This is the Auto Supply Chain Prophets Podcast.

We're on a mission to bring you the latest insights and thought leaders leading the

charge on supply chain transformation in our beloved automotive industry.

This podcast is powered by QAD RedZone.

I'm Jan Griffiths, your host and producer.

Let's dive in.

Hello and welcome to the 100th episode of the Auto Supply Chain Prophets Podcast,

and we are marking this episode — this milestone episode with a conversation

that matters right here, right now.

We are gonna be talking about why AI Is not just the

future — we know it's the future.

It is the difference between the winners and the laggards.

The people that understand agentic AI, that understand how to implement AI

and bring it into their lives, into their institutions will be the winners.

The big question is how?

How do we do this?

Well, joining us for the conversation today, I couldn't think of two

better guests to have on the show.

First and foremost, we have Sanjay Brahmawar, CEO of QAD.

He is the foremost thinker of technology in the manufacturing space today.

Sanjay, welcome to the show.

Thank you, Jan. Thank you so much for having me.

I appreciate it.

And also would say congratulations on the hundredth episode of

Auto Supply Chain Prophets.

I guess this is a major milestone and you know, also a good sign

that these kind of conversations really matter in the real economy.

They're not just theory.

So look, I'm excited about being here.

Excited about talking to Bryan.

And, particularly Bryan, especially your work through AVT has

consistently shown the progress.

And I completely agree with this doesn't come from perfect

technology and perfect conditions.

It comes from systems that operate in kind of like a messy and real world

environments and with humans in the loop.

So, very excited about this conversation, Jan.

Yes.

And I know Sanjay, you're a big data person.

Do you know a piece of data that only 7% of podcasts

actually meet the 100th episode?

Actually reach that?

Awesome.

Congratulations again.

Yeah.

And joining us on the mic is Dr. Bryan Reimer, research scientist at

MIT and author of the newly released book, How to Make AI Useful: Moving

Beyond the Hype to Real Progress in Business, Society, and Life.

And that it is.

Bryan, welcome to the show.

Jan, Thanks for having me.

Hundredth episode is a heck of an accomplishment.

And Sanjay really excited to be here with you and learn a little bit about

your rich history in manufacturing.

As you mentioned, I come from a manufacturing engineering background.

So I spent a lot of time studying that and really do see, as you mentioned,

this messy space of where man meets machine being the future to optimize

AI and really technology's evolution in supporting manufacturing automation

in lots of different aspects of life.

Let's dive right into it.

We've said that manufacturers don't lack data, they lack action.

This ability to take action.

Why is that?

Sanjay, you first, tell us.

Great question, Jan. Look, action, I think breaks

down the moment insight requires a certain amount of ownership.

When I talk to many manufacturers and C-level execs, I think manufacturers

are very good at dashboards.

But dashboards they explain yesterday.

They don't decide what happens next.

And when no one owns the next move, any kind of insight just sits

there and it will just — just wait.

So that is actually, I think the core difference between a system of record,

where you store and you record and you have data to a system of action.

While the system of record observes; a system of action actually

decides, executes and learns.

So, I think if you bring it down to the industry in automotive.

And I've seen that, your research shows this clearly.

Knowing something is wrong and not acting on it is not neutral, but at

some point, insight without action is kind of worse than ignorance.

So that's where I think AI gets frustrating.

And I would say frankly, irresponsible if it isn't empowered to move the system in.

Sanjay, that's some great words.

I think that a lot of the processes in which we want to automate, which

we wanna leverage are just too unknown to make good, strong decisions.

So I think that we are looking right now to the lure of AI to conduct that data

action step, that is still a step that human expertise needs to sign off on.

So if we are not willing to make that step and sign off on the data alone that's

largely distilled by teams and engineers at this point, and we believe that just

because an AI algorithm programmed by a different team and a different set

of engineers is gonna produce an action or suggested action, you know, follow

through is not necessarily gonna be there.

I think the big piece really comes down to trust.

We don't have trust in others enough.

We often don't trust our teams as well as we should.

Way too much micromanagement out there and poor leadership.

We don't go down to our teams and say, they're recommending

something, with a high success rate.

If they're recommending it to me, it's probably something I

should be taking under advisement.

Okay, I'm gonna automate a bunch of the actions of my team with

AI and lots of other agents.

And all of a sudden I'm gonna believe it more.

