In recent years, artificial intelligence (AI) has become an integral part of many areas of life, from software development to medicine and the humanities. The field of diagnostics and repair of household appliances is no exception. The implementation of AI in this process has a significant impact on the efficiency of service companies, particularly in terms of speed and quality. This not only improves the quality of service but also reduces costs, bringing economic benefits to both service providers and end consumers. To discuss these processes and innovations in such a seemingly conservative sector, we asked expert Vladislav Kislov to answer our questions.
Vladislav Kislov is a results-driven entrepreneur with a strong background in business operations, finance, and service management. Founder and owner of a successful essential home services business in the U.S., generating $2.5 million in annual revenue with over $1 million in salaries. Skilled in managing high-volume service operations, overseeing a team that services 3,500+ houses per month, and optimizing business strategies for efficiency and growth.
Vladislav is part of The Ventures, an exclusive community for tech founders and influential leaders. The club offers valuable venture capital insights and strategic tools to its members. In 2025, Vladislav was honored with the Cases&Faces Award as the “Manager of the Year” in the “Consumer Services” category. The Cases&Faces Award celebrates exceptional ideas, projects, trends, and individuals driving innovation in fields like science, culture, education, entrepreneurship, management, social initiatives, gender equality, creative transformation, and digital technologies. Its mission is to provide both expert and public recognition to those behind groundbreaking contributions.
How does the implementation of artificial intelligence in the process of diagnostics and repair of household appliances help reduce costs for service companies and improve customer service?
The implementation of AI significantly reduces the time spent on diagnosing faults and lowers the likelihood of errors. Instead of spending time manually searching for the cause of the malfunction, the technician receives a precise action plan and a list of potential issues based on the analysis of the device’s symptoms. This reduces the costs of repeat visits, increases service speed, and minimizes expenses on unnecessary spare parts. As a result, customers receive faster and more accurate service, while service companies reduce operational costs.
What real results have you already achieved thanks to the use of AI in your business? Can you share specific examples? For instance, has repair time decreased or have errors reduced?
We have already observed a significant reduction in diagnostic time—on average by 30–50%, depending on the type of device and the complexity of the issue. The number of errors in spare part orders has also decreased, as AI helps technicians select the parts that are most likely to be needed. Another important indicator is a reduction in repeat visits by about 25%, as the initial diagnostics are now more accurate.
What key AI technologies are used in the process of diagnosing household appliance faults, and how do they help speed up the process?
We use machine learning and natural language processing (NLP) to analyze problem descriptions provided by customers. AI also analyzes data from previous repairs to identify the most likely causes of malfunctions. Another important technology is computer vision, which allows technicians to upload photos of faulty parts, and the system suggests possible defects and recommended actions.
How do AI systems help reduce the number of returns and repeat repairs? What changes in business processes have this caused?
Thanks to accurate diagnostics in the initial stage, the likelihood of incorrect repairs is reduced, which in turn decreases the number of repeat visits. AI also helps technicians verify the functionality of the device before completing the repair, further reducing the risk of returns. As a result, business processes have become more standardized: technicians now follow clear algorithms rather than relying solely on personal experience.
How do AI systems help reduce the role of the human factor in diagnosing faults, and what advantages does this offer in terms of accuracy and reliability?
The human factor is one of the main causes of errors in diagnostics, especially if a technician is inexperienced or dealing with a rare malfunction. AI operates based on statistics, eliminating subjectivity, which improves diagnostic accuracy. As a result, newcomers learn faster, and the likelihood of errors is reduced for experienced specialists.
How do you assess the economic efficiency of using AI in appliance repair from the perspective of long-term costs and profits? What would you recommend to companies that are just beginning to implement such technologies?
The long-term economic efficiency of AI is evident in reduced costs associated with errors, repeat visits, and spare parts, as well as increased productivity for each technician. This allows more orders to be processed without increasing staff. Companies that are just starting to implement AI should first identify key problem areas—such as a high error rate in diagnostics or long order processing times—and begin automation with those.
What do you see as the future of AI in appliance repair? What new opportunities and prospects are opening up for service companies and consumers?
In the future, AI will be able to fully automate diagnostics, making it even faster and more accurate. Predictive analytics systems may emerge, alerting customers to potential malfunctions in advance based on sensor data from their appliances.
Additionally, AI could become the basis for remote support, where customers can independently resolve minor issues under the guidance of an intelligent assistant. This will open up new business models for service companies, such as subscription-based technical support instead of one-time repairs.
