The Garage by Sonatus

In this podcast episode, filmed at CES2026, Ajit Chander, Associate Partner and Mobility Practice Leader at Frost and Sullivan, discusses a comprehensive research report analyzing the impact of cloud and edge AI in the automotive industry with host Dr. John Heinlein, Chief Marketing Officer, Sonatus. The conversation covers key findings from their analysis, including market projections showing AI automotive spending growing from $48 billion in 2025 to $238 billion by 2030, representing a portion of the overall $500 billion software-defined vehicle market. Chander explains emerging AI use cases in vehicles including prognostics, usage-based insurance, sensor virtualization, and edge cybersecurity. The discussion highlights how AI can be deployed on existing vehicle hardware without requiring advanced processors, providing immediate cost savings and new revenue opportunities for OEMs. Specific examples include virtual sensors that can replace physical components like IMUs, saving $20 per vehicle in bill of materials costs. The conversation emphasizes the importance of end-to-end AI lifecycle management and positions Sonatus as a key enabler for achieving AI and software-defined vehicle objectives in the automotive industry.
Frost and Sullivan report "In-Vehicle Edge and Cloud AI at Scale": https://www.sonatus.com/resources/in-vehicle-edge-and-cloud-ai-at-scale/

Chapters:
0:00 Introduction to the Guest
0:50 Ajit's Background and Experience
3:36 Overview of Frost and Sullivan
4:36 Research report on In-vehicle and Cloud AI
7:17 Market Outlook for AI in Automotive
8:05 OEM Strategies and Investments in AI
10:00 Emerging Use Cases in Automotive AI
11:59 AI Not Always Driver-Visible
16:13 Future of AI in Automotive and Continuous Improvement
18:13 Sonatus Vehicle AI Products
20:43 Conclusion and Call to Action

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, our
guest is a Ajit Chander,

who's associate partner and
mobility practice leader for

Frost and Sullivan.

Today's conversation,

we talk about a recent analysis
report from Frost and Sullivan

that was partially
sponsored by Sonatus,

where we looked at cloud and
edge AI and the impact and

benefits of those incredible
innovations in the mobility

space in automotive.

It's a very detailed report where
we looked at a number of different,

application and use cases that
provide tangible benefits.

And in today's podcast,

we talk through that report
and hear from Ajit who has

incredible background in
automotive and mobility.

Let's go.

Today in The Garage, our guest
is Ajit Chander from Frost and

Sullivan. Ajit,
welcome to The Garage.

Thanks for having me.

We're so excited to
have you join us today.

Start by telling us a
little bit about yourself.

All right. Well,
I am an engineer.

I was born and raised in India.

And I actually joined Frost
and Sullivan in India before I

moved to North America.

It was primarily to Toronto.

Been with the company a
little over seventeen years.

Seventeen years,
that's a long time.

That's a longer time
I care to admit, yeah.

And I worked across
multiple industries on the

IT services sector.

I worked in the manufacturing
sector before that.

So I bring that cross
industry experience.

Primarily all these years
with Frost and Sullivan,

I've been in the
automotive and transportation industry working

across the different
players in the

ecosystem from OEMs to
suppliers to the finance

of institutions to

the oil and gas companies
to the energy players.

So a pretty broad ...

Very diverse background.

That's great.

So you got to start with
a fun fact about you.

Well, so my daughter,
so I have two daughters.

My younger daughter, she
picked up playing volleyball,

a sport that I've never played.

I played other sports.

I've been decent at
some of the sports,

but never played volleyball.

But, you know, when
when when your kids,

they get get started and start
involving the games, know,

as a dad, you start
to get involved in it.

So I decided to train her.

So I started learning the sport
and then we hired a personal

gym and started training
her on a weekly basis.

And we started seeing
some results in the games,

so did her friends.

So they wanted to also you know
once they realized that you

know the parent parents started
asking me what's going on,

I mean what's the change?

So we had to tell them what
we are doing so they started

bringing their children then.

In no time, I started
managing a teen girls team,

the snacks, the drama,
and the group chats.

