We recently teamed with TSIA for a recorded Q&A about machine learning and AI as it relates to its use in support organizations and their particular needs.
The idea for this webcast came from our time at a recent TSW show. We spent a lot of time at the conference talking to people about these technologies. While people were genuinely interested in the capabilities and the far-reaching potential of AI and machine learning, ultimately what people really wanted to know was how AI and machine learning might impact them, their day-to-day, and their team.
Using machine learning in customer support equates to taking your most experienced rep – someone with years of experience responding to customer questions – and making her/him exponentially faster. Taken a step further, machine learning allows you to replicate their expertise and newfound proficiency amongst all of your reps.
We’ve been hearing about Artificial Intelligence for years now – enough so that even the most casual AI watcher has a high-level understanding of what AI is: the art and science of getting computers to act and respond just like humans do. Casual AI watchers are also likely to recognize that AI is getting smarter - less artificial and more intelligent - by the day. In part, that’s thanks to machine learning, an AI technique that helps AI become more intelligent by learning over time, on its own, without having to be programmed. It does so by observing real-world interactions between humans and computers, and analyzing the data and information associated with these interactions.
Here in Massachusetts, we find ourselves surrounded by beautiful old mill buildings that act as a constant reminder of how our nation’s history is built on manufacturing. In the early days of the industrial revolution, water powered the mills that produced the products the young nation needed to move forward.
Even today, with tech companies all around us, we know that machines continue to power the world. They move us from place to place, whether that’s a car, train, airplane, or ship. They provide the power we need and till the soil to provide the food we eat.
The enemy of production is downtime, so to keep these machines running mechanics are constantly doing regular maintenance, often repairing and replacing parts long before they need it to simply avoid breakdowns.
You can hear that line in dozens of movies and it's a clear indication that a lead character spends a lot of time at a particular diner. Of course the long-time waitress (and it's almost always a waitress) knows exactly how the main character likes his eggs (and yes, it's often a "him").
What does this have to do with AI? Randi Zuckerberg, president of Zuckerberg Media, recently launched a pop-up experience to help kids better understand science through food. Called Sue's Test Kitchen it had some very interesting experiments, like serving 3D printed pancakes or freezing with liquid nitrogen, but Andrew Brust found a much more interesting take on it: the use of AI to get to know you, the person eating the food.
The concept of Garbage In, Garbage Out (GIGO) is almost as old as computing itself. Its origins have been traced back to the 1950s and basically means that if you start with bad information, you get faulty results. It’s a pretty simple concept that remains at the core of computing.
As machine learning takes off in the marketplace, the GIGO issues have become even more pronounced. In truth, the garbage is everywhere and we need to be careful about training our systems to emulate the wrong human behaviors. This is at the heart of a story in Wired Magazine that points out how photos were helping some machines learn sexist behaviors. A pair of researchers began to notice that some images, like those of kitchens, were more associated with women than with men. As they looked deeper they realized the problem wasn’t in the algorithm or in the core of machine learning, but in the images used as the base datasets designed to train the system.
How many times have you switched your mobile phone service provider when the service or support was poor? How hard did that service provider work to keep you? It’s likely they didn’t try very hard. They have many customers, so losing one isn’t that big of a deal. But for companies that provide complex products like those in manufacturing, aerospace or oil and gas, a high-quality customer support program is critical. The question is, what does a quality customer support program look like?
The Support Challenge for High Value Products
A couple of scenarios to demonstrate the need for strong customer support and preventive maintenance programs in manufacturing industries.
As we’ve explained in many blogs and 5-minute guides, a cognitive search platform should combine AI technologies such as natural language processing, machine learning, and knowledge graphing to deliver a contextualized search and discovery experience without compromising security. Those technologies can turn ordinary search into something much more powerful and transformative for any organization. But in the hands of life sciences companies, it can help deliver drugs and other therapies that lessen suffering and save lives.
Today’s business users don’t search for documents that may have the information they seek buried within, instead they ask their systems for answers. This shift in attitude is a key driver behind the move to cognitive search.
Any large enterprise is packed with disparate sources of data coming from any of a variety of different systems. Cognitive search is about creating connections between this data so that employees can get answers quickly, so they spend more time on core activities, and they make better informed decisions.
From a business perspective, this means creating experiences that match how a user interacts with information.