KG addresses a growing need for training and education in automotive after-sales arising from the increasing complexity of modern car technology. Today, cars are high-tech machines. An average new car has more lines of code than Facebook and more micro-controllers than a fighter jet. A lack of technicians who understand complex electronics results in more wrong diagnoses and repeat trips to the mechanic than any other factor. Automotive training is still mostly done the same way as decades ago: in training centers, with inefficient methods that have little scalability. Technicians periodically travel to a training center for classes of predetermined content. Many mechanics don’t get trained as often as they should and increasingly fail to properly repair modern cars. Finding skilled motor mechanics today is already a serious problem. In the UK, for example, 80% of independent garages say they have trouble finding able technicians, and there will be a need for 25,000 mechanics in the USA by 2020. This problem will intensify as electric vehicles (EVs) are increasingly on the road. Industry estimates predict up to 20 million EVs in circulation by 2020. There is an urgent need for education and training to update technician’s skills with EV tech. Only 1% of technicians are qualified to repair/maintain EVs.
KG's goal is to dramatically improve the quality of after-sales services by breaking down the barriers to delivering technical knowledge in the field. To achieve this goal, KG is striving to become the leading, centralized knowledge platform for professionals across the industry, enabling technicians to get training on demand through Self-Service Training and supporting them on the job through Automated Diagnostics.
The feasibility assessment completed in this project has strongly confirmed the market need for and client interested in these two solutions, thus providing strong support for the continuation of the project. In addition, the assessment also produced important learning for the successful market introduction of these two solutions. For Self-Service Training, a minimal amount of training content and pre-defined use cases needs to be available with the solution upon purchase to facilitate implementation and engage users to develop their own use cases. For Automated Diagnostics, a clearer positioning and more granular differentiation is necessary in order to communicate more effectively the product’s USPs, particularly over clients’ existing diagnostic solutions.
