The use of artificial intelligence (AI) is a central element of the digital transformation process at the BMW Group. The BMW Group already uses AI throughout the value chain to generate added value for customers, products, employees and processes.
Michael Würtenberger, Head of “Project AI”: “Artificial intelligence is the key technology in the process of digital transformation. But for us the focus remains on people. AI supports our employees and improves the customer experience. We are proceeding purposefully and with caution in the expansion of AI applications within the company. The seven principles for AI at the BMW Group provide the basis for our approach.”
The BMW Group continues to follow global developments in terms of both technological innovations and regulatory and ethical issues. Together with other companies and organisations, the BMW Group is involved in shaping and developing a set of rules for working with AI, and the company has taken an active role in the European Commission’s ongoing consultation process.
Building on the fundamental requirements formulated by the EU for trustworthy AI, the BMW Group has worked out seven basic principles covering the use of AI within the company. These will be continuously refined and adapted as required according to the multi-layered application of AI across all areas of the company. In this way, the BMW Group will pave the way for extending the use of AI and increase awareness among its employees of the need for sensitivity when working with AI technologies.
Seven principles covering the development and application of artificial intelligence at the BMW Group:
- Human agency and oversight.
The BMW Group implements appropriate human monitoring of decisions made by AI applications and considers possible ways that humans can overrule algorithmic decisions.
- Technical robustness and safety.
The BMW Group aims to develop robust AI applications and observes the applicable safety standards designed to decrease the risk of unintended consequences and errors.
- Privacy and data governance.
The BMW Group extends its state-of-the-art data privacy and data security measures to cover storage and processing in AI applications.
The BMW Group aims for explainability of AI applications and open communication where respective technologies are used.
- Diversity, non-discrimination and fairness.
The BMW Group respects human dignity and therefore sets out to build fair AI applications. This includes preventing non-compliance by AI applications.
- Environmental and societal well-being.
The BMW Group is committed to developing and using AI applications that promote the well-being of customers, employees and partners. This aligns with the BMW Group’s goals in the areas of human rights and sustainability, which includes climate change and environmental protection.
The BMW Group’s AI applications should be implemented so they work responsibly. The BMW Group will identify, assess, report and mitigate risks, in accordance with good corporate governance.
“Project AI” was launched in 2018 to ensure that AI technologies are used ethically and efficiently. As the BMW Group’s centre of competence for data analytics and machine learning, it ensures rapid knowledge and technology sharing across the company. Project AI therefore plays a key role in the ongoing process of digital transformation at the BMW Group and supports the efficient development and scaling of smart data and AI technologies.
One of the developments to come out of Project AI is a portfolio tool which creates transparency in the company-wide application of technologies making data-driven decisions. This D³ (Data Driven Decisions) portfolio currently spans 400 use cases, of which more than 50 are available for regular operation.
WHERE IS THE BMW GROUP ALREADY USING AI?
USE CASES FROM DIFFERENT AREAS OF THE COMPANY.
The following examples show that Project AI pushes the BMW Group forward with AI focused, company-wide networking and knowledge transfer. The fundamentally identical technology forms of AI can generate added value for customers, employees and business processes. For example, the customer benefits from natural language processing (NLP) with the Intelligent Personal Assistant directly in the vehicle and employees are supported with translation tools and context-processing assistants in administrative processes. Intelligent data analysis and machine learning are used to optimise energy management both in buildings and in vehicles. And image processing AI relieves both the customer with driver assistance systems from monotonous driving tasks and employees in production from monotonous processing steps.
E XAMPLES FROM RESEARCH & DEVELOPMENT.
AI-based energy management in vehicles.
A vehicle contains a large number of electric consumers, such as seat heating, the entertainment system, the air conditioning, etc. In many cases, the driver is not aware that using these consumers also has an effect on CO2 emissions and/or the range of the vehicle. AI experts at the BMW Group are conducting R&D work on AI-based software for in-vehicle energy management. Taking user behaviour and route information as a basis, the system learns how to adjust energy consumption in the car as effectively as possible to the driver’s requirements and the need for energy efficiency. In this way, CO2 emissions can be reduced, energy saved and operating range increased.
Acoustic analytics: sensory enhancement in the sensor model for automated driving functions.
The BMW Group is taking an all-encompassing approach to monitoring the vehicle environment. One of the areas the company is exploring to this end is how acoustic signal processing can be added to the AI sensor fusion. Incorporating auditory perception can have benefits for urban scenarios, in particular, going forward.
AI in requirements management.
At the BMW Group there are over 33,000 requirement specification documents containing more than 30 million individual requirements for vehicles, components and characteristics. That’s an enormous amount of data. AI technologies can help employees to process large quantities of data more quickly and carefully. Here, an application has been developed which uses natural language processing methods to improve the quality and analysis of individual requirements in specification documents. The web-based tool allows thousands of requirements to be automatically translated and checked – in real time – for linguistic quality, similarity and consistency
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