You should be using artificial intelligence for HVACR field service management
The future of artificial intelligence holds a lot of promise for enterprise systems, and we are already beginning to see its significant impact in automation. In three areas of field service, there are already commercially available, practical AI solutions delivering real business value: enhancing customer interactions, enabling management by exception and quickly masterminding complex scheduling.
AI for customer interaction
First impressions matter, but the initial interaction a customer has with a service organization often involves several missteps — chief among these are long on-hold wait times. Customers are also reaching out through multiple channels including email, chat and social media, and when these data streams go into disconnected siloes, the result is disjointed communication.
Today, AI solutions can solve both these problems, but it requires more than “just” chatbots. Commercially available AI software which ties into chatbots is capable of learning which answers are appropriate for each question and automating a significant majority of chat interactions. A chatbot can be taught to answer commonly-encountered questions, such as inquiries about when a technician is scheduled to arrive. This enables customers to have a digital, convenient experience in their own time, while also providing call center efficiencies and the ability to use your agents to deliver a personalized service.
AI alone can handle, typically, between 50 and 60 percent of requests, but at some point an AI chatbot may get stuck and this is when personalized service is required, with a human agent taking 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. AI-based chatbots can enable a good agent to handle up to five or more chats at a time. It can consolidate Facebook messages, emails, telephone and tweets and direct them to an agent or to AI for intervention.
The beauty of this AI functionality is that it learns from answers provided by human agents and gets better at answering questions. 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.
Enabling management by exception – driving ROI
In the case of AI applications for the service organization, a primary ROI driver is that it enables humans to manage by exception. A high volume of activity can be automated, with human intervention limited to when a situation falls outside 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, but if a longer-standing customer is cancelling their service the interaction gets routed to an agent dedicated to saving the account.
Complex scheduling made easy with AI
Human agents can excel in serving customers directly, but in the case of scheduling technicians in the field, humans are sometimes just not able to manage the constant numerical challenge of optimally adjusting a schedule.
Manual or traditional software-based scheduling may be a workable solution for service organizations with a very small number of technicians engaged in a small number of jobs. But it does not take many technicians or jobs for the number of possible solutions to outstrip human computation capabilities either individually or as a group.
A dynamic scheduling engine (DSE) driven by AI algorithms is designed to solve complex scheduling problems in real time—problems much too complex for any human dispatcher or customer service agent to handle. Even at the low end of the spectrum, a human dispatcher cannot quickly identify all the possible solutions and pick the best one. Two technicians, and five service calls yields 720 possible solutions. Four technicians and 10 service calls present a dispatcher with 1,037,836,800 possible solutions. By the time you get to five technicians that must complete six calls each—a total of 30 calls, you have infinite solutions.
Finding the optimal solution becomes even more complex as rapidly-changing factors are added into the mix — including emergency jobs, SLAs and other contractual requirements, technician skill sets, or tools and materials currently in stock on each service vehicle. An AI-driven scheduling engine automates the schedule, making adjustments in real time, based on priorities set by service organization management and real time information. This frees up human dispatchers to manage by exception and deliver meaningful customer interaction that builds loyalty and deepens the relationship.
A smarter approach to inventory logistics
Service management for many businesses relies on inventory. When a service request cannot be closed on the first visit, it is often because the right part is not on the truck or immediately available.
Service management software should encompass inventory management functionality, and that functionality should include automated reorder points for each part. The ability to take parts availability into consideration is a critical data set for AI to work on, as parts are a critical determinant in first time fix and job completion. It’s also key to successful SLA and outcomes-based commercial relationships.
Once inventory data is available and integrated, a powerful DSE can be configured to influence inventory logistics so parts and materials are housed in warehouses, satellite offices or inventory drop locations closer to anticipated demand, and inventory is matched to jobs in a forward or current day schedule.
In one very large implementation of IFS Planning and Scheduling Optimization software — the London Underground transit system—inventory and tools are dropped ahead of each service visit so technicians who ride the subway to the service site can pick them up. This is only possible with a high degree of coordination between the service schedule, inventory logistics and an AI-driven scheduling tool.
Transform, outflank, disrupt
Service organizations should recognize the tremendous potential AI holds — they can harness it to transform their operations, outflank their competitors and disrupt their markets. We are only starting to tap into the different ways AI can be used to better solve the problem of delivering optimal service in a rapidly changing environment as adoption is still lagging despite the real benefits AI brings. The good news is there are several straightforward and easily accessible ways service executives can harness AI technology right now today.