The Garage by Sonatus

In a live recording from COVESA all-member meeting in Porto, Portugal, John and Omdia analyst Maitê Bezerra discuss the 2026 SDV survey, which reveals the industry expects to reach SDV level 3 by 2030. Key findings highlight a shift toward data-driven value creation and predictive maintenance, though organizational readiness still lags behind technology deployment by several years.

Chapters:
0:00 Introduction to COVESA Meeting
1:29 Guest Introduction: Maitê Bezerra    
2:13 Transition from Wards Intelligence to Omdia
4:03 Key Conclusions from the SDV Survey
5:25 Understanding SDV Levels
7:01 Current State of Vehicles on the Road
9:52 Reality Check on Technology Deployment
12:04 Near-Term Future of Technology Implementation
13:01 Diversity of AI Use Cases
15:17 Shifts in Data Monetization Strategies
18:55 Regional Variations in Data Strategy
22:15 The Role of AI in Automotive
26:44 Predictive Maintenance as a Key Focus
30:28 Accessing the Survey Results

Creators and Guests

Host
John Heinlein, Ph.D.
An experienced technology and marketing leader, John brings his background from startups and established companies to Sonatus. He worked for 14 years at Arm, most recently leading Automotive Partnerships for North America where he engaged OEMs, Tier-1s, and others to deploy Arm-based solutions into automotive applications, including autonomous vehicles. His team was integral to launching the SOAFEE industry initiative for software-defined vehicles of which Sonatus is a member. Earlier he served as VP and Chief of Staff to the CEO and led a group responsible for competitive strategy. For three years, he was VP of Corporate Marketing where he led centralized outbound marketing, spanning marketing campaigns, press, events, web, digital marketing, ecosystem programs, and working closely with investor relations. Prior to Arm, John had an 11-year tenure at microprocessor startup Transmeta where he held several senior roles spanning business development, marketing, and customer success, among others. John earned his B.S. in Computer Engineering from Carnegie-Mellon University and an M.S. and Ph.D., both in Electrical Engineering, from Stanford University.

What is The Garage by Sonatus?

The Garage Podcast from Sonatus brings you conversations with thought leaders from around the vehicle technology space discussing far-reaching topics about software innovation in vehicles.

These episodes will include industry experts, whether from Sonatus, or from our amazing partners across the industry, all of whom will share their Ideas and their outlook for the most important topics in vehicle technology. Episodes span from more technical topics, to business evolution, and market trends. Join us and learn about the future of vehicle in The Garage.

Today in The Garage, we're
recording live at the

[2026] COVESA all-member
meeting in Porto, Portugal.

One of the things that we
do every year at the COVESA all

member meeting is Sonatus
has worked with Omdia,

an important research
firm in the industry,

to survey the industry and
understand the trends in

software-defined vehicles.

This is our second year
sponsoring the annual SDV survey.

And last year,
working with Omdia,

we significantly expanded the
survey set to a much larger pool.

We found incredibly valuable
conclusions last year about how

people across the industry
were driving ahead towards

software-defined vehicles,

what they felt was important
and value-creating,

what are some positive signs,

and what are some warning signs about
how technologies are being deployed.

In this week's episode,
we talked with Maitê Bezerra,

who's principal analyst for
software-defined vehicles from

Omdia.

We go through the important
conclusions of the SDV survey

and look at it from a
lens of different regions,

different subgroups,

and what we can take from it
to learn about the evolution of

software-defined vehicles.

It's a very exciting update to this
year's survey compared to last year.

We look forward to sharing
with you. Let's go!

Welcome to The Garage.

We're so excited to have Maitê
Bezerra here with us today.

Maitê, welcome to The Garage.

You have the distinct honor to be the
first three-time guest of The Garage.

Because you've been here many
times talking about this SDV

survey a year ago and so on.

But before we jump into that,

I want to give you a
chance for our guest to introduce yourself

and tell us about your
company and your role.

Thank you, John.

First of all, thank you for
having me for the third time.

So I'm a principal
senior analyst,

in software-defined vehicles,

and I lead the digital
mobility coverage for Omdia.

So I have been looking at
software-defined vehicle as a

transition for the automotive
industry for quite a while now,

and pretty happy to be working
in the survey with you.