Also survived a
lot of eye rolls,

but still had them
run all the drills.

Well, that's fantastic that you're
getting so involved in your in

your family and your
and your daughter.

My fun fact, I always like to
have a fun fact back to our guest.

My daughter was in some many
years ago when she was in

fourth grade, a
science competition.

I'm on a board of a science
center in in San Jose,

and they joined the science
competition for kids, you know,

fourth through twelfth grade.

And so I was a coach for
a team of fourth grade girls doing the

science competition.

So it was so exciting to teach
them about how to use tools

safely and how to build things because
they didn't know much about that.

And so it was good fun.

So there's my my fun
fact back to you.

Nice. Okay. Okay. Nice.

So tell us about
Frost and Sullivan.

Frost and Sullivan is a is a famous
consultancy and analyst firm.

Tell us about your your role
and and what are the scope of

Frost and Sullivan they do?

So I am a associate partner
and I lead the mobility

practice at Frost and Sullivan.

I've been with the company
seventeen years, like I said.

I decide the business
strategy for mid to long term.

At Frost and Sullivan,
we look at disruptive

forces and how it
reshapes the industry.

We are looking at technology
that impacts our clients.

We are actually in the right in
the middle of the forefront of

technology integration.

We're bringing together OEMs
and suppliers and the tech

integration partners.

And as part of this,

we do publish a lot
of research studies.

We engage on strategic projects.

We also publish a lot of
industry wide papers looking at

emerging opportunities.

That's what we do at
Frost and Sullivan.

Well, and that's quite a good
transition that you were the

lead author on a report we were
involved with you recently on

cloud and edge AI.

Looking at how AI has potential
to really transform vehicles

and add value.

It was so great to work with
you and so thank you for your

partnership on that.

And it was also an
incredible work product.

I'd love it if you start with
an introduction of what were

some of the key conclusions and
takeaways you saw in doing that work?

Sure.

So let me start with what are
the impending challenges in the

industry to achieving
the SDV objective?

So there are the fragmented
ML Ops framework.

The data required to train these

models comes from
different ECUs that run on

different models,

more different models
use different frameworks like

PyTorch, TensorFlow and
the validation, testing,

deployment and management of
these different workflows are

handled differently in terms
of tool chains and teams.

So this causes
significant silos,

and there is the disparate
hardware and software.

That's such an important
conclusion because, you know,

as we're working to
deploy AI into vehicles,

one of the things we want to
empower OEMs is to use the

diverse hardware they have.

We shouldn't be
thinking that, well,

we can't do AI until
there's central compute,

or we can't do AI until there's
a giant optimized AI accelerator.

The reality is, and I think this was
a conclusion borne out in your report,

is there's so many use cases
across the vehicle that can be

much more modest performance
requirements and can get

deployed on today's hardware.

And that's one of the things
that Sonatus is trying to do

and it was exciting to see that I
think your report bore that out.

Yeah. You're right.

Mean, so to continue, the
data is coming from different

ECUs and ECUs run
on their own RTOS

and operating system
and middle west stack.

And

that comes with data
access and lifetime OTA

management challenges.

Right.

Now look at what are the
key enablers of SDV, right?

The decoupling of application
software from hardware with

middleware stack and
operating system,

standardizing interfaces,
creating modular designs,

continuous updatability
and upgradability

with integration.

Right.

And then creating
SDV ready hardware.

Yeah, exactly. Absolutely.

And your report said this
was quite a sizable market.

I mean, what's the outlook on
the potential for AI in vehicles

and AI in automotive?

Alright.

I mean, we estimate
the SDV spend by

2030 to be upwards
of $500 billion.

And then we and we analyze
the different use cases.

There's a current impact in
2025 of about $48 billion

that grows up to $238 billion by

2030.

This includes includes several
interesting emerging use cases

like prognostics,
usage based insurance.

So this is the AI part.

So the AI portion of SDV
spend going from 2025 to

two hundred and what in 2030?

From $48 billion to

$238 billion.

So this is real money.

