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September 2023

SBD Explores: Automotive AI Use Cases

There are many forms of AI. Which one
is right for your needs?
10-minute InsightGenerative AI has taken the world by storm this past year.
Many industries both inside and outside automotive arelooking to leverage its power to increase profits.
However, while generative AI can be applied to many use
cases, other forms of AI may be better suited in someinstances.

In this insight, SBD explores different automotive use cases
and the type of AI which can best support it.

Target audienceProduct planning / Strategy

Focus market(s)GlobalEngineering / Investing1

What is happening?
Key takeaway

Consumer Experience

Car Features
Next-Gen Virtual Assistant

Car Services
ADAS / AV(Most OEMs)

Predictivemaintenance

Examples ofrecent
implementations

AdvancedVehicle
diagnostics

OEM Business Process

Finance &Administration

Research &Development

Supply Chain
Management

Production &Manufacturing

Sales &Marketing

Intelligent security
centerSW developer copilot

Material scienceSmartfactory/digital twin
factory

Sales planningadvisor

Vehicle testing

Product design

Synthetic data forADAS

Supply Chain Risk
Assessment

The latest advancements in Generative
AI (Gen AI) have demonstrated
significant potential across various
business processes, prompting OEMs toreevaluate their AI strategies andapplications.
• AI can support a wide range of businessactivities. But it can be categorized into
two main objectives, consumer
experience enhancement and OEM
business process optimization.
• Most OEMs today prioritize integrating AI
into modern car features like Virtual
Assistant and ADAS. Yet, we see many
opportunities for in-car services and OEM
business processes, especially in R&D.

• While Gen AI has attracted lots ofattention recently, it might not be thebest fit for all use cases.
• We anticipate there will be numerous AI
initiatives within OEMs spearheaded bydiverse teams. This will need high level
prioritization and coordination to ensure
maximum efficiency and best Return on
Investment is achieved.
• Mercedes-Benz is becoming a leader inautomotive AI by investing more in AI
than others, including an employee AI
training program.
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Why does it matter?
Use Cases

AI Category

Relative Maturity

Complexity

Data Type

Next-Gen VirtualAssistant

Gen AI (LSTM,Seq2Seq)

MediumMediumVoice, Text

ADAS / AV System

Deep Learning (CNN,Reinforcement
Learning)

MediumHigh

Image, Video, Radar / Lidar Data

Predictive maintenance

Machine Learning(Time Series Analysis,Regression)

Low

MediumTime series, Sensor data

Advanced vehicle
diagnostics

Machine Learning(Decision Tree, SVM)

Low

MediumSensor data, Device logs

Low

High

Network logs, System logs.

Intelligent Security
CenterGen AI
Deep Learning (CNN)

SW development
Copilot

Gen AI

MediumMediumSource code，Text

Vehicle Testing

Gen AI (Decision Tree)

MediumMediumSimulated data, Text, Code

Generative DesignGen AI (GANs)

MediumMedium

3D Models, Design parameters

Synthetic data for ADASGen AI (GANs)

MediumHighImage, Video

Material science

Deep Learning(Regression)

Low

High

Lab experiment data, Physicalproperties

Low

HighSensor data, Factory system logs

HighLow

Text, Image, Customer
interaction records

Smart factory/digital
twin
Customer service agent

Deep Learning
Gen AIGen AI

Key takeaway
The relationship between AI models canbe complicated. At a high level, thesemodels can be viewed as subcategories
of one another with each one providing
additional functionality. Understanding
which model to apply to each use case iscritical for effective implementation.
• Machine Learning: A form of AI that canbe trained to perform tasks which may
be automated. It can also identify andclassify data in a structured form (i.e., intabular form). Automotive applications
include predictive maintenance, remotediagnostics in cars. Machine learningneeds less data and runs smoothly on
modern vehicle MCUs/MPUs.

• Deep Learning: A subset of machinelearning which uses a neural network toanalyze data. It outperforms machinelearning when unstructured data is used(image classification, natural language
processing, video, etc.). Deep learning is
generally more about recognizing
patterns, making decisions, or extracting
features from data.
• Generative AI (Gen AI): A subset of deeplearning. Gen AI can read data, learn
from it, and generate new synthetic
instances. This form of AI works well fora situation which requires adaptiveoutputs.
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Where Next?
Key takeaway

Smart Factory /Digital Twin
ADAS / AV System

Investment

Synthetic Datafor ADAS

Next-Gen VirtualAssistant
Supply Chain
Risk Assessment

Predictive
Maintenance

AdvancedVehicleDiagnostics

Generative
DesignCustomer
Service AgentIntelligent
Security Center

Material Science

Vehicle Testing
SW Development
Copilot

Implementation Time

SBD has evaluated some use cases
related to the automotive industry anddetermined which ones should be ofhigh priority based on implementation
time and investment.
• The LLM-powered virtual assistant willtransform in-car experiences with its
deep grasp of human intentions, multiobjective capability, and swift responses.
However, integrating LLM-powered
virtual assistants into vehicles andmaking a seamless experience on theroad requires more fine-turning effort.
• Predictive Maintenance and Advanced
Vehicle Diagnostics can be facilitatedusing traditional ML algorithms,
eliminating the need for Gen AI.
However, gathering such data demands arobust information infrastructure for boththe vehicle and its service network.
• Customer Service Agent on either an
OEM website or app can be a quick win
to leverage Gen AI with very little finetuning and can be implemented inweeks.
• Smart Factory takes a longer time andmore investment to build and fine-tune
with digital twin technology. However,this is an ideal moment to begin as mostOEMs are preparing their new EV plants.
4

What to watch out for?
SlowerRelative Development Rate

Key takeaway
FasterAI Building

Machine Learning

Deep Learning

AI Deployment

Generative AI

Edge AI

While AI can be overhyped, it is still avery powerful tool when implemented
correctly. However, the entry ticket canbe expensive depending on the model
used and there are uncertainties andconcerns when applying it into
enterprises. SBD has the following
suggestions:• AI will be embedded into different layersof technology stacks for future vehicles.
It will be a combination of different small
and large models running from chip tocloud. Monitoring industry trends will
help OEMs to refine their AI strategy.
• The operational cost of LLM-Inferencing
remains high and should be a primaryconsideration for large-scale
implementations of Gen AI. However, it
does save cost on data annotation during
the model training.
• Watch edge AI players. Research is
ongoing into model compression methods
to optimize complex large models so they
can run effectively on embedded systems.
• Some players in the chart offer both AI
tool chains and training datasets. OEMs
lacking in data might consider sourcing itfrom external parties.

5How should you react?

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3Prepare
All intelligent AI models need to be
trained with quality data. Lookinternally for where this data cancome from or explore third party
options to acquire data.

Prioritize
Identify weak points within yourcompany where automation andefficiency can be improved. Consider
different types of AI models to use
depending on the use case.

Authors

Andrew Wilczynski
Research Analyst

Michael GrafAndy Qiu

Research Analyst

Domain PrincipalRelated SBD Report
Ref:202
Start-up
Tracker

Ref:201
Innovation
GuideMonitor
See how the AI model performs andadapt as needed. If AI wasimplemented in a consumer facing
use case, respond to customer
feedback to improve themodel/system.

Related SBD Consultancy▪ Cyber security assessment▪ Technology forecasting▪ UX and CX▪ Strategic advisory

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