So, you know, I know you were with
Wards Intelligence for a long time,

and I think maybe you want to
explain for people the kind of

changes that have taken place
over the past couple years.

Of course. That's
a very good point.

So we were all Informa
companies. Right?

But I was
with Wards Intelligence before,

but now we transitioned the
name to Omdia because we merged

with the Informa Research Group.

But we are essentially
the same company now,

just a different name.

Okay. Fantastic.

So the SDV survey,

you've been running this
for quite some years.

This is our second year
sponsoring the survey with you.

Last year, one of the things we did
was expand the sample size so we

were covering more
geographies and more people.

So can you begin by telling us
about the survey methodology

and how many responses we
got as part of the survey?

Of course.

Well, it's interesting you say
this because we are at COVESA,

and I was in COVESA in 2024,
and presented the survey.

The sample size was a hundred.

So I'm very pleased with the
sponsorship because now we have

a five hundred and
fifty nine sample size,

which we had last year
and we repeated this year.

And with our sample,
we always try to get,

an equal distribution of
automakers and suppliers.

Right?

So we have 50% automakers
and then 50% divided between

tier one suppliers and
tier two suppliers.

We also try to
get representative

geographical distribution.

So we have seven countries
in North America, in Europe,

and in Asia as well.

That's phenomenal.

And we recognize it was so
important to be across the world.

And we also saw that the
data was so valuable,

we really wanted to
get a good sample size.

Now we both have our iPads here
because there's so much data,

and we have to go into the
saying that what we're gonna

cover is a tiny tiny
fraction of this total survey content,

but we tried to pull out some things
that we thought were really relevant.

First, what are some of the
key conclusions we took away?

So I would say the first
conclusion is about the state

of the industry.

So if you think about
software-defined vehicle as a

technology that has
zero to five levels,

our respondents really think
that we are in the zero

to two levels today.

Right? So level two is the max
that we have achieved today.

Right.

And we're not talking
about self-driving,

SAE self driving levels.

This is SDV maturity levels,

which we'll introduce
on the next slide.

But sorry, please tell us.

No. I'm glad you make this
difference in between the two.

And our respondents think that
we're gonna reach SDV level

three by 2030.

So, really, we are the
inception of this transition.

The second thing we noticed was we
kind of called the great rebalancing.

Data, of course,

continues to be critically
important to the industry.

There's no change in that.

But in last year's
survey result,

we saw a big focus on data
monetization, and we're seeing,

and we'll get into
this in more detail,

a shift towards
more creating value,

creating new capabilities instead
of just monetizing data directly.

A very interesting conclusion.

Yeah. I really like that one.

But another thing that was
really interesting is that when

you think about readiness for
software-defined vehicles,

we see that a lot of the
enabling technologies are going

to be deployed in
the near future,

but we are still lacking when it
comes to the organizational readiness.

So there's a kind of delay of
four to seven years in between

technology and
organizational readiness.

So technology is there,

but are the companies
ready to take it in?

It's really
interesting. Precisely.

So let's begin with
this these SDV levels,

and maybe you can introduce the
SDV levels and talk about what

were some of the
conclusions we saw.

So this survey, it's
based on this framework

that we're trying to
standardize the SDV levels.

Right?

There are lots of important
people in the industry that

came up with levels,

and what we're trying to do is
get these people together and

come up with the common
areas of these levels.

So trying to to explain
them very simply.

Level zero is a car that is
not connected and the software

remains static unless you take
the car to the dealership.

SDV level one is when you have
embedded connectivity and you

have OTA firmware updates.

SDV level two,

you will start introducing new
functionalities over the air,

but only in the infotainment.

SDV level three,

you add new functionalities to
nearly all vehicle domains and areas.

SDV level four, full abstraction
of hardware and software.

So regardless of your
hardware version,

you have access to
the latest software,

like it happens with
the iPhone today.

And SDV level five, it's
the agent orchestrator.

So it's when you have agents
that become sort of the brain

of the car, and they are
working autonomously,

inside the vehicle,

but they also connect the
vehicle to the external world

through cloud or connecting to
consumer devices or the ITS.

Now, John, we put
these levels for the

respondents, and we asked them,

where do you think most of the
vehicles on the road today are?

So what did we find?