This is not a niche application
and it's also you were starting

to say...I'd love to understand some
of the use cases you found

were the most maybe compelling
or value generating.

There are there are quite a few.

I mean, I would like to just
cover one more point before that.

I mean, we looked at what
are the challenges in the journey

to SDV objectives.

We looked at what are the,

you know, SDV enablers.

So we also now look at what
are the OEMs doing. Right.

I mean, as you looked at General
Motors announcement of central

compute architecture.

And then that increases their
AI compute capabilities by

35 times and their OTA
capabilities by ten times.

Now OEMs are trying to convert

these assets to
monetizable opportunities.

Frost and Sullivan research
shows that ten other OEMs or

ten plus more OEMs are moving
towards a central compute

architecture with middleware
stacks with, you know,

ethernet backbones and OTA
for their DevOps transition

To secure a five-plus year
window for continuous upgrades.

When look at what Tesla did
in just in 2025, They sent, know,

to eight OTA updates,
made approximately,

I think, about forty two
feature updates just in

2025.

Significant. Yeah.

I think there is an opportunity
to deploy AI models existing

hardware in the current
generation silicon.

So no advanced NPUs or high
performance SoCs are required.

And then this has immediate
impact in terms of the use

cases that we have analyzed
as part of the white paper.

That's great. Your
report was so impressive.

You've done and we'll put a
link to the report in the show

notes so people can can look it up
and find out and find out about it.

You put a lot of numbers
behind the impacts and the

the potential benefit of
some of these use cases.

Now here at the at the
show at CES, just outside,

we can just outside where
we're we're sitting here,

we can see our AI Director,

Sonatus AI Director examples
where we're showing many

different use cases.

I'm wondering which are some of
the use cases that stood out to

you as particularly impactful,
particularly valuable?

I think there are several
emerging use cases that we

analyzed as part of
this white paper.

I mean, a lot of them on the

ADAS and cockpit use
cases are well documented.

They're well known, yeah.

Yeah, so, but we looked at several
other emerging use cases that has an

immediate impact.

We looked at prognostics,
usage-based insurance,

sensor virtualization,
edge cybersecurity,

in-cabin sensing.

And we thought a lot of these
have a significant revenue

opportunity, monetization
opportunity for OEMs.

And things that actually stood out
for us in terms of higher impact

are sensor virtualization.

And I can give you a couple of
use cases that we looked at.

And in fact, you
have some real world

partnerships on those
with COMPREDICT.

We've been working
with COMPREDICT,

it's a fantastic partner that
shows how something that would

have normally be done
with an IMU, know,

a motion sensor that
detects angle of vehicle and

accelerometers and so on.

They can remove the IMU,

save twenty dollars
in vehicle BOM costs.

And twenty dollars may
not seem like a lot,

but it's a colossal important
cost savings as OEMs are trying

to squeeze cost
out of the vehicle.

And that's just one use
case. That's correct.

There's many other use cases for things
virtual sensors just in your example.

Yeah.

And then the EU is mandating
it from 2027 That's right.

For headlight leveling sensors
to go on all the vehicles,

and it's going to add
BOM costs for OEMs.

And of course, there
is the indirect TPMS

that can infer tire
pressure from antilock

braking, inertial measurement
units, speed sensors,

visual input.

And it can

detect leaks,
deflation, and efficient

tire management
solution overall.

It's true.

What struck me, and we've
been visiting with many,

many customers throughout
the show here this week,

and I've been explaining and talking
through these different technologies.

The thing that struck me from
the report and from these many

meetings is it's not only that we're
adding some new feature to the IVI.

It's not only that we're adding
a thing that's driver visible.

Some things, driver visible.

You might provide a usage-based
insurance that lets the driver

see feedback to help them be
a safer driver and then by

extension get lower
insurance rates.

That might be driver visible.

Then you might have something
that's slightly less visible

like tire wear,

where you're monitoring the
tire wear and maybe talking to

ADAS system to say, hey, the
tread's getting a little low,

you better increase
the braking distance,

you better be a little bit
more conservative with the

acceleration you permit.