And I think the decision logic will collapse because the trust isn't gonna

be there in the AI to be any better.

So our belief that this investment will work, probably erode and

manufacturing is so special because the margins are so small.

Automotive manufacturing in particular is such an interesting case study because

we often look at giants like Ford, GM for the vehicles they produce and the

beautiful designs we see on the road.

But the art of these manufacturers is less than the design.

It's in the ability to mass produce these complex systems at low margin.

And that's truly where companies like GM, Toyota, Honda, and

the likes really do excel.

Is it Sanjay?

Is it in the leadership?

Is it trust?

Is that a huge part of it?

I think Bryan's got a really good point here.

I think leaders in manufacturing are still asking AI to prove

itself before they trust.

And they continue to just, trust human only decisions that are, I guess, in some

ways demonstrably flawed, under pressured.

So they weren't certainly upfront in the environments where there is

some sort of inherent uncertainty.

I don't think the real bottleneck is algorithms.

It's basically the institutional mindset.

It's that fear of being wrong, the approval layers somewhat

of belief that learning must be complete before action starts.

So you know, in manufacturing that will show up as delays, manual

overrides, missed learning cycles.

And I think, leaders who win to accept a hard truth: confidence

doesn't come before action.

It comes from action.

I think that's really important.

So I think that's what it is.

And, yeah, absolutely, leadership is so crucial in this transition to be

able to leverage this technology in it.

Sanjay, that's a really great point because I think that as we

look at the transformative change in new technology, AI, in the ability for new

data systems to support manufacturing.

We really look to an environment where we need to unlearn

as much as we really learn.

We gotta get rid of some of the handcuffs of history to move forward,

stay flexible, use the data in new and innovative ways, and really

cultivate that man machine interaction.

Look, the machine can interpret and manage data in ways that

man could never have dreamed of.

But at the end of the day, the foundation of garbage in

to garbage out still applies.

And human-based decision making is really critical to building trust

in what comes out of the machine.

Well that's simple statistics and regression, or that's complex neural

networks, the same really applies.

So, I think we need to really begin to reward those who begin to

actionize decisions in many senses, starting small, piloting narrow

and then accelerating from there.

Does this seem to be working?

Great.

Let's dive in with two feet

now.

We got to trust a little bit.

We gotta try a little bit before we're actually gonna succeed

in actually finding the system optimizations that we're looking for.

It reminds me a lot of the days of business process outsourcing.

Remember that?

Back in the nineties where it was a race to get your business process,

define it, and then throw it over the wall to a low cost country?

And what happened?

Many companies failed because they failed to do the work.

The basic foundational work of mapping the process and understanding

the decision making loops and how all that needed to work, and then

streamlining it and then putting it out to another low-cost country.

I think the same is true for AI.

You better understand your processes and how decisions

are made before you apply AI.

Is that a good analogy?

It is really a good analogy, Jan. And I think that's why

in many senses a lot of the Chinese manufacturers are succeeding is because

they are making authoritarian decisions based upon good data, not perfect data.

And they're actualizing them, and they're moving forward here in the

United States, and to some degree in Europe, maybe not quite as bad as

Europe as it is here in the States.

Decision paralysis is the name of the game.

We can't make a decision, and that is costing us critical in

markets of accelerated change.

I fully agree.

I think, you have to change the ways of working.

You're right, Jan. It's not about just throwing the process across.

It's actually, fundamentally the way you do things becomes different.

And that requires a different mindset.

It requires an ability to think.

How things will be different, and then go and act upon that.

Manufacturing needs to think a little bit around what we

call in software industry, MVP.

It's like a Minimum Viable Product.

Now, you would never manufacture a product and like send it

out 90% working or something.

But, as you're starting to use new things and new ways of

working, one has to iterate.

You cannot wait for perfection and it'll be too late.

People would've gone, you would've lost the competitive

edge and the moment is gone.

So it's important to start.

Start small, start experimenting, allow the teams build that different

mindset and then, go and act up on it.

When you talk about mindset, it's something

that struck me about you, Sanjay.

Many months ago we did an interview at an event, at a user group event.

And you had, I think, just come out with launching an ERP in 90 days.

Is that correct?

90 days.

Yes, indeed.

And at the time, you know, I've been in this a long time.

I've been in manufacturing automotive a long time.

And I looked at you and I'm thinking, "Is this guy nuts?" But that's exactly

the kind of thinking that we need.