So as of today, as of 2026,

the the predominant level was
level two with about thirty

percent of the
vehicles at level two,

which is providing
some upgradability,

some limited upgradabilty.

But looking forward
to twenty thirty,

the expectation in 2030 is around
30% will be at level three,

which is what we call a real
beginning level software

defined vehicle with obviously
some amount of vehicles at

higher levels and and
a number below that.

So it's showing a shift from
level two to level three over

the next four years
as the center spot.

Which makes sense.

And it's it's quite interesting
that the industry is aware that

we we are only at the
beginning of the journey.

The next thing we looked at
was the timeline of critical

ingredient
technologies for SDVs.

And this chart, which
is very complicated,

you can see on the screen and
we'll walk you through this,

is looking at what was the
timeline that respondents felt

each of these respective technologies
was going to be deployed.

And what you see at the top,

and the first four technologies
are ones that are over 20%

where respondents feel these
technologies were already deployed.

And then there's a number of
other important ingredient

technologies that
were below 20%,

starting with things like
service oriented architecture,

time sensitive networking,
containerization, and hypervisors.

But then what was interesting
is looking forward to the

future, what the
respondents said.

What was the outlook?

So this is what I
call a deployment cliff. Right?

So this is when we
look at the data,

most of the respondents believe
that the enabling technologies

of SDV will be deployed
in between 2026 and

2027.

So it's really really in that
between that the enabling

technologies of SDV
are going to be there.

Right?

So I I thought that this
this is a it's really it's

interesting, but also
it poses, you know,

some areas for thought.

So if everyone is
deploying at the same time,

we may face some bottlenecks,

we we may have some
problems with interfacing

of all of technologies.

So it's important
to prepare for it.

So it's interesting because the
survey respondents were from

OEMs, from tier ones, tier twos.

There's a lot of different
people coming together.

So if you look at the
the bars on the chart,

the dark blue bar on the left
is those respondents that

thought these technologies
were already deployed already.

And then the next bar,

which is the sort of the lighter
blue with the red outline.

So if you look at that combined,

what you're seeing is more
than fifty percent of all of

these technologies are
expected to be deployed,

and in some cases,

more than fifty percent
by the end of 2026/2027.

Now we'll see on the next
coming slides why there may be

some reason for pessimism, why
it's quite this aggressive.

But what it does suggest,
as you said, the cliff,

which I think is interesting,

is to think that a lot of these
technologies are right at the

precipice of being deployed.

And if not, maybe not
exactly 2026/2027,

certainly going through the
process of being deployed.

And there is some optimism
about these ingredients,

which are crucial
to making SDVs.

Exactly.

The recognition that these
are the enabling technologies.

Right?

Which is an excellent evolution
from where we were a few years ago.

Right.

Right?

Where people were asking,

what are the key
technologies for SDV?

Right.

So if we look at the next slide,

we call this the reality check.

And this was looking at
the survey results of

what technologies were listed
as already implemented.

So this is looking backwards in
what people said in last year's

survey and what people said
in this year's survey in which

technologies were already
implemented in the past.

Now we should point out that
this survey, large sample size,

has a margin of error
around four percent.

So you have to take with
a pinch of salt when you see

things less than four percent.

So what we saw is three
technologies are showing

increase in what the
respondents said were already

deployed this year
versus last year,

which is containerized
applications, hypervisors,

and time-sensitive networking.

Now of these, only containerized
workloads were which was showing ten

percent is above
the margin of error.

Hypervisors and TSN at five
percent was just a hair above

the margin of error,

but nonetheless showing a positive
trend for these technologies.

But then below, there's some cautionary
tales on some other technologies.

Yeah.

So when you go to technologies
such as COVESA VSS or

microservices, we see that
we have a lower percentage of

people saying that these
technologies have been deployed.

Now what this indicates
and as you can see,

it is within the
margin of error.

So what really indicates is
that we didn't really see much

progress, Right?

These technologies are very
likely more complex to be

deployed than these
people thought.

So it's not necessarily
fully deployed.

But, yes, as you say,
let's take a step back.

Looking at this, what essentially
it's saying is we haven't moved

significantly ahead, but still
deploying this technology.

So obviously,

you take with a pinch of salt
that that that there's some

noise in the system,
but regardless,

some reasons for optimism,

but still some technology
still in front of us.

