A study conducted by Gartner back in 2019 revealed that 25% of the participants already had a machine learning (ML) or AI project planned for the following 12 to 18 months.
However, this doesn’t mean that all businesses are able to streamline the potential of this nascent technology into practical applications that manage to generate immediate results.
With this in mind, we’ve handpicked three ways AI can optimize business service management to bring further benefits for businesses.
#1 Service Triage
Business service management is a complex combination of processes that, when put together, work to achieve a smooth process and good results.
Just like an urgent care facility, teams need to be able to prioritize their jobs, be aware of the available staff, and so forth. Stretching this analogy further, some clients come with specific problems that have a quick remedy while others require complicated diagnostics.
Now imagine a scenario where your team is able to identify the principal cause of the issue before arriving on-site. For more serious issues, they will be able to be prepared in advance, have the necessary historical information, and get the job done a lot quicker.
This is where the service triage concept comes into play. It’s consisted of the following:
- Dispatch the technology which consists of the best skill set to get the job done and guarantee they’re supplied with the right parts when they arrive.
- Empower agents who are customer-facing to properly troubleshoot the issue during an online chat or on the phone by going through a promptly prepared dynamic checklist.
- Gather the necessary historical information, store it appropriately and apply machine learning and AI to unlock important insights.
When it comes to business service management, it’s important to be both on time and quick, especially when dealing with pressing matters. Implementing AI can help you prepare in advance and be ready for whatever may come your way.
#2 Optimizing Parts Pickup
Another critical component of business service management is optimizing the parts pickup. As a business owner, you might already be tired of talking about stagnant rates for first-time fixes.
Nevertheless, many studies have confirmed that the lack of correct parts is one of the biggest challenges when it comes to this particular service struggle. One report by Salesforce shows that rates have remained 70-75% on average, even though a lot of the companies are even averaging in the mid 60 percent. This does take its toll on customer relationships and on truck roll costs.
Now, there are clever ways to boost the rates by ensuring that you dispatch the proper technology to jobs, coming with the right parts on the very first visit. You can also take the guesswork out through an AI-powered solution that can make quick use of the otherwise manual headache.
- You can use historic and real-time data to assign appropriate technicians to the depot with the proper and right parts for the jobs based on the inventory and distance.
- You can analyze already available information about equipment, appointments, as well as the availability of your technicians and locations to determine the correct parts that are necessary before a technician actually arrives on the site.
- You can automatically stream this information into scheduling systems for an optimal manager and employee visibility.
#3 Prioritizing Jobs
As we mentioned in our first analogy, job prioritization is another key component of appropriate business service management.
The most basic prioritization follows the “order in – order out” concept. Unfortunately, service clients are not exactly diner patrons who are waiting for their special.
If you integrate an intelligent platform that’s capable of interpreting huge amongst of information, you will also be able to make unimaginable strides when it comes to efficiencies.
An AI-based system that takes advantage of both Artificial Intelligence and machine learning is capable of prioritizing jobs based on the needs and preferences of the customer, on the KPIs, long-term business goals, and so forth. It can even prioritize jobs based on the skills of the technicians. This way the customers will get what they want while both technicians and dispatchers won’t spin their heads.
The way it works is as it follows:
- Intelligent systems are capable of analyzing service data continuously. This includes work orders, parts inventory, product catalog, customer contracts, as well as technician notes to boast a smart order.
- The algorithms are capable of scanning the requests and generate a priority list for your customer tickets based on the insights that are drawn from the service data.
- You will be able to configure priorities based on the business’s requirements. Some of the main parameters to consider include workforce capacity, geographic location, depot location, job complexity, customer status, and parts inventory.
Wrapping it up
All in all, business service management is a dynamic field where being able to prioritize, analyze, and call the shots based on actionable information is critical.
An AI-based system can substantially improve business processes, streamline your overall workflow and, in turn, increase billables and service fees.
It’s also true that AI systems will only get faster and smarter in the future. This is why it’s crucial to stay in line with the rapid pace of change so that you don’t fall behind.