And then later, sometimes
you warn the driver, hey,

by the way, your
tires are getting low,

you should replace the tires
and here's a great way to get

OEM brand tires there.

We know we're gonna
work for your vehicle.

So that's sort of like
semi driver visible,

sometimes visible, maybe
sometimes invisible.

And then you have something
like the virtual sensors you

mentioned, which might be
completely driver invisible.

Driver never knows,

but that we're able to drive
BOM cost out of the vehicle,

which maybe allows the OEM to
have maybe better margins or

maybe be more cost competitive
with their their vehicle or

perhaps combination.

So I think there's a spectrum
of things that are very visible

to not visible at all.

And I think that's a for
me, it's a key observation,

and I think for the industry,

not many people appreciate that
it can be very diverse like that.

Absolutely.

And in fact, I think when I
when we saw this analysis,

we kind of looked
at three buckets.

One is directly impacting the
bill of materials for OEMs.

And then one increases the
operational efficiency and then

gives them the ability to
continuously monetize it over

the life cycle of the
vehicle and then creates this

personalized experience
for the users, end users.

So that I believe is a
significant opportunity and

that accelerates OEMs
towards the SDV objective.

That's I love the way you've
broken that into a slightly

different taxonomy than I did.

But it's interesting because in some
cases, like, example, the BOM cost.

If you're able to take
costs out of the BOM,

it's immediate revenue
or cost benefit,

immediate top line or
bottom line margin benefits.

And then if there but there's
ongoing benefits, well,

that's also useful and it
may provide some ongoing services

revenue or ongoing
parts sales revenue,

which OEMs are
always looking for.

But in a way that possibly
drivers may find attractive.

Because there's we
always talk about this.

We've talked about this many
times on the podcast is that

people say, "Oh, well,
getting getting money,

getting subscription
from drivers is tough".

But the reality is it's not
tough if you deliver them

something value
creating for them.

If you you deliver them
a feature they want,

they'll be happy
to subscribe to it.

It's when they feel that
they're pushing something on

them that they don't want or
they feel like they should be

getting for free.

You know, we have conducted several
research talking to end users,

understanding what are they
willing to pay in vehicle.

You know, looked at
features on demand,

looked at safety and
security, vehicle performance.

So anything that can enhance
their in car experience

and create a more
personalized experience.

If you look at why
Xiaomi is doing so well.

Now they have this
connected ecosystem, right?

They have appliances in the
house that are connected.

Right.

And then you get in a car and they
see all these as connected entities.

It's a continuum of
your digital life.

Right.

Mean, see the appliances are robots
inside your house and the car

is a robot that's
outside your house.

And then, you know, you add
to this concept, the humanoid,

then that becomes a
connected ecosystem, right?

I mean,

again, the ability
for OEMs to make

the life seamlessly
connected for the end

user is what the end
user is looking for.

That's really exciting
examples you mentioned.

And you also made me
think about something,

your last point that
we as an industry,

we don't know what
algorithms are coming next.

We don't know the kinds of
compute requirements coming next.

And so I think it behooves
us and I think it's what Sonatus

is trying to do,

but I think the industry
collectively is trying to do is

we have to provide the infrastructure
for things that are coming next.

So that OEM doesn't ship a vehicle
and the moment they ship it,

they're they have some sort
of handcuffs that prevent them

from delivering the next
exciting innovation,

but that they come with some
flexibility, some headroom,

but not necessarily
only headroom,

but especially flexibility
to deploy things.

And or even models
that are already there,

realizing the models
are going to improve,

the models can be refined.

How can we make sure
they're upgradable

both through OTA but
even through other means.

Our Sonatus AI Director
product, for example,

AI Director, is designed to
allow deployment of models very

easily in a very lightweight
fashion that's easy to do

without the kind of the
heavyweightness that you might

associate with a full OTA.

Because AI models, we
expect and I believe,

could evolve more rapidly,
could evolve more frequently as

data tuning exists.

You're seeing that with
autonomous driving.

You know, autonomous
driving cars,

imagine you consider Tesla or
Riviera or something like that.