We need people — leaders to challenge the norm to say, you know what?

This is happening.

We're doing it.

We're doing it.

And then you rally a whole team around a mission like that, and guess what?

It happens.

And I believe it's happening.

Is that right?

Yes.

You know, Jan, that's the whole point.

We said, look, we can either bury our heads in the sand and just say, "Oh,

you know, nothing's gonna happen." Or we can leverage AI and actually

take it to the advantage of our mid manufacturers and our clients.

And so what we've done is we've build Champion Pace, which is effectively

working backwards and saying, "Hey, gone are the days where it used to

take two years or 18 months to deploy an ERP. No, mid-market manufacturers

got 18 months to 24 months to wait." So what if we change that to 90 days?

90 days is the benchmark.

And so let's work backwards and say, "How can AI help us to do things

like data migration, configuration, custom extensions, all of that much

faster?" And the reality is that the technology exists to be able to do that.

And I can tell you we've done exactly that and you can see that we just announced

together with our client tentacle.

They went live last week.

And, we can show that these things can be done in 90 days soon.

Sanjay, what I love about that is you're starting with the problem

and saying, "Here is the problem we need to solve." Okay, what tools are out there?

And AI is a tool that can help with lots of problems.

But too often we are looking with this enormous technology that we dream

of in Jetsons, like science fiction, is a solution looking for a problem.

And leadership today, needs to make some strategic decisions on where do we invest,

which means that we have to downplay some priorities to make those investments work.

And, things that draw out and take 18 months or two years to occur.

Create more complexities along the way than saying, "Okay, we are going

to figure out how to get back to Mars or the moon in the next two years.

We don't have three, we don't have four. We are doing it in two years."

And look, that's how the modern space race started.

But too often in manufacturing and in many other areas fell to have

that leadership and that vision.

Today leadership really means observing, seeing the reality clearly,

and then making a decision to act.

And we just see that too infrequent in today's, day and age.

What is the biggest blockage in manufacturing leadership

thinking in automotive, right now, to prevent them from thinking

like a tech CEO, like Sanjay.

What's blocking the thinking?

Is this a myth?

Is there a lie they're telling themselves?

What is it from your experience?

In my experience, we're looking for solutions without action.

We hate to be held accountable for bad decisions, so we end up

delaying, delaying and delaying.

Usually we know what we should do.

And we think about it for too long, and that's not days and hours.

That's Months and years.

So if we wanna make change, we need to really think about our microcosms and

dividing that up and enabling teams to prove, "Okay, we can walk in a new

way." And then worry about running.

But too often we are so stuck on, we have to solve everything

and find a new way to run.

No, build a sandbox.

Enable a subset of your team to figure out how to optimize that sandbox and

when you feel like you can begin to move your progress from that sandbox

outside, make the decisions to do it.

But it doesn't mean recharting and rebuilding the entire

assembly line overnight.

Yeah, there's going to be lots of hurdles.

Supply chain challenges over the last few years, who would've predicted?

Those are gonna continue to come.

And flexible, resilient organizations, strong leadership is gonna overcome that.

Great, I can't get the supply or the microchips I need today, we're

gonna figure out how we have to overcome that, and we are gonna

solve this problem tomorrow.

And that's not right now.

Sanjay, what do you think blocks the thinking?

I think the biggest, I would say, and As you were saying, what

is true and what's not true, I think what's not true is that the frontline is

resisting AI because they're afraid of it.

I don't think so.

You know, I'm going to meet my clients almost every week,

and I go onto the shop floor.

I really wanna walk the shop floor.

I want to talk to the people on the front line.

I want to discuss with them, how they use capabilities.

And the reality is they don't resist AI.

What they do resist, Jan, is actually, they resist tools that slow them down,

second guess them or create more work.

So I think the whole point is, when AI removes friction, you get rid of

manual data entry, firefighting, rework.

Then, adoption is not a change management problem.

It's actually quite automatic.

And so this is why, when in QAD RedZone when we start,

we start from the front line.

Champion AI doesn't supervise the operators, amplifies them.

Gives them early signals, better context.

Allows them to execute faster.

So you are kind of almost, people trust automation when

it respects their expertise.

And I think in plants and in vehicles, I think adoption always

follows usefulness, not mandates.

You tell somebody you have to use AI, that's not the way it's gonna work.

You've gotta create and show them the usefulness.

And I think then it's not a change management problem.

