients well in advance. Incidents being prevented, problems anticipated.
Total automation today can work very well for easy and repetitive tasks. However the work that requires creativity, social skills and intuition (so called edge cases) do need humans. AI is used to augment human Intelligence and knowledge. Machine Learning can analyze a lot of data and recognize as well as predict patterns that humans can’t. Think about all the data you have in your Help Desk or Support Organization that no one is looking at..
To stay up to date companies need to adopt a comprehensive digital strategy for their Customer Service organizations. Aim to bring humans and machines to work together leveraging the best in each force.
I remember having conversations with Support Engineers where they at times would express concerns about individual KPIs being overly ambitious, saying “I’m not a super-human to run with 4-5 chat sessions and answer a phone all at the same time”. With AI/ML we might just be able to get as close as it gets to being those super-humans. Never before has the business world been exposed to a ‘wild-west’ atmosphere of opportunity. Those that run, not walk will win.