AI co-pilots in service: 3 practical examples
"It's amazing what AI can already do," said one of our partners recently after a proof-of-concept project in which he and his team tested an AI co-pilot in service for machines and systems. Generative AI (GenAI), especially in the form of AI co-pilots in software, is on the rise and is not even stopping at the service departments of medium-sized companies. - A guest article by David Hahn
From everyday helpdesk processes to technical field service operations and the highest levels of complexity and escalation, this technology will fundamentally change the way we work in service in the future. Unlike 'traditional AI', the use of which has often been reserved for large companies with high levels of investment in IT and data infrastructure, skills and capabilities, AI co-pilots will have a much wider application.
GenAI is virtually predestined for use cases in service and after-sales. Why? Because these tasks are heavily text-based - be it customer inquiries, spare parts orders or filling out service reports and maintenance logs. AI co-pilots create texts independently, recognize complex correlations and suggest creative solutions. This relieves employees - regardless of their technical expertise - and raises service processes to a new level.
Why an AI co-pilot rather than an AI chatbot?
Nevertheless, it is still important to keep your hands on the wheel. The large language models (LLMs) tend to make things up from time to time - this is known as hallucination. It is important that employees always check the generated content before it is sent. A fully automated chatbot is therefore fraught with risk when it comes to complex issues, such as in mechanical engineering. As the term "co-pilot" suggests, the employees remain at the wheel and the AI serves as an assistant. The risk of hallucinations can be further reduced by limiting the context for the AI to a system or machine, for example. The AI is then only fed with the information relevant to the system, which significantly increases the probability of a precise response.
Be it in request management or in the documentation of service reports: AI copilots offer massive support for text-heavy tasks.
Practical examples of AI co-pilots in service: from request to solution
We show you what a service organization supported by AI co-pilots can look like:
Helpdesk: Relief for routine tasks
In the helpdesk, routine tasks are significantly simplified by AI co-pilots. Requests that previously had to be checked, prioritized and processed manually are analysed and categorized by GenAI and a suggested solution is generated. This significantly shortens the processing time. GenAI also summarizes requests or tickets to the essentials in a matter of seconds. The training effort for employees, e.g. when involving other departments, is significantly reduced. E-mails are also created and translated at the touch of a button. This significantly reduces the amount of paperwork and allows the team to concentrate on solving complex problems.
Field service: rectify faults faster
AI co-pilots increase the efficiency of technicians in the field. Previously, it was often a challenge to have the right information to hand quickly on site, which often led to queries from colleagues in the back office or helpdesk, where they then had to search for a long time. AI co-pilots then automatically search through technical documentation such as digital operating instructions, manuals, maintenance and service histories, e.g. in relation to an individual machine, and summarize this information concisely or can also answer questions asked directly.
This allows technicians to diagnose and rectify problems much more quickly and often on the first visit. A detailed overview of the spare parts installed and the maintenance history helps to quickly and effectively assess the situation and respond appropriately. Another advantage is the automated creation of service reports: AI co-pilots collect relevant data and convert simple entries in bullet points into precise, detailed reports, which reduces documentation effort and increases efficiency. AI co-pilots also have the potential to reduce the severe shortage of skilled workers, particularly in field service.
Preserving knowledge: What happens when all the baby boomers retire?
When tickets, forms, reports and logs are filled out and processed in modern solutions, the software solution itself serves as a knowledge database in which the service works. An AI co-pilot that builds on this can bring together this knowledge from the individual modules and make it accessible via a simple chat function, for example. Many companies fear that valuable knowledge will be lost as the baby boomer generation retires. AI co-pilots can provide a remedy here.
The best thing is that these tools can be used by all employees without any special prior knowledge. Nevertheless, training in the use of tools such as GenAI, ChatGPT or AI copilots is recommended in order to exploit the full potential of these technologies and provide employees with the best possible support.
The most important conclusions
We are currently experiencing a revolution, and not just in the service industry. The possibilities offered by generative AI are impressive. Interactions are moving closer to the customer, which not only saves time but also costs - the "shift left principle" towards more help for self-help is taking on a whole new meaning. And the best thing is that every service organization, whether large or small, can already benefit from GenAI today - whether through the direct use of solutions such as Microsoft Copilot, the exchange of best practices within the team or the integration of AI copilots into your software solutions in Field & Customer Service. Incidentally, Hans WEBER Maschinenfabrik demonstrates the efficiency gains that an AI co-pilot enables in service practice today in its Case study.