Yeah.

And what I love about the way that you've approached that, Sanjay, with connected

workforce is, you're all about putting the data in the hands of the individual,

but you used essentially like an iPhone, iPad technology that's user friendly.

And I can't tell you how many years I've spent on shop floors where, people just,

either the data is on some manual board, and then somebody wipes up against the

whiteboard and you've lost the data and then, "Oh yeah, but that was yesterday's.

We just haven't updated the board yet."

And it's so on and so on and so on.

But when you actually put technology in the hands of the people and

you use a system that they're already familiar with, genius.

Adoption will follow.

Absolutely.

I mean, I think you can see them feeling proud about, not

having to use paper again.

They just talk about they can use their iPad in front of them and show you,

"Hey, look, I'm working on this line.

I can show you what are the things that I have to do before the

changeover, after the changeover.

What are the issues that I'm gonna have during the production run?

How do I prevent those issues?"

And there's a certain sense of pride of being empowered that the frontline

worker says they don't need the supervisor to tell them what to do.

I think this is where, in our words, the magic happens, that's when you

get 26% more productivity is when the individual feels that they kind of almost

are so enabled to do their job better.

And I think that is where AI plays such an important role.

I personally believe this whole thing is around making the frontline

three to four x more powerful.

To me, Champion AI has gotta be their, in some ways, their Iron Man suit.

It should allow them to be really able to perform at three to four x.

You know, Jan, just listening to this, it is so synergistic

with the framework around "How to Make AI Useful," in my new book and

really looking at AI as the amplifier.

Building trust that you are upskilling your workforce, empowering them with

data to make decisions, to carry out their roles more efficiently.

People like being part of the process and, the hard part of AI is so much of

the show and below trying to oversell its capabilities, the fear of job loss,

the feel of an organization trying to automate away my employment opportunities.

That is not the path that I see is one of value to AI on automation today.

I think what Sanjay's really talking about is the real true value point, the

utility of AI in making us as humans.

Better employees, socially, better personal lives, all the

things that we want to improve.

Using a little automation to move away the boring activities just a little

bit and using a lot of amplification to make us better structured and better

positions to make stronger decisions.

And that's, to me, the true value of AI.

And it's really fun to see it being implied that way,

in the manufacturing world.

And we will see if, which way it seems to go in the driving world

where we seem to be back to trying to prioritize AI as being able to drive a

little bit too much for us right now.

So, it just see different sectors balancing this so differently right

now, but I think the true value proposition is around making us, as

humans, better parts of the system.

I have a question for both of you.

I gotta ask you this question 'cause there's burning a hole in me.

I'm gonna go to Sanjay, first.

Why are you so passionate about manufacturing?

You are a tech guy.

Why are you so passionate about manufacturing?

There's far more sexier areas in the business to be in, and it doesn't

get a lot of love or attention.

So why Sanjay?

Why are you so focused on manufacturing?

Oh, great question, Jan. I mean, look, I started my career on the

shop floor, in Honda assembling engine.

So, I just had that love for manufacturing where people are

actually building, engineers come together to really design and build

real products and real technology.

So, that love has been there for some time.

But most importantly, I like the interface of technology and manufacturing.

And I like to see how technology can play a role in helping and supporting

manufacturing with delivering these amazing products to the end users.

Now, one thing I would say, which connects with Bryan's work is

actually I think we, and when I say 'we', I say, I think you know.

All parts — the academia, the manufacturing sector and all.

And us as software, we need to do a better job at actually helping manufacturers

and the people who work in manufacturing, understand the impact of AI.

I think today, this whole fear mongering and, this constant conversation

around job losses, and all, this is the only news that is gets covered.

This is the only conversation that happens.

Where the truth is what we have just been discussing.

The power to create a three x and a four x. The power to be able to enable people

and make them a lot more empowered.

And I think the most importantly, as I said today in manufacturing,

US manufacturing, there's a deficit of half a million jobs.

By 2033, this deficit's gonna be 2 million.

Where are we going to get these amazing people to work in manufacturing today?

Well, we've gotta excite young generation to come into manufacturing, and I

think AI plays an amazing role there.

When you create, systems like for example, RedZone that we have

created, which are based on iPads and the new ways of working.

That's how you excite the young generation to come in and work in manufacturing.

You can't offer them blue screens, green screens, and the old ERPs and excite

them to come and do those mundane tasks.

