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in Cars

Yami

Created on March 13, 2026

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Transcript

Enzo Boquet

AI

in Cars

Erwann Leprohon Balis

Automatised driving

The security

AI

in cars

Users experience

THANKING

Vehicle manufacturing

Automatised driving

  • cameras
  • sensor
  • radars
  • road surveillance

Ai in Security

  • Driver Attention Alert (audible and visual alert )
  • Driver assistance(crossing lines, obstacle detection...)

AI in Construction

-dark factories -no more employees -don't need lights for product

Advantages of AI

Improve: -effectiveness of resolve problems -logistic -supply chain

Disadvantages

-Hacked -Increase price of the vehicle -can be dysfunction

How to integrate AI into the organization

Steps for effective adoption

Foster a culture of innovation and continuous learning.

Ensure quality data to train reliable models.

Identify processes with high automation potential.

Measure impact with clear KPIs (cost, time, efficiency).

Start with pilot projects and scale based on results.

Where AI enhances performance

Direct impact on corporate areas

HR

CustomerService

Operations

Marketingand Sales

Selection, training, and talent analysis.

Automation, inventory control, and predictive maintenance.

Customization, segmentation, and demand forecasting.

Chatbots, self-service, and 24/7 support.

Artificial Intelligenceas a strategic driver

Efficiency and growth

Generate new business opportunities and competitive advantage.

Free up talent for higher value-added activities.

Accelerate decision-making through predictive analysis.

Optimize internal processes and reduce repetitive tasks.

How to integrate AI into the organization

Steps for effective adoption

Foster a culture of innovation and continuous learning.

Ensure quality data to train reliable models.

Identify processes with high automation potential.

Measure impact with clear KPIs (cost, time, efficiency).

Start with pilot projects and scale based on results.

Ethical AI and corporate governance

Secure and sustainable implementation

Training and staff adaptation to prevent resistance to change.

Bias prevention in models and algorithms.

Responsible data management and privacy.

Transparency and human oversight in automated processes.