AI-driven case and work order management can significantly reduce the time-consuming manual routine work for energy companies, freeing up essential resources. Automating these processes minimises the error rate and dependency on individual employees. Most importantly, AI enables creating and implementing new, efficient workflows that guide the work, ensuring consistency and adherence across all teams involved..
So how does it happen?
Predefined case flows guide the work.
AI-driven automation is based on rules and algorithms. In our case, different workflows with different business rules are set up for different case types - for example, fault rectification, maintenance, new installation, meter replacement or relocation. The business rules specify what information is needed to perform a case type, what steps are to be performed and what triggers a status change.
The predefined case flows also govern what the workforce is expected to do, primarily through forms and checklists that must be completed to initiate or close the case. Information entered into the system is validated and quality assured in real time.
Through forms, notifications and validation of data, the system ensures that the case is carried out in a way that meets current customer agreements and SLAs. In this way, the system moves the case forward regardless of which people and actors are involved, and it drastically reduces personal dependency.
When a case is initiated, it is classified by case type. The system then ensures that all information needed to perform the case type is retrieved. It can be location, type of job, work method, SLA rules and expected revenue when the price is predetermined.
The forms can be largely filled out automatically and only need to be checked and adjusted manually, saving time, and reducing the risk of errors.
Planning and scheduling
Ai-driven automated scheduling is extra valuable for maintenance planning, service and meter replacements, but also for urgent matters, such as power outages.
The system analyses all work orders that exist within the period you want to schedule and creates an optimal schedule, which, among other things, considers skill requirements, case times and other SLAs. This schedule complies with all regulations and will minimize costs and thereby also travel times and carbon dioxide emissions. If there are special requirements that the scheduling needs to consider, there are an additional 20 business rules to choose from to adapt them to your needs.
Automatic assignment ensures that the work order is sent out to an installer.
The beauty of this is that your employees don't have to try to keep all the rules and information in their heads. The system provides them with a much better plan than they could do on their own. At the same time, they get an overview of all resources and what the need looks like. It eliminates administrative work, improves resource utilization, and reduces the number of revisits.
Monitors and follows up.
Employees can be alerted to deviations from the plan when they occur and propose measures via notifications. Some actions can be automated, for example, finding a new installer nearby if the person assigned the job gets a flat tyre or gets sick.
The system also ensures the financial flow from ordering to e-invoicing and reporting, by feeding the financial system with the correct financial data.
Improves communication.
The field workers need to know what equipment to take with them and what is on site. The system tells them that. It keeps track of where the cars are and what equipment is located where. It tells them what to do, who to work with and what to report.
Your employees and not least your end customer continuously find out about the status of the case through notifications. The customer can receive an SMS when the matter has been received, when someone is there when it is expected to be finished and when it has been carried out.
It can also actively participate in the planning by the system calculating several suitable visit dates to choose from.
Two challenges with automation
Automation assumes that there is sufficient information about the cases and that the business rules are adapted to reality. In the beginning, for example, it can be difficult to calculate the time required for various steps.
Many companies also feel that exceptional cases make it difficult to define a case process. There are many exceptional cases. Automating the normal cases is much easier - focus on them and don't try to automate everything. Instead, you can find manual procedures for the exception cases. AI will probably take care of the exceptions in the not-too-distant future.
