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The road to artificial intelligence in mobility--smart moves required

The road to artificial intelligence in mobility--smart moves required

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SUMMARY

This report finds that automotive OEMs and industry players are positioned to lead the development and application of artificial intelligence (AI) technology, while consumers have a positive outlook on AI use in mobility.

Although the idea of artificial intelligence existed since the 1950s, the emergence of machine learning (ML) and deep learning (DL) has helped AI functionality. Narrow AI applications may perform better than humans but in specific objectives. This report focuses on AI systems that use ML and DL to create new features or enhance current operations.

Machine learning can enhance three key objectives in the automotive-mobility industry. ML can make reasonable decisions during complex situations; ML can manage a large number of possible situations. Lastly, ML uses data from previous situations to improve performance. These high-value tasks are appreciated by consumers. Only 25 percent of consumers see a major risk associated with AI. Comfort and convenience are key drivers of this openness. 

What automotive and mobility areas can machine learning disrupt? The report identifies three categories in the automotive and mobility sector: i) Optimizing production and processes, ii) Augment products, and iii) New emerging business models and use cases. Likewise, there are three challenges: i) embedding technology into vehicles; ii) industry leaders, governments, and policy standard organizations must cooperate to regulatory standards; and iii), policymakers must decide suitable business models.

OEMs are positioned to profit from AI developments. However, to overcome AI development and deployment challenges, there is work to do in the automotive and mobility sector. To successfully apply AI technologies, automotive and mobility players can work together to drive regulatory standards, focus on core use cases, manage new business models, including the collection of data, and support tech and business partnerships.

OUTLINE

AI in mobility and Automotive


Opportunities in ML based automotives


Challenges


Deploying ML


Outlook


Appendix

DETAILS

Overview

Researchfinder Rating
5 out of 5 stars
Title
The road to artificial intelligence in mobility--smart moves required
Region
Global
Published
Sept. 30, 2017
Publisher
McKinsey & Company
Author(s)
Andreas Cornet, Matthias Kässer, Thibaut Müller, and Andreas Tschiesner
Price
FREE
Language
ENGLISH

Content

Number of Pages
10
Number of Tables
0
Number of Exhibits
6
Topics
ai
Tags
deep learning, machine learning, McKinsey, mobile application, mobility
Author(s)
Andreas Cornet, Matthias Kässer, Thibaut Müller, and Andreas Tschiesner
Methodology
The findings in this report are based on a start-up and investment landscape analysis, and a consumer survey. Interviews and in-vehicle experiences support the findings. McKinsey surveyed 3,000 consumers in China, Germany, and the United States. The publishers interviewed industry leaders, including automotive influencers, technology players, and academics.
EVALUATION

This report comprehensively analyzes the current automotive and mobility industry as well as how players are directing artificially intelligent technology.


There are diverse exhibits that are accessible. However, the data is solely based on McKinsey's research. More secondary research is necessary to broadly understand AI deployment in the automotive and mobility sector. The findings are inspirational to tech and automotive players because there is a lot of innovation and growing development.


Positives

  • Visual data is accessible
  • Clear writing

Negatives

  • Requires broader (secondary) data

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