With organizations like Forrester reporting that artificial intelligence (AI) investments could help businesses grow to $1.2 trillion in revenue by 2020, it's no wonder why it's one of the biggest trends in business.
In our first installment of Adventures in Artificial Intelligence, we defined four different versions of AI, and how each could potentially impact service. In this week’s installment, you’re in for a wild ride. Things have been very interesting in artificial intelligence circles.
A Taste of Top AI Stories
Just recently, The National Center for Scientific Research and the University of Bordeaux reported they had created an artificial synapse that can learn autonomously. The group also successfully installed the artificial synapse on a computer chip. In the human brain, synapses connect neurons, which is the basis of thought and brain activity.
What does it mean? This is a major step towards computers learning to quite literally think.
In addition, researchers from OpenAI revealed last week that an unsupervised system designed to predict text in Amazon reviews has self-taught itself to read sentiment. You read that right, self-taught. It seems robots are getting closer to understanding human emotion. At least within product forums, that is.
Our final news update features an ex-Huawei artificial intelligence engineer who married one of his robots last week, named Yingying.
According to Mashable, the ceremony was attended by his mother and friends. Whether or not this move was an elaborate public relations stunt, it reflects a growing trend in artificial intelligence: there may be laws to govern relationships between robots and humans by 2050.
Will any of this impact field service in the short term? No. But these trends are worth keeping an eye on, as they may impact service down the line.
Today, we will discuss virtual assistant technology. This timely and relevant artificial intelligence opportunity may prove useful in the short-term for savvy field service organizations.
Virtual Assistant Defined
A virtual assistant is any self-employed worker who performs professional administrative, technical, or creative assistance to clients from a home office. This industry first emerged more than a decade ago, and has been steadily increasing in size as workplaces have become increasingly digitized over the past decade—decreasing the need for in-person staff.
As artificial intelligence software evolves, many virtual assistant tasks are merging with AI technology, taking the form of chat bots, automated text messaging, and even voice-activated assistants.
From an IT perspective, Gartner defines a virtual assistant as:
A conversational, computer-generated character that simulates a conversation to deliver voice- or text-based information to a user via a Web, kiosk or mobile interface. A VA incorporates natural-language processing, dialogue control, domain knowledge and a visual appearance (such as photos or animation) that changes according to the content and context of the dialogue. The primary interaction methods are text-to-text, text-to-speech, speech-to-text and speech-to-speech.
Artificial Intelligence & Virtual Assistant Technology
Many common tasks performed by virtual assistants are quickly becoming automated through artificial intelligence technology. This is providing countless opportunities for streamlining both consumer and enterprise field service tasks.
Common applications of intelligent personal assistants have rolled out across a variety of devices including Apple iPhone (Siri), Microsoft Office (Cortana), and Google devices (Now). These assistants can perform basic voice-activated tasks, like reminding you to change the laundry, or go for a jog.
But more complex AI assistants are emerging, like Mona, a mobile app to personalize and handle all of your shopping. More robust tools are rolling out across the business world too. An AI scheduling assistant dubbed Amy can now schedule all your meetings, just by being CC’d on an email thread.
In field service, route optimization software is advancing to incorporate artificial intelligence capabilities. It can now optimize in real-time, and communicate changes directly to field techs.
While some of the broader possibilities of AI for field service are significant, virtual assistants and AI may prove most useful in the short-term.
Here are three major opportunities for enabling virtual assistant tech with AI to improve service:
1. Boosting Customer Satisfaction through Chat Technology
Through machine learning methods, enterprise organizations can now create automated online chat robots that are highly customized to customer preference, taste, and can even access purchase history. In field service, answering customer queries in their preferred channel has become paramount to success. Service organizations stand to boost customer satisfaction by creating chatnbots that can answer customer queries when human assistants are either busy, sleeping, or otherwise unavailable.
Developing chat technology using AI and machine learning requires a keen understanding of customer wants and needs. We certainly do not recommend a completely automated online footprint, but any small tweak that can get the customer to resolution faster is worth investigating.
2. Improving Real-time Technician Intelligence
Technicians are increasingly armed with mobile devices while on job sites. AI and virtual assistant technology could prove to be amazingly productive for service technicians troubleshooting challenging equipment scenarios while in the field. There are several different ways this could come to life, including:
Reactive Voice Technology
When a technician encounters a problem, he or she would simply ask his mobile device (and the AI software embedded within) for an answer. This software could be synced up to a corporate database, and ferry information from deep databases right to the technician. Relevant manuals, parts, videos, or even step-by-step instructions could all be delivered to the tech in real-time.
Many devices today can detect temperature, use infrared technology, test chemical presence and more. By combining these devices with artificial intelligence software, technicians could make faster, and safer assessments of equipment and scenarios while in the field.
Machine Learning Libraries
Currently, machine learning libraries are being used at scale in retail scenarios to find correlations between purchase behavior, weather, and past behavior to deliver hyper-targeted products to customers. Similar models could be built in service that leverage historical service data.
These models could potentially predict service outcomes, future equipment failure rates, and more. Technicians equipped with this intelligence while in the field will improve first-time fix rates, safety, and overall effectiveness of service technicians, and service organizations at large.
3. Reduced Complexity with Dispatch & Routes
Field engineers spend a whole lot of time behind the wheel. When routes are not properly optimized, wind-shield time can cost service organizations significant money each year in lost revenue, cost of fuel, and technician pay. Artificial intelligence software could effectively reduce drive-time complexity in two ways:
Computers think faster than humans, and have access to more data too. Using artificial intelligence algorithms to automate select aspects of dispatch can significantly improve worker efficiency. Your goal is getting techs to job sites to resolve as many issues as possible. Artificial intelligence can assess customer history, routes, technician skill level and more to assess the best tech for each job. They can then send push notifications, voice memos, or even call techs to inform them of their updated dispatch request.
It’s a challenge keeping up with real-time traffic changes, construction, or obstructions. Many service route optimization scheduling softwares lack the ability to create deep efficiencies across an entire fleet. Artificial intelligence can assess all available data in seconds and ensure the tech is informed of the most optimal route. Furthermore, virtual assistant technology can inform the driver of the change at the safest moment possible, based on current technician activity and location.