How AI Changed a 150-Year-Old Industrial Manufacturing Company! | CXOTalk #901 - CXOTalk

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(00:00):
Why spend millions on AI that helps customers buy less of your
product? Sounds like bad math, but Hexion
CEO Michael Leffenfeld says it'sthe way to escape the commodity
trap. I'm Michael Krigsman, welcome to
CXO talk #901. Let's get into it.

(00:22):
Hexion is a company that every person pretty much in the world
interacts with every day, but they just don't know it.
We make products that make products better, as BASF used to
say. We, we make the adhesives that
go into wood panels. So you think OSB plywood, it's a

(00:42):
bunch of pieces of wood glued together.
We make all the adhesives aroundthe world among, you know, among
that space. We make fertilizers for
agriculture, we make oil and gas, you know, purifying
products. We make coatings and paints and
things along those lines that make those products better.

(01:03):
So we touch the surfaces, the materials, everything you
interact with everyday has some Hexion, you know, brand to it.
So you're kind of hidden behind the scenes, as it were.
We are. Hexion is AB to B business.
So Hexion doesn't end up on a product name you know when

(01:23):
you're buying it, but it is enabling the product you're
using. What I found so fascinating is
your emphasis on AI. Give us some insight into what
is that, why are you using AI, how are you using AI and so
forth. Hexion's 150 year old company,

(01:44):
we've got a lot of materials, a lot of processes, a lot of
formulas, a lot of, you know, excellence in what we do and AI
is an enabler to that. AI allows us to take the best
and make it even better. You know, that is really the
critical push for AI, and we useit to look at all people in our

(02:09):
organization. And you know, as you can
imagine, in any role, in any job, there's a bell curve of
quality of people and performance.
There's some lesser performers, some really substantially
productive performers, and then there's an average in the middle
of that bell curve ranging from from good to bad.

(02:33):
And what AI enables is taking everybody on the lower side of
that bell curve and making them the best they can be if they use
it in the right ways. And that's how we enable AI
throughout the entire organization of Hexion and how
we also use it to make our products and the solutions for

(02:53):
our partners and our customers even better.
Now let's take a moment to learnabout Emeritus, which is making
CXO talk possible. If you're a business leader
navigating change or driving growth, explore executive
education programs with Emeritus, a global leader.
They offer programs developed incollaboration with top

(03:16):
universities, designed for decision makers like you.
There's a program tailored to your goals, whether that's AI
and digital transformation or strategy and leadership.
Find your program at www.emeritus dot org.
So it sounds like you are infusing AI through your

(03:41):
operations in a in a both a a broad and a deep way.
Yes, every person at Hexion is empowered to utilize AI.
You know, I used to when I firststarted here.
I used to get emails from my associates in my organization
and say I did this work. I didn't use AI to do this, this

(04:03):
was my work. And I'd write back to them and
say, why do you feel that that'sa exception using AI and not the
rule, right? When we go to the doctor and we
ask the doctor to diagnose us, we want them to use all the
tools at their disposal. You don't want them just to, you
know, just to listen to your heart and listen to your lungs

(04:26):
and then guess what may be wrongwith you, right?
You want them to use every you know, system and and equipment
to make you know your diagnosis better.
And at Hexion, we're the same way.
We want to use all the tools that are enabling for us to do
the best work that we can't. So AI needs to be championed at

(04:46):
every level throughout the organization.
Can you drill down and give us some examples?
Make this concrete. And, and the reason I'm asking
it this way is because everybodytalks about AI.
There's so much hype and everybody says, yeah, you know,
we're investing in AI and the words do not necessarily

(05:08):
indicate the reality underneath.And I know for you this is very
serious. So please dig into this for US.
Innovation at Hexion is a critical growth lever that we
utilize. And so if you think about an
entire research organization, AIis an incredible enabler.
There is a lot of new science published every day.

