Artificial Intelligence, or A.I., continues to fill the headlines with promises of programs that can actually learn, think, and make decisions. This removes the need for complex programming instructions that define exactly what needs to be done in a given scenario. Instead, by looking at large datasets thousands of times over, a learning A.I. can take a base set of parameters and then quickly follow basic instructions to execute tasks.
This technology is already seeing rudimentary, but very promising, applications in a range of industries. Facial recognition is likely one of the more publicized areas that A.I. is finding success in. Learning A.I. has the capability to examine huge sets of images of people’s faces, then sort and identify them after enough information has been fed to the program.
The practical uses for true learning A.I. don’t end there. With nearly limitless applications in every sector, these programs have the potential to revolutionize the way we interact with technology.
For the service desk, one of the biggest challenges in the last decade has been trying to balance response time and workload. Nobody likes using ticketing software to place a request with the service desk only to get a canned automated response pointing them to a knowledgebase article that isn’t even relevant to the problem at hand. This lack of a personalized response may soon end with the introduction of A.I. systems – designed to provide both excellent service and a personal touch to each query. With A.I, every incoming ticket becomes a learning experience and each analysis can provide real solutions to daily service desk problems.
Advanced A.I. will make real decisions
One of the most obvious benefits of A.I. is the replacement of everyday interactions during ticket submission requests. In the past, this has been a bit of a double-edged sword. On the one hand, handing off a ticket request to an automated system is often necessary in order to properly sort, respond, and address each incoming ticket. On the other hand, most people don’t like receiving robotic responses to their desperate pleas for help.
Advanced A.I. has the potential to solve a lot of these problems. With enough training, a learning A.I. could feasibly and swiftly sort and respond to each ticket. Moreover, it can provide rapid, real-time responses to situations, and perform actual problem solving for more challenging requests. This won’t be limited to canned responses and form-like messages; learning A.I. isn’t that far away from being adaptive enough to mimic the actions of a real human. It could even successfully add a human touch to each message. Eventually, an A.I. response will be indistinguishable from that of an actual human being.
It’s not going to be a perfect system, and there’s going to be situations that require the attention of a less robotic mind. That’s another decision these automated systems will be able to make – once it’s clear they’re unable to assist the ticket, they will automatically hand over the work to an experienced employee. This hand-off process could include detailed information on what was already attempted and where each action led.
Even better, each task that requires being handed-off to a person will be a learning experience for the A.I. The eventual solution will be fed back to the program and the system will then learn from the information gathered.
Functions will evolve based on gathered data
Using this learned experience will allow advanced A.I. to gain new functions as time goes on. With more and more experience will come more and more functionality. Eventually, an individual A.I. system will be able to cover an extremely high percentage of incoming ticket requests without additional assistance.
Not only will this reduce the number of raw personnel hours needed for the service desk as a whole, but it should also drastically speed up response times and create new expectations for what the service desk is capable of.
All of this will be shared throughout the network
Any data gathered from day-to-day operations could be shared throughout a network of interconnected A.I. systems to help speed up the learning process for each individual program. By utilizing a cloud deployment, a very small client-side A.I. package could have access to a vast sea of collected data to work from. When this technology eventually rolls out, the service provider could use networked data from each of its clients to generate enormous data sets for the collective A.I. to work with.
This kind of in-depth learning will rapidly improve the functionality of each A.I. as time progresses. The more clients operating connected to the network, the more solutions the A.I. will have access to. Using this collective knowledge will greatly reduce the time for each subsequent deployment of an A.I. to reach operational status.
Deep learning like this is already being conducted for a number of different real-world projects. Major technology titans from Google to Amazon to Microsoft are already working on their own A.I. projects that are largely centered around breakthroughs in this specific category of research.
Dynamic analytics in real-time
Advanced artificial intelligence has the potential to come pre-packaged with analytic analysis and problem-solving programming that mimics real problem-solving skills. Analytics and basic problem solving are already going to be an integral part of an advanced intelligence package, it’s not a stretch to include macro problem solving within that design space.
Traditionally, data analytics revolving around the service desk is a process that requires a human eye to resolve and find inefficiencies or areas that could use improving. Learning A.I. could easily take control of these suggestions, routing the most relevant ones to senior staff for review. If the A.I. notices a trend in hardware failures centered around a specific day, for example, that information could be sent off for closer inspection. Or, if hardware from a particular vendor tends to fail earlier than the expected lifespan, that will be detected by the A.I. These valuable insights may otherwise be overlooked, particularly on an enterprise level.
The benefits of advanced A.I. aren’t that far off
The technology behind advanced A.I. is progressing at a rapid pace. Although the industry hasn’t quite reached Skynet levels of sophistication, it’s fast approaching a point where practical deployment is a realistic goal. Using these advanced learning programs is not just a possibility, it’s an assurance, and it will be exciting to see how far the technology takes us in relation to the service desk.