Artificial Intelligence has long been talked about as ‘game changer’, however, unlike some tech ‘hot topics’ the hype around AI is rapidly being backed up with credible and highly valuable applications; significantly, the service management sector, where attention has traditionally focussed on primarily increasing efficiency with a CX facet, rather than reimagining the problem and their optimal, holistic solutions.
What Is the Problem?
When we think about CX across our businesses, there is a danger that we play around the periphery; we define optimal customer journeys, communication channels, messages and engagement; this is certainly true in Field Service Management. However, often, we are seeking to find ways to make something ‘bad’ better.
Let’s take an example. One Tuesday, you wake up, climb into your shower only to find that your boiler has died. You call your service provider; you get through in seconds, they talk you through the triage steps; you restart the boiler, increase the pressure. Nothing.
The operative is empathetic, reassuring and dispatches an Engineer; but they wont be with you until tomorrow morning. The Field Service Management system kicks in.
You get confirmation of the appointment time, you get an opportunity to feedback on your experience, but you’re still in a towel, cold and late for work. You take the morning off from work to wait for the engineer; you have messages telling you when they’ll be with you and a map showing their progress. They arrive and are the embodiment of polite and professionalism. But. The widget at fault has caused others to fail. It will require a second visit, another half day at home, more inconvenience and no shower.
All the understanding, apologies and flowers might make the experience more bearable, but you are still inconvenienced and only the flowers will hide the growing the inevitable smell.
What if I told you that there was another day?
Artificial Intelligence in Service Management
Indulge me whilst I play out a Sliding Doors alternative that AI allows in Field Service Management.
Sensors attached to your boiler relay its vital statistics to the Field Service Management system in real time. Subtle variations in normal operating parameters are detected and fed through a deep learning neural network; the algorithm highlights the approximate time to failure and triggers an engineer appointment, allocating the replacement parts typically at fault and any ancillary components that may need replacing.
Utilising usage information from the boiler, an optimum appointment time is suggested and transmitted to the customer.
On Sunday you receive an email from your Boiler Service Provider. They would like to have a look at your boiler as part of your maintenance package. They suggest the next day at a time that is oddly highly convenient, you click to confirm.
You get an appointment confirmation, mapping of the operative’s location and they arrive on time, while you’re catching up with the soaps. They change a small widget in your boiler and they’re away. Tuesday morning comes; you have your shower and get to work on time.
All the actions are fed back into the FSM system and further train the algorithms.
How to Use Artificial Intelligence Intelligently
Of course the key benefit of an AI solution in this, and any other context, is that the more you use it, the more it learns and the more intelligent it becomes.
It is not about taking over the roles of humans, but allowing them to do their roles more efficiently and effectively, freeing them up to take on more jobs in a day or focus on other core parts of the business. Even the rarest faults and issues will be highlighted allowing an immediate fix, from an engineer turning up at the right time, with the right tools, at the right place.
This Predictive Service Management (PSM), as described in action above, rather than the traditional, Field Service Management (FSM) approach has yet to be fully exploited and the potential consequences on your customer experience strategy could be huge.
How Does This Impact CX?
As PSM is continually learning; building relationships and establishing performance levels from a broad spectrum of data sources.
By eliminating faults before that they actually occur and impact your customers, your customer satisfaction rates will rise dramatically. There will, undoubtedly, be a period of education needed where organisations will have to communicate effectively to customers that even though there is no evidence to the eye that there is a fault on their product, there is, and a fix is required. Once this period has been completed customers will be fully on board and confident that when an organisation calls to conduct a proactive fix, it will be saving them a huge amount of inconvenience further down the road.
The deep learning means that it is not just faults that can be identified but issues such as site access, which now, on the whole, are not picked up and cause issues for engineers and customers alike. This of course optimizes uptime, maximises efficiency, eliminates cost (for you and the customer) and all adds up to great customer experience.
The variety of sectors that this type of solution can fit is huge. The one aspect they all have in common though of course, is the level of competitiveness. Losing customers to rivals because of poor customer service is an avoidable situation, but one that many organisations continue to struggle to solve. In the services industry particularly, having the ability to solve customer’s problems before they even have any impact is a true game changer, giving those that embrace the technology first a huge advantage, and leaving those that do not way off the mark and struggling to recover.