(05:32):
There's hundreds, thousands of patents and papers and
presentations and journals that that are given, you know, and,
and released on a daily basis. If I had my scientific team
having to read every one of those papers, every one of those
patents, every one of those publications to basically

(05:54):
determine if it could be helpfulthrough our innovation pathway,
they would never have time to bein the lab or in the, you know,
in the research facilities doingthe work that we want them to do
to make products better. And so we utilize AI directly in
that role to scan a wealth of data that would never be able to

(06:18):
be done by single individuals and then summarize it.
And then they can take that summary and say, oh, this really
hits home for what we're trying to do.
And they can then take that paper and read it more in depth,
look at the figures, look at everything that's enabling you
to it, and then allow them to take their strategy forward

(06:39):
using that new learning. AI isn't a learning tool and we
want people to use it to educatethemselves, think bigger, ideate
farther, and really, you know, be capable of expanding their
own boundaries. Do you ever receive resistance
from folks who are you're askingto adopt these tools and these

(07:04):
new ways of working? Of course, you receive
resistance for pretty much everything that you try and put
into an organization. That's change.
You know, Change management is critical to an organization and
making sure you do it properly. And you know, one way we got
around that, you know, sort of reticence to adopt AI.

(07:26):
We did an AI challenge at Hexion.
First prize was $50,000 to a person or to a team that came up
with an AI solution to one of the problems that we have.
And so we look at enabling people, whether they're subject
matter experts, whether they're doers and coders and, you know,

(07:47):
data and analytics experts. We want all of them to
participate in that AI push in the way that their strengths
lend to it, right? In the people that have been in
the job for a long time. You know, they've got a way of
doing it and they have a processthat works for them, but it

(08:07):
doesn't necessarily remain enabled through the best
technology that's out there, automation and manufacturing,
You know, speed of data processing and being able to
tweak the reactors and the and the products in a way that is
using all the information instead of going into a report,

(08:28):
but going into something that's giving you informed information
to help you make decisions. And that's how you get them to
really lean into it because they'll see the enablement that
it causes. Now let's quickly hear from
Emeritus, which is making CXO talk possible.
If you're a business leader navigating change or driving

(08:48):
growth, explore executive education programs with
Emeritus, a global leader. They offer programs developed in
collaboration with top universities, designed for
decision makers like you. There's a program program
tailored to your goals, whether that's AI and digital
transformation or strategy and leadership.

(09:10):
Find your program at www.emeritusorg.
So there's a cultural shift thattakes place.
I'm not trying to put words in your mouth where you are driving
the use of that of the information that comes from the
AI to actually take action stepsand incorporate those learnings

(09:34):
directly into the workflow. That's exactly right.
So AI doesn't have to be the doer of everything.
Everything doesn't have to be made to be autonomous and
automatic. What it can do is also a
recommendation engine. It could say, hey, this data is
looking in a certain way. You may want to tweak a process
condition to make something better.

(09:56):
And this, you know, this is whatalso enables our smart tech
technology that we commercializeout into the wood panel
manufacturing industry. We give users and operators that
use our software recommendationsto make their line work faster,
work smarter, use less material.That's where AI is really an

(10:19):
enabler to complement it. You know, with the know how of
the people that have been doing the job for so long and have so
much know how and information and pair it with the data
processing power that is unmatched by a computer.
So you bring those two together and you have a really perfect
storm. Folks, you can ask questions.

(10:42):
If you're watching on LinkedIn, pop your questions into the
LinkedIn chat. If you're watching on Twitter X,
use the hashtag CXO talk. We're speaking with the CEO of
large chemical manufacturer Hexion.
When else will you have the chance to ask someone like

(11:03):
Michael Leffenfeld pretty much whatever you want.
So I urge you take advantage of it.
And we have a a very interestingquestion from from Twitter, from
Arsalan Khan, who's a regular listener.
Arsalan always asks great questions, and he says what sort
of disincentives and incentives do you have for people to adopt

(11:25):
AI as a partner rather than a job taker?
We don't look at AI as a job taker.
We look at AI as a, you know, isbasically an improvement to the
skill set. But we want every employee at
Hexion and anybody that we hire to have some AI experience.