So I think that's also a very important part and I guess that's where my

passion comes out also because I feel that we can do a great job here.

Tech sector and software sector can do a great job.

Yeah.

Bryan, same to you.

You are very passionate about manufacturing.

Why?

It's process improvement.

It's just taking a process that could be optimized and using the

tools that we have to implement that.

It's learning about the experience, learning from the teams that are actually

doing the work and saying, "Okay, here's a better approach that we can try to

adopt. Now, we need to listen to the boots in the ground, especially with AI."

Often I think the best strategy is much like Sanjay's described, is packaging

it into existing workflows, not trying to reinvent workflows at the same

time, introducing it as small add-ons.

That can, you know, this is gonna get me a one x know, 1.1

%. Okay.

Then we'll add to that.

Rewarding accuracy in performance of these systems, and ensuring that our workforces,

are listening to the positive aspects of what we can accomplish as opposed to being

shaded by the fear that unfortunately the media seems to drive it in the market.

Again, negative news sells.

I think that is a problem that we need to move beyond.

I think we need to be talking about the positive attributes

of what AI is to bring.

We need to ensure that our workforces understand what we're investing in

those, so that they are along and bought into the process improvements

and contributing as part of that engine.

I think one of my fears moving forward is we look at economic

slowing in certain sectors.

We are gonna blame a lot of layoffs on AI and that is gonna

drive more fear into the market.

And I think that's something that we need to move away from.

We need to look at the power of AI to amplify and we need to be

honest with ourselves when we need to do workforce reductions.

It's not because of AI most of the time.

It's really because of other processes or other business outcomes that we

need to be more transparent with.

Yeah, as our audience is listening to this, they're probably

thinking, "Well, this is great", and I'm sure it's resonating, and they're

saying, "Yep, agree, agree, agree." But in the back of their mind, they're

going, "Oh, but where do I start?

What do I do first?"

So if you're leader.

In the automotive supply chain, and we use supply chain in the broadest

sense, encompassing operations, purchasing, logistics, all of it.

But if you are a leader, listen to this right now and you wanna get,

I dunno, a 60-day, 90-day win.

Where do you start?

A few little stepping stones just to start.

'Cause we all know, that's the tough part is just starting, right?

Is whether it's going to the gym, whatever you're doing, it's that first step.

What's that first step look like?

Sanjay, I go to you first.

I think that's the right mindset, Jan. First,

thinking these 90-day sort of sprints or 90-day spans is really good.

Now, this is the way we've designed, example, Champion AI.

We've thought about offering agentic capabilities in three buckets.

Bucket number one is what we call role-based personas.

And in fact, what we've done is we've actually mapped out all the personas

in manufacturing, whether you are a logistics scheduler, a planner, shift

supervisor, or a plant operator.

Whatever your role is.

Each of these person, they have a certain set of tasks, or let's say

a certain set of, I would call them mundane activities that they do

in the ERP or any kind of system.

And what we've done is we've created agents that help these particular

personas do the job much more efficiently or much more effectively.

And so, they can, for example, tell the agent to overnight create five views that

they need to start the job in the morning.

Normally they would come in the morning, they would spend a lot of time

in the system creating these views.

Well, overnight the agents done the job, it's ready.

They can come in, they can start working and making decisions.

Working on things that have real value and impact.

And I think that's a very good place to start, because it's also really

helps the employee, the shop floor worker, truly get the value of AI or

start seeing what's the value of AI.

So that's number one.

Number two, bucket is where we attack some complex problems.

For example, you've gotta think about what has true impact to

the working of a mid-market manufacturer, or let's say their P&L.

And so we chose inventory carrying costs.

It's sort of the top three cost categories in in for a mid-market manufacturer.

And as Bryan said, these manufacturers are working on very low margins.

Automotive tier one, tier two are working on literally, 5 to 6% margins.

So if you can cut down inventory carrying costs by like, say, 15 to 20%, that's

a big impact to their profitability.

And what the agent does, it looks across the entire supply chain.

It thinks about your inbounds, your inventory at hand, your different

schedules, production schedules.

And it starts recommending adjustments to the replenishment levels.

Things where a human being would be more cautious because they're

so concerned about stockouts and so they overcompensate.

And the system measures all of that and comes up with the recommendations.

And we've run these with our clients for over three, four months now to see,

"hey, on an average the agent could save 30% inventory carrying costs." Major

impact to the P&L of the manufacturer.

