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MAPPING TOMORROW’S AI ADVANCEMENTS THROUGH TODAY’S DIGITAL TRANSFORMATION

Based on today’s technology investments, what can we reasonably predict about the future of Artificial Intelligence? 

There are two different ways that we can evaluate the data. First, we can take a look at current investments and identify how those current investments could be enhanced by AI capabilities. Secondly, we can look at the AI-specific initiatives, and the current level of market implementation today, versus planned implementation over the next few years. Let’s begin by taking a look at the current field of technologies supporting digital transformation.

The top technology that organizations are implementing in support of digital transformation is call center technology, and it’s easy to see ways that the call center could be enhanced through sophisticated AI. The most obvious, AI-driven chatbots, are in varying states of sophistication. These can help manage simpler service needs that don’t require specific escalation, thus allowing support staff to focus on more complex remote issues. Improving AI, and tying AI systems into connected devices has the potential to make remote resolutions quicker, easier, and far less labor intensive. This is compelling, but certainly not the most compelling use case for the call center, which is how AI can assist human technicians during a service call.

The fact of the matter is that when issues arise, people would rather talk to actual, verifiable humans, and today, humans are better at conversation than AI.

So rather than actually fielding the call, allowing AI systems to listen in on human interactions, and make recommendations could save time and improve service outcomes. There are two significant ways this has already been employed today. One is through speech parsing and recognition, which allows AI systems to diagnose potential issues and provide solutions.

Diagnostics are generally an act of isolating and identifying issues, and humans are fallible, limited to what they’ve seen previously. Some more atypical issues may not occur to a human, but an AI system can catalog a list of symptoms and make informed recommendations in real-time, without a person having to consume valuable time consulting reference material. This crosses over into the second-most adopted technology, knowledge management.

"Algorithms only get so much smarter over time—at least today. Additional AI capabilities will enhance these systems through adaptive learning, allowing them to self-improve..."

The other process AI can support is escalation.

Certain AI systems can read conversation, tone, tenor, and severity of issues, and serve up the appropriate directive without having to consult a manager. For example, a customer’s machine has malfunctioned due to a technician’s failure to secure a part after routine maintenance. The customer is angry. The AI system can take all of these factors into account, and say that a free repair, and six months of complimentary maintenance has resolved a similar combination of customer issues in the past.

This will allow phone operators to offer incentives without putting the customer on hold to check with a manager, and as these offers are accepted or declined by customers, the AI can improve in recommending those offers.

One final piece of currently-adopted technology worth evaluating in terms of its improvement through Artificial Intelligence would be scheduling optimization. AI-driven optimized scheduling offers clear benefits to a service organization. Scheduling optimization today takes your available technician, service appointments, anticipated time of completion of each job, and automates scheduling. AI algorithms can make decisions based on the current location of the technician, their skill set, parts on their vehicle and urgency of the service request—solving problems too complex for any human dispatcher.

But the algorithms only get so much smarter over time—at least today. Additional AI capabilities will enhance these systems through adaptive learning, allowing them to self-improve. Scheduling optimization engines will work towards more accurate predictions and better scheduling decisions, meaning smaller time windows for customers, and much higher fleet utilization rates.

For an AI system to work in this capacity, it needs a full view of the entirety of the service process, from employee’s individual average time to complete a job, to vehicle information, to inventory positioning. It’s imperative, then, that your systems be prepared for this today. This doesn’t just mean that you have technology in place to manage specific field service activities. Those technologies need to be able to communicate in a centrally-located, common language. This is something that service organizations should be working towards today in order to prepare for digital transformation.

Want to know more? The full white paper relating to this series is available as premium content to fieldservicenews.com subscribers...

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Data usage note: By accessing this content you consent to the contact details submitted when you registered as a subscriber to fieldservicenews.com to be shared with the listed sponsor of this premium content, IFS, who may contact you for legitimate business reasons to discuss the content of this content.