Three Practical Artificial Intelligence (AI) Approaches For Field Service Management (Part 1)

Nov 21 • Features, Fleet Technology • 736 Views • No Comments on Three Practical Artificial Intelligence (AI) Approaches For Field Service Management (Part 1)

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Artificial Intelligence has increasingly become a key discussion in all industries and its impact in field service management is predicted to be hugely significant, but how should field service organisations leverage this powerful twenty-first-century technology? In the first of a two-part feature, Marne Martin, President Service Management IFS, offers her expert insight…

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Note: Please note that by utilising this link for applying for a subscription your data may be shared with the content sponsor IFS, who may contact you for legitimate business concerns relating to the topic of this content (i.e. field service management). For more information on how we store, control and process data in line with EU regulations visit fieldservicenews.com/subscribe

Artificial Intelligence (AI) will impact every industry and every business discipline—including field service management. But how quickly will practical solutions be available that enable the typical medium to large field service organization to take advantage of AI? And by practical solutions, I mean AI that delivers knowledge efficiently, processes solutions to complex data sets, and automates repetitive activities to allow human workers to focus on personalized service, solving complex problems and escalations, i.e. what people do best.

In some cases, these easily applied solutions are still on their way to market. In three specific areas, however, practical AI applications for field service are already commercially available as proven, commercial off-the-shelf software delivering real business value.

AI For Customer Interaction

First impressions matter. And unfortunately, the first interaction a customer has with your service organization often involves several missteps. Chief among these are long wait times on hold due to high call volumes. And then, as a customer attempts to reach out through multiple channels including email, chat and phone, the resulting data stream goes into separate siloes that are disconnected from each other, resulting in disjointed communication.

Today, AI solutions can solve both these problems, but it requires more than “just” chatbots.

Today, AI solutions can solve both these problems, but it requires more than “just” chatbots. Commercially available AI software that ties into chatbots is capable of learning which answers posed in a chat are appropriate for each question and automating a significant majority of chat interactions. A chatbot can be taught to answer commonly encountered questions, like inquiries about when a technician is scheduled to arrive. Of course, at some point, the AI chatbot may get stuck when personalized service is required, and a human agent takes over the discussion thread without missing a beat. This should be seamless not only to the customer but for the internal customer service, ticketing and support systems as well. The chatbot—regardless of whether driven at a given moment by AI or a human agent—should update the same customer record as other channels including social media, phone and email.

And from interactions, the AI functionality learns from answers provided by human agents and gets better and better at answering questions through learning processes. A truly advanced AI chatbot will also seamlessly hand off the chat to a human agent when the extent of its learning is overtaken. Only then can the entire customer experience be unified and consistent, even with a static number of agents handling a rapidly growing fluctuating volume of customer interactions.

AI-based chatbots, for instance, can enable a good agent to handle up to five or more chats at a time. It can capture Facebook messages and tweets and direct them to an agent or to AI for intervention. AI alone can handle, typically, between 50 and 60 percent of requests, freeing up human capacity or lowering staffing levels required to handle a given volume of activity.

Enables Management By Exception

In the case of AI applications for the service organization, a primary driver for ROI is that it enables humans to manage by exception. A high volume of activity can be automated, and humans intervene primarily when a situation falls outside the business rules or logic built into service management software. AI doesn’t eliminate the need for human interaction—it makes the human interaction more focused on what humans do best—handle escalations and complex decision making for unique cases.

At one IFS customer, an AI chatbot handles about 50 percent of interactions— primarily those reaching out to cancel their service after a free three-month trial period. Interactions cancelling a free subscription are handled entirely through automation. But if a longer-standing customer is cancelling their service, the interaction gets routed to an agent dedicated to saving the account.

Some interactions are by default easily handled by AI. If 30 percent of inbound contacts are requesting information on the arrival time of a field service technician, it may be possible to automate 90 percent of that 30 percent of contacts. But it is also important to consider the demographics of the customer base. Millennials are more likely to communicate via chat or social media, so if a significant percentage of customers are under 40, heavier reliance on chatbots and AI may help you increase engagement by streamlining your customers’ preferred method of interaction.

Management by exception is also more successful when an AI application has access to extensive information about each customer.

Management by exception is also more successful when an AI application has access to extensive information about each customer. So full integration with enterprise resource planning, field service management and other enterprise tools is essential. AI tools can be more effective if they have more rather than less information on the status of the customer’s account, including their maintenance or service history and warranty or service level agreement entitlements.

Integration between an AI chatbot, email, voice, social and enterprise applications is important for another reason. It enables one version of the customer record. Lacking this, a customer can send an email, and get no response. They send a direct message through Twitter. Then call and sit on hold. Then initiate a chat. All these interactions may not appear in a central customer record, but there have been three attempts to contact the company. Right from the first contact by email, the clock started ticking on a service level agreement.

Full integration can also enable a customer service team, once a customer request is resolved, to close off all queuing activations at the same time for the various contact methods associated with a customer case. Failing this, a service organization may spend a significant amount of time chasing customer requests that have already been resolved.

Want to know more? There is a full white paper on this topic available to fieldservicenews.com subscribers and if you are a field service management professional you are eligible for a complimentary industry subscription. click the link below to apply for your subscription and we will send you a copy of the white paper instantly!

Click here to apply for an industry practitioner subscription now and get access to the white paper instantly!

Note: Please note that by utilising this link for applying for a subscription your data may be shared with the content sponsor IFS, who may contact you for legitimate business concerns relating to the topic of this content (i.e. field service management). For more information on how we store, control and process data in line with EU regulations visit fieldservicenews.com/subscribe

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