AI: A Cornerstone of Your Digital Transformation Strategy

When business leaders think about Artificial intelligence (AI), they usually do so in one of two ways. Either they think of it as science fiction, or they see it as a customer-facing gimmick. The reality, though, is that Artificial Intelligence is already beginning to integrate itself into tools used for Digital Transformation in a variety of ways. While we’re only now seeing this happen on a relatively small scale, it’s easy to see the ways that today’s capabilities will blossom.

Here’s an example of how AI is subtly coming into play today: Let’s say you’re a commercial HVAC company, and your systems are connected via IoT-enabled sensors. For the last six months, you’ve been benchmarking internal data about how your HVAC’s output changes in advance of a breakdown. That information is fed into an AI-powered system that can then take that data and say—two days before a system malfunction, we see these three things change, so when those three things change, I will set up an automated alert. Setting triggers based on specific set of criteria being reached is programming 101, of course, but learning what those criteria are, and evaluating how to handle it, defines AI. This sort of prescriptive repair will only increase as AI systems better learn and adapt.

A question worth considering as it relates to Artificial Intelligence is just how important it will be to digital transformation relative to the other triggers that have led us to our digital-minded present. Microchips, the internet, and mobile computing all represented major paradigm shifts in the way that we think about technology, leading organizations to transform themselves into digital businesses. Does AI represent a similar shift?

The answer, I’d postulate, is yes —but less obviously. This owes itself somewhat to AI’s form factor. You can’t pick AI up off a shelf and install it in your business, the same way you could buy a computer or a phone, or connect to the internet. Academically, Artificial Intelligence has more in common with a programming language that one of those utilities, and because of that, many organizations employing complex field service software don’t even realize that they’re using elements of AI in their business today. In order to understand how that has—or soon will—make a difference, we need to look at what technologies organizations are adopting in support of their digital transformation efforts today.

According to IFS’s research on the subject, 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 Artificial Intelligence. The most obvious, 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.

How AI Assists, Not Replaces Human Interaction

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 to human interactions, and make recommendations could save time and improve service outcomes. There are two key ways in which this has been employed today. One is through speech parsing and recognition, allowing 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 waste time consulting reference material.

The other piece where AI can support is for escalation. Certain 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 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. There’s a pretty clear line to be drawn to the enhancements that AI is already offering in scheduling optimization. Scheduling optimization today takes your available technician, service appointments, anticipated time of completion of each job, and automates scheduling. Since AI works primarily to enhance these systems through adaptive learning, AI allows for these systems to self-improve, working towards more accurate predictors of time to complete a job, 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 these things. Those technologies need to be able to communicate, and the data collected from those technologies needs to be available in a centrally-located, common language. This is something that service organizations should be working towards today.

One of the most common mistakes that plagues organizations attempting to chart a path to true #DigitalTransformation is the oversight of cultural implications. https://t.co/kwM8vtxH7u

— IFS (@ifs) March 31, 2020

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