Then we looked at
the near term future.

So this is a similar analysis,

but not what's
already implemented.

But what would be implemented in

2026/2027 thought of as last
year's survey versus what will

be implemented in 2026
in this year's survey.

Very interesting results here.

What do you take away from this?

So it's interesting because
we're talking about the

deployment cliff.

So most of technology
is being deployed within

2026/2027.

But when we compare this
survey with last year,

the the percentage of people in
this bracket is actually smaller.

So the timelines have
increased a little bit. Right?

So overall, when
you look at this,

when you look at
these technologies,

hypervisors and etcetera,
microservices, it's actually

they are going to be deployed,

but I would say that this
2026/2027 timeline i actually

2027/2029, right?

So if we if we actually take
this survey, take a step back,

we can actually say that these
enabling technologies will be

deployed by the
end of this decade.

It's important when you're
doing a survey to ask questions

in multiple ways,

because a lot of times you
can you can tease out trends where

maybe there's optimism in one
question and there's pragmatism

in other questions.

And what this says is that these
technologies are at this precipice.

They are at this
deployment cliff,

which is good for the industry.

But we shouldn't be, you know,

counting our chickens that
every one of these things is

going to be happening
in the next year,

but we need to
watch them closely.

Interesting.

One of the things that that
is particularly notable is

hypervisors and
virtual machines,

which we talked about before is
something that is is growing.

Actually, the optimism of that deployment
actually has really fallen off.

That's a notable one that's
fallen off in this year's

survey compared to last year.

Yeah.

And I think this is, well,
we can take, of course,

their external factors for
this sort of, you know,

I wouldn't say lack of optimism,

but it's actually realization.

Right? Yeah.

We had the tariffs, we have
the geopolitical situation.

But I think when it
comes to hypervisors,

it's we still have this discussion
on where should the workloads run.

Should we mix the criticality
with infotainment?

So I think this is still like,

how do we separate these
these different workloads?

This is still a question.

I agree.

I think it's for me one of
the most interesting things to

watch because a number
of different, you know,

silicon providers and
tier ones, tier twos,

they all have a slightly
different take on that.

And there's and you've we've
also done another question,

which we're not going
to summarize here today,

looking at vehicle
architectures and what you're

seeing is a huge
diversity of thought.

You would think
that at this point,

when you think about phones
and think about other devices where

there's a sort of a fairly
clear architecture and so on,

in vehicles, you're seeing
tons of heterogeneity still.

And I think that that's bearing
out in these hypervisors

because it's affecting
not the hardware,

but it's also affecting
the software, how it goes.

You're spot on.

You know, I usually call it
people say hybrid architecture.

I call it a hybrid zonal
architecture. Right?

Not just zonal because
everyone has a different way of

interpreting what zonal is.

That's true. That's
true. Yeah. Absolutely.

Then moving on to data.

And we're here at
COVESA, and, you know,

despite some a little bit of
pessimism of the deployment of

VSS, Nonetheless,

we're all here because we
believe the importance of data.

We believe in the importance
of standardization of vehicle

data, and we are
seeing a proliferation,

but maybe a little
slower than we thought.

But we also asked

survey respondents, what would
what data collection use cases

provide the most value to OEMs?

And one of the
interesting trends,

we mentioned this
at the beginning,

was that last year's response,

the number one response
by a mile was vehicle data

monetization, which if I were
to summarize it in a way,

it's like directly
charging for a feature.

Pay for this feature, and we will
get direct revenue from that.

That was fifty one percent of
the respondents by far the the

strongest response by a lot
last year was the top answer.

This year, that's changed.
It's dropped a lot.

And also it's
dropped regionally.

If you look at the the
responses around 51%

last year said it
was number one.

Now it's down to 44%.

But there's still a number of
other applications of data that

are still very strong.

And I think what we took away
from that was that data is

still important, but instead
of directly charging for data,

we're using the data
to create other value.

What were some of the other
things we saw in the survey?

Precisely.

And I think not only
just charging directly,

but also getting this raw data
and just selling the data.

Right?

I think the OEMs are
realizing that, first of all,

the data may not be as valuable
as they thought initially.

And also that there were some
problems with privacy, with consent.