Yes.

They have feature updates.
You talked about that earlier.

But they're also doing
continuous updates to the

driving algorithms.

Continuously.

And not only when you
request some update,

rather they're continuously
tuning the parameters.

I believe that from many of these
AI models that will be like that,

where there's continuous
improvements in AI to make them

smarter and make the
models more accurate.

Instead of only relying on
heavier OTAs that maybe happen

every month or every two
months or every six months.

I agree.

And in fact, I'll bring up two
points to your first point.

The high-power SoCs
on ADAS and cockpit,

the growth of high power
SoCs has been significant.

From Qualcomm to NVIDIA
to Horizon Robotics, AMD,

if you look at ten percent
of the Chinese vehicles that get

manufactured today have high
performing associates from

Qualcomm and NVIDIA.

Right.

The NPU engines

compute-intensive AI
workloads are increasing.

And this is key to seeing
agentic AI models use cases

within vehicle like the
virtual personal assistant.

Now you need a single
orchestration AI model

that manages the end
to end life cycle

of AI in cars and this is key
to acceleration towards the

SDV objective like we discussed
at the start of this conversation.

The solutions from Sonatus is critical
to achieving the SDV objective.

The Collector AI,
the AI Director,

Automator AI and
the AI Technician.

It's end to end
lifecycle AI management

tool chain and runtime
environment that provides

secure containerized
runtime environment

for a lot of these deployments.

Yeah. Thank you for saying that.

And we're we've been showing so
many different ways that that

all those products you
mentioned work together.

You know, in some cases,

OEMs may have the data they
need to manage their models and

then we can use our AI Director
product to deploy them.

In other cases, some of our
model vendors are saying,

you know, actually,

I really would love to be able
to use your data to tune the

model on the vehicle.

In some cases, you
know, in some models,

you actually need to optimize
it for the specific vehicle's

weights and tires
and things like that.

So actually,

some of our partners are
wanting to use Collector AI to

gather in-vehicle data for this
specific vehicle to adjust its

parameters to make
it the most accurate.

So So that kind of continuum is
exciting for us and we've had

great response here at the show.

The interesting part about some
of those when we analyzed a

little bit more about
the different solutions,

it's end to end, right?

I mean manages the entire
lifecycle, which is key,

which gives the
ability for OEMs to

collect, analyze,

deploy, train

and you know, monitor,
optimize, and automate.

That's right.

This idea of a closed loop,
it's not like fire and forget.

It's not like you ship
the vehicle and that's it.

The reality is that
understanding it, tuning it,

continuously optimizing it is
going to be a requirement I

think going forward.

And so we're excited to be
able to participate in that.

And I think it's it seems
like the sky's the limit.

The the number of different

applications people keep
suggesting seem seem significant.

Yeah.

So look, we're so excited to have
worked with you on this report.

We highly recommend
people take a look.

It's it's like, is it
forty, fifty pages?

It's some it's very long report,

but really documents the
value proposition and the

potential revenue impact.

There's lots of
dollars... That's correct.

Lots of numerical breakdown that
we couldn't possibly cover here.

So encourage people
to check that out.

I'm so excited to have
you on the the show.

You have such an incredible
wealth of background over your

seventeen years at
Frost and Sullivan.

So really, you for joining us.

Yeah. Thank you for having me.
It was an absolute pleasure.

We had a a lot of fun
doing this white paper with

your team and you're right.

I mean, there are a lot of revenue
impact that we couldn't cover

in this conversation.

It's a very quantitative report.

I mean, looked at
real world use cases.

I mean, it's time to go
beyond pilot cases to to

actual real time implementation
for OEMs to see this value

at an enterprise level.

And that's where we are getting
at with this white paper,

and we want everyone to read it.

It's impactful. I
strongly recommend it.

Look for the show notes.

Take a look at it, and thank
you for coming on the show.

Oh, thanks for having me.

If you like what you're seeing
in today's episode of The

Garage, please like and subscribe
to see more episodes like it.

And we look forward to seeing
you in another episode of The

Garage very soon.