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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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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.

 

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Paul Whitelam, VP Product Marketing, ClickSoftware, puts across the case that in the race towards AI adoption we shouldn’t forget to see the value and importance of human input in the service cycle...

Like many industries, field service has seen an increase in the adoption of artificial intelligence-driven automation.

The benefits are many: improved efficiency, schedule accuracy, workforce productivity, responsiveness, cost savings and higher profit margins, and, importantly, happier customers.

Naturally, the onset of automation causes some anxiety in workers whose tasks are being handed over to AI. As with previous industrial revolutions, we’re not likely to find ourselves in a low employment high-leisure utopia. While the nature of work might change, plenty will remain to be done. Getting the full benefits of AI and machine learning still requires some human participation and a good understanding of who (or what) is best for each job.

People provide context

Service management solutions powered by artificial intelligence and machine learning can rapidly process high volumes of data to use as a basis for automated decisions. But when it comes to learning, machines can be a lot like humans— garbage in, garbage out.

When Microsoft launched its Tay chatbot on Twitter in 2016, few would have guessed that in just a day it would become a bigoted bully. The problem, of course, was that Tay was learning to converse by interacting with Twitter users, some of whom seized the opportunity to educate it on humanity’s worst impulses. Even with less shocking or inflammatory outcomes, AI learns from what it is shown and told. It’s likely to replicate bad behaviour if that’s all it’s shown.

AI-based tools can also provide simulations and modelling for multiple scenarios and highlight the interaction of various policy and process changes. For example, if the objective is the fastest response time available for every job, more technicians might have to be available for dispatching, increasing labour costs and decreasing utilization.

People must still define the process and priorities for automation to ensure your system optimizes for the right business goals. While intelligent computing power can grease the wheels of daily service operations, the real value comes from informing businesses to foster improved decision making.

Managing the unique and unusual

While humans can grow bored with the rote and routine, machines have yet to complain. Tasks that are repetitive and predictable are best handled with automation.

AI can manage most routine and ordinary tasks – chatbots, scheduling, appointment confirmation, routing, showing a mobile worker’s location and travel path to a job, it can even reassign and redistribute jobs around disruptions, addressing unplanned work with urgency. One UK gas utility can dispatch engineers to address a leak emergency in 13 seconds from the initial customer call—without human intervention.

AI can use a variety of inputs to increase schedule and travel time accuracy and optimize in real time, but what happens when you just don’t have the data?

One of the challenges faced by self-driving car producers is how to navigate remote areas, especially with routes that lack landmarks or distinguishing features.

Too few inputs can stump the machine. There is also additional context in some situations that will not be gleaned from data analysis, and impact from factors that perhaps are not being measured.

Hands off, humans

Applying new technology to solving problems in old ways yields minimal benefits, if any. Field service organizations see the greatest benefits from automation after reviewing their processes, KPIs, and business goals to leverage exactly the kind of data processing and analysis they didn’t have before.

They guide machine learning by providing good and plentiful data, filtering out the unimportant, and prioritizing the right goals. Specificity is key.

AI will do exactly what you tell it to—including replicating inefficient processes or making dubious decisions to optimize for a single outcome.

For example, prioritizing the shortest possible wait times for a technician to arrive could result in overstaffing and idle time—costing a lot of money.

Use projections and simulations to see how various goals interact to find the optimal balance, and remember that instructing your system includes telling it what not to do.

The vision of zero-touch service scheduling and dispatching enabled by AI and the Internet of Things is increasingly becoming a reality for service providers. Resist the temptation to interfere when unnecessary so you can give the machine a chance to learn, and reap the full benefits of increased productivity and efficiency.

What can your employees do with the extra time in their day? Focus on the people stuff, of course: training and coaching, brand ambassadorship, cross- and upselling, remote support—you name it.

There is still plenty for humans to do in the increasingly automated field service world, and it’s the work that relies on person-to-person connections and trust. While your people are improving service quality and strengthening relationships with colleagues and customers, trust that automation can handle the rest.

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