So it's and I think there's
also this realization that

this data may give you a
better value if they are

used back.

So if you use this data
to improve your products,

enhance what you are offering.

Right?

So if you see ongoing
improvement in ADAS,

data driven product
development, it's something

that has grown.

Right? Right.

So it's the data going to
other areas within the company.

We also saw regional split.

Very interesting when we split
for the responses from people

who said that data monetization
was the top answer.

We looked at that,

and China is significantly down
compared to last year where

China said 59% of people said
data monetization was valuable.

Now it's down to 34%.

And I took away —we
chatted about this—I think that our

takeaway from this is China is not
saying they don't care about data,

but rather I think they're
using the data to improve their

products or using data to to
improve the driver and the

customer experience as opposed
to directly charging for data

capabilities themselves.

I think that's what
I took from that.

Me too. And a strong trend
in the US as well. Yeah.

Now one thing about Europe
that we have been discussing is

interesting because Europe
still thinks that data

monetization itself is the
highest application for OEMs.

And it could be that
Europe has a more

developed ecosystem
for data monetization.

There has been lots of
initiatives around this and

data sharing from OEMs.

Strong privacy
protections in Europe...

Precisely. Precisely.

Or it could also be
that when it comes to

deployment of SDV technologies
in production vehicles,

we see that other regions
are a bit ahead than Europe.

Europe's now catching up.

So perhaps Europe will
still face this realization

that there is more value in
actually using the data internally.

Yeah.

We'll see and you will see some
data later in the survey about

how some European perspectives
on some of the data are

different from some
of the other regions,

which is not to say it's wrong,

but it was a
different perspective.

Different. Yeah.

We also looked at
how the data strategy

varied by region more generally and
some really interesting trends here.

What were some of
things you you observed?

So it's when you look

at data monetization,
vehicle monetization,

we spoke about it.

Right? So going down
in some regions.

But one one thing that we see
that is strong everywhere is

using vehicle data to enhance
ADAS and autonomous driving.

And we have seen increased optimism
regarding autonomous driving,

especially now with end-to-end
systems that are capable of

handling situations that the
traditional rule-based systems couldn't.

We also see ADAS becoming
table stakes in China with BYD

launching ADAS in their
in their vehicle line.

Many many regions have
stronger requirements.

Europe has stronger
requirements for automatic

emergency braking.

Lane keeping is becoming
more and more standard.

And I think that it shows a
recognition that we can't think

of ADAS and autonomous
driving as a solved problem.

We think of it as a
continuously tuned problem as

we learn more, as
we gather more data,

and we improve things.

Precisely. Precisely.

And again, the data driven
product development,

I like this because it's
actually the OEMs thinking,

I can actually make a
sustainable business case if

I use the data, I
understand my customers,

I understand how my
products are performing,

and I make them better.

Right?

Another interesting trend we
noticed was personalization

where vehicle personalization,
which many people and and we

had Roger Lanctot in
the podcast as well.

We were talking about the
importance of personalization.

But regionally, the survey
respondents were were split on this.

China felt that far and
away personalization was a

key value creating aspect.

And you're seeing a lot of
Chinese OEMs perhaps coming

with consumer electronics
roots in some cases,

coming with a very
customer first,

customer facing approach.

Versus if you look at the
US, Europe, even Japan,

the importance of personalization
was much lower on the survey.

Very interesting trend.

I really like that.

It's a a really different way
of thinking of the car. Right?

Yeah. And what delivers value.

And I know that we know
in China, for example,

there's a big focus on
integrating with people's

digital experience more broadly,

with smart home and
so on like that.

That's probably less
proliferated in other regions,

I would say, partially because
of less standardization,

different, you know,

regionality in different
states and localities.

The other thing we noticed is
if you look at other uses of

data strategy,

there's a number of categories we
we split into some small pieces.

We looked at diagnostics
to reduce or avoid recalls,

diagnostics for better service
experience and customer

loyalty, as well as
data driven product

and and improvement,
which you mentioned.

If we think about those which
we split into small slices as a

mega slice, they would be
far and away the top usage,

and we'll see that in some
other data in a minute.

So using data to
enhance products,

using data to improve quality,
using data to enhance loyalty,

clearly a value creator from the
survey respondents across regions.