So that's bucket two.

And then the last one I would say is the implementation agents, where you

can implement things much faster.

Data migration, this is the 90-day champion pace, where you can do things.

So I think, organizations start with the persona based agents.

Use one or two very important agents that can actually help you with specific

problems that you wanna solve, and then, you go into the implementation sphere.

I think that's a good way to start.

I love that.

It's a very comprehensive answer.

Thank you.

And when you talk about persona based agents, if I can just bring

it down a level, simply think about what your people are doing.

What processes are they following and what decisions are they making?

And when you actually map that out as a very, very basic starting point,

the results might surprise you.

Right?

Absolutely.

Bryan, where would you recommend people start?

I think it's about empowering your organization, to

find the points of optimization.

Allowing them to leverage the modern AI tools.

Obviously within corporate governance, but creating the low risk

environments for teams to experiment.

I mean, the best experts, the folks doing the work.

So the more we allow them to experiment in low risk environments with modern

tooling and, Sanjay's talking about some phenomenally interesting tooling,

without fallout or repercussions.

The more we can find where this works for my organization, your

organization, and so forth.

When we find things that do make sense and we need, as we talked about earlier,

make those decisions to spin this off into larger product directions.

Using AI where possible as the equalizer.

Amplifying the team's capabilities, the team is going to be much more invested in

amplifying itself than replacing itself, that's gonna help when the leadership

and frontline workers are fostering trust and transparency within the organization.

They're gonna learn from each other and they're gonna be more

forthcoming to improve the processes.

And, the AI and data telemetry side is only a tool set that is

helpful in improving the processes.

Everybody on a team is going to have to work through their part to improve

processes as efficiently as possible.

Yeah.

You gotta have buy-in from the entire system.

Which means that in many senses, individuals in the organization

can't protect information.

They gotta democratize it, they gotta share it so other

people can iterate from that.

It is all about team based environments, where the team has gotta succeed.

And then leadership, and when you move up in the organization, ensuring that

they're there to support, encourage, and build transparency to make quick,

rapid decisions to improve processes.

I think that we will never make every decision perfect.

But building the environment where we are trying, we are trying to make good data

driven decisions, and when we make a bad decision, we correct paths and we move on.

Blending the strengths of the data and forensics we have

and our expertise as leaders.

And I would close with a question to our audience,

and it's this: Ask yourself this question about your own leadership.

Are you ready to put data and decision making into the hands

of your frontline employees?

If there's some hesitation in that answer, then there's some

work that needs to be done.

But it is also clear to me that we need to bring more conversations like this

across the airwaves where we are talking about real people, real pain points.

Bryan, you said it many times, we're solving problems.

Let's talk about problems that we're actually solving, using

that approach, using AI and let's amplify these conversations

so that people can hear them.

So let's drown out the noise, the bad news cycle about AI and the fear about AI.

And let's really amp up the positive stories about ai.

I say we do that.

Are you with me?

Absolutely.

Sanjay?

You with me?

Absolutely.

Fully aligned and I love this conversation, Jan. Because think

about the work that Bryan's doing and how aligned it is with how

we see everyday in manufacturing.

I firmly believe Agentic AI and AI is not about replacing people.

It's actually about, augmenting, empowering.

It's about elevating the human judgment when it matters the most.

I think there's so much potential here.

And I think, perhaps the best way is to actually share some success stories.

Bringing some good examples to the table, but there's so much

positive to be able to share also.

Jan, I think we got to remember that manufacturing reemerges

as a success story and a competitive advantage across the automotive industry.

It goes up and down over time.

I think the modern revolution in AI has the potential to shape the next

wave and the competitive advantage of the automotive industry using AI as a

part of that manufacturing optimization to build the next wave in success.

Yeah.

And we are actually rebranding the podcast to Auto Supply Chain

Champions podcast, to do exactly that.

To focus on champions, people who are solving problems.

And that's exactly what we're gonna continue to do.

So thank you both for joining me at the mic today.

Bryan, always a pleasure.

Thanks for having me.

Sanjay, great to have you on the show.

Thank you so much, Jan, and congratulations

once again — a hundred episodes.

Awesome.

Thank you.

We want to hear from you, our listener.

Tell us, what are your challenges right now?

What conversations do you want to hear across the airwaves on this podcast?

Drop us a comment on our podcast website.

The link is in the show notes.