Yes. And it has been
validated in here.

I think it resonated
really well.

We we got a lot of
feedback from from people,

from automakers and suppliers
that this is actually the trend.

Yeah.

We should say that
here at COVESA,

just earlier this morning,

we presented this to the
COVESA all member meeting

participants, and we were
had a number of people,

as you're saying,

discussed with us afterwards
that they really appreciated

the conclusions, actually,

many different conclusions
I had people resonating with

after our presentation.

Which is great.

Now shifting over to AI.

Of course, AI is crucial
for autonomous driving and

ADAS. Of course, we know that.

So we actually said
to our respondents,

putting that aside,

because clearly that
would be the top choice,

what other applications beside
that are use cases for AI,

and what were some
of the conclusions?

Well, it was really
interesting that we have smart

diagnosis and predictive
maintenance as the top

application for AI.

Right.

So I like it because it's a

vehicle-centric application.

Right? It's delivering
value to your customer.

Another thing that was interesting
was the vehicle AI assistant.

So last year, we had vehicle
AI as vehicle assistant,

and he ranked around
fifth position.

Right?

Now this time, we added the
agentic part and it's right on top.

Yeah.

And I think that the point
you're making here is that when

you add the agentic element,

the voice assistant is not
just a voice assistant anymore.

It's actually an autonomous
system taking actions for you

in the background,

which can improve the vehicle
quality and the vehicle response.

Now it's important to say that
we we spoke in the SDV levels

that a full agentic system
level five is certainly probably in front

of us.

But the question, the
exact survey question was,

what are the most promising
use cases of AI in vehicles

besides ADAS and
autonomous driving?

So this may be
looking to the future.

But as you say, it's clear
that agentic AI is clearly a

value-creating thing that
people are looking at.

And there's a lot of
companies, including Sonatus,

that's spending a lot of
time focusing in that area.

Other things we talked about,

I think if you look at
other use cases of AI,

what we found is besides
those two standouts,

which is vehicle
diagnostics and AI systems,

there's a wide range of things
that were almost tied pretty

much, which is feature tuning
and continuous improvements,

cybersecurity, virtual
sensors, route planning,

driver safety
monitoring and scoring,

all of which were almost
tied for use cases.

So what we're seeing is a
real diversity of uses for AI.

And collectively,

I think it provides
a huge opportunity going forward.

And only features that
your phone cannot deliver.

It's true. Right?

Because I think a lot of some
people feel and some automakers

are saying, well,

I'll just use my phone as
the way to interact with

vehicles—with drivers.

But the reality is a lot of
these capabilities require

knowledge of the
vehicle systems,

not necessarily safety critical
because that's another thing.

But bringing AI into
understanding and improving the

vehicles even outside of the
safety loop is still hugely

value creating if
you're tightly coupled.

Yes.

And I for me, this is a
direct path to monetization.

Right. Yeah.

Fantastic.

And then speaking of
predictive maintenance,

one of the things that we found
is it was a really consistent

priority across respondents.

What were some of the people
we saw respond to this?

So it was it was really
interesting because sometimes

when you look at your regions,
you see a lot of variation,

right, or different companies.

But, you know, it is
top among all of them,

and it's very strong in China.

So it's really interesting
seeing China that it's really

investing in AI and putting an
emphasis on using AI for the

predictive maintenance
of the vehicle.

So they're really understanding
that the vehicle quality is

what they should be
looking at at the moment.

Right. China was
very strong on that.

North America as well, 43% of respondents
thought AI was a top priority.

Tier two suppliers as well,

which is really interesting
because you generally think

that if we're going to be
doing predictive maintenance,

the tier two suppliers who are
making subcomponents, really,

they're going to have to be engaged
in this if we expect it to succeed.

They see value in this,

and it means they're
probably investing in it.

There was one standout, though.

There was an outlier,
which is Germany.

It's quite interesting that
they put very little emphasis

in in predictive maintenance
for their AI investment,

which could indicate that they
think they can do it without

AI. True. It's true. They
have different priorities.

Yeah.

I mean, we we were joking that,
you know, the German quality,

attention to quality
is famous and historic.

And so it may be that
they're they want to build in quality

in such a way that they
don't need to build in AI,

or they want to use more
conventional approaches to to

solving their problem.

So but it it is
interesting trend.

And more generally, we looked
at monetization and we we said

what kinds of features and
services would create the most

customer loyalty or
after-sale revenue.

So it could be
loyalty or revenue.

What are some of the
things you saw there?

Well, what is on top again?
Predictive maintenance.

Right.

Which is really, again,

looking at the quality of the
vehicle and how the vehicle is

delivering value to how the drivability
actually is is taking place.

Right?

But, we also see automated
driving, ranking really high,

and enhanced personalization.

Although it's interesting that
there is a lot of regional

differences in here.

Right?

But I'm gonna talk a
little bit about that,

but what I think is important
as a takeaway is that the OEMs

can't really take one strategy.

Right? One strategy fits all.

You need to have a software
defined platform that is

flexible enough for you to
adapt to different regions.

Right?

Because most OEMs in
in most cases, not all,

but in most cases, companies
are selling worldwide.

And thinking and we've been
talking about this for years.

Thinking that you can have
a one size fits all strategy

across regions is
not a good approach.

There's regional regulatory
differences in some cases.

There's privacy differences.

There's regional
preferences differences.

And so one of the key values
SDVs may bring is the ability

to tap into diverse markets
around the world with a common

hardware platform
in a reusable way.

Precisely. And this leading to
a sustainable business model.

So then you can deliver high
in-vehicle entertainment in

North America, which is
something they value.

You can

get a give an emphasis in
automated driving in Japan or

in China where they value
it a lot and predictive maintenance

in North America or
vehicle ride customization,

which is highly valued in Japan.

It's the second year in a row
that I have to point out that

that automated driving in
Europe is was low last year and

continues to be low
and has dropped.

I think the Europeans just
like to drive their cars,

and they want to be in
control of what to do.

But in China, it's it's strong
and growing, and in Japan,

strong and growing.

Very interesting.

Yeah. But who knows?

With the new new systems,
the end to end systems,

I think maybe we will see some opinions
changing in Europe at some point.

The last one which
is looking at this,

we talked about
predictive maintenance.

It's something that Sonatus
has been focusing a lot on.

We see a lot of interest from
our customers in this area,

and we've been working with our AI
Technician product very extensively.

It was so interesting to see
this data because what what it

showed was predictive
maintenance came out on top in

multiple different directions.

It came out as the top AI use case
outside of autonomous driving.

34% said it was
the top use of AI.

It's also the top revenue
and loyalty driver.

44% of respondents said
it was the top driver.

And it was also consistently
seen as valuable from tier

ones, tier twos, and
automakers, suppliers.

All of three all three of
them said that was a key key

provider, a key
value creating thing.

Very interesting.

Very interesting.

I think this is definitely
going to be the focus for the

industry moving forwards.

And, again, I'm happy with this.

This is what the industry
should be focusing again in

making vehicles better.

Making them better, improving
the loyalty experience.

We were chatting earlier about
how customer loyalty is incredibly

valuable for for OEMs.

Customer acquisition is hard.

So if you can retain a
customer, it's much better.

So if we can improve
the service experience,

if we can improve the quality,

you're going to get the driver
to come back and buy another

vehicle from you in the future.

And you touched a
really important point,

which is differentiation.

Because moving to
software-defined vehicles,

many OEMs get worried about how
they're going to differentiate

and and keep their
unique selling point.

Now if you focus in the quality
and keeping your vehicle

for a long time in the roads
and vehicles that can receive

new functionalities
for a long time,

I think you you were
definitely going there.

Fantastic. Well, Maitê,
this is a huge survey.

Let's talk to people about
how they can get access to it.

On Sonatus' website, very
soon you'll see a link,

we'll provide this
in the show notes.

We'll have the survey,

some key conclusions of the
survey you can get from us.

And if people want to dig
in even deeper than that,

they can reach out to you,

and we'll have your contact info
on the the show notes as well.

They can reach out to you at Omdia
to learn more about this survey.

Yes. And learn more about
the other questions.

It's a very large survey, and
I'm very excited about that.

Thank you as always for joining
us every year. It's a pleasure.

We did this at COVESA last year
and exciting to see it again.

And thanks for for joining us
on this important initiative to

understand the industry.

Thank you.

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