(11:47):
And I don't mean they have to know how to build an agent.
I don't know, meaning that they have to know how to code a
language model. What I'm saying is, is that they
have to know how to, you know, utilize a proper prompt to get
the information and be able to get their ideas and expense, you
know, out there. So that is a critical
foundational lever for us. Now, if they're truly, you know,

(12:10):
against using AI, you know, we can have and have found roles
for them in subject matter expertise to inform those that
are more capable in the AI to build their systems, knowing
what they do every day and how well they do it and being able
to translate it into that, that that space.
Now, we don't also at Hexion don't look at AI as a job

(12:34):
replacer. We're not looking to cut
headcount. That is not in our strategy at
all. What we're looking if somebody
develops an AI agent or application that replaces their
job, that would be phenomenal because that person will be an
enabler for more parts of the organization to bring an AI

(12:56):
thought process all the way through.
So that person has probably a promotion on their hands, not a
job loss if they're able to do things like that.
So at Hexion, we use all enablement.
There's very little stick. There's only lots of carrots
when it comes to AI at Hexion. Michael, you must acknowledge

(13:17):
that there is real concern out there that as AI drives
efficiency, there will be a temptation to eliminate certain
jobs because you can. I am not a grow by cutting CEOI
was chosen by the board to come into Hexion through a

(13:39):
transformation for growth. My background is
entrepreneurial, my background is growing new products.
I have patents. I've, you know, I've brought new
things to the world throughout my career.
So I am not the one that would be here to ultimately go through
headcount reduction processes and, and look at, you know, now

(14:02):
look, I will tell you, AI has enabled us to save money and do
things better and more efficiently.
But you know, the head counted Hexion is not at risk from the
AI component. If anything, if we get, if we're
freeing up time of individuals to do more, they'll do more.

(14:23):
We have a lot of goals and a lotof plans in how we're looking to
expand Hexion into, you know, doubling our size and, and our
and our revenue and our and our ultimately profit.
But that is the goal for Hexion is just for growth and that is
what we are enabled to do and that's the strategy we have.
And I will mention you have over100 patents and pending to your

(14:48):
name as well. I do, yes, I am I, I am a nerd.
It is the the single descriptor word that works almost perfectly
every time if you want to describe me.
It sounds like you have a different attitude towards AI
adoption than many other CE OS who are looking at it through

(15:08):
that lens of efficiency and reducing headcount as a result
of automation. Through my entrepreneurial early
parts of my career, I've done every job.
I'm not going to say I can do every job, but I had to do every
job. At the time, I wasn't great at
all of them, but in any one day I could be scrubbing toilets and

(15:31):
meeting with a president or ACEOin the same day.
So I spent time in supply chain,I spent time in procurement, I
spent time in operations, I spent time in finance, all the
different roles in the organization.
So, you know, as ACEO of, you know, now Hexion, a global large

(15:52):
billion dollar organization, I'mable to enable a lot of
different functions with some very simple questions because I
have an experience level in every role that I can be a
little more intriguing and lead to a little more creative
thinking that comes from my background and my experience.

(16:14):
And that's really what's been, you know, I think what's taken
Hexion to that next level as quickly as we have in the last
couple of years. You know, wood panel engineering
is our and building and construction products are our,
you know, are our bread and butter.
We are leaning into that, you know, in a in a very big way, in

(16:37):
a new way that hasn't been done before.
And we think our partners and our customers are seeing that
too. We have a question for an
interesting one from Nandeep Nagarkar, who says what
guardrails do you have? So when it comes to your AI,

(16:58):
what are the how do you think about the guardrails?
AI does need some human interaction, at least at this
stage in time. We don't let anything run
without some, you know, some user feedback to it.
But there are ways to build AI, you know, agents and models that

(17:18):
do have its own checks and balances.
But there is, I still think, youknow, at this point in time
there is still a need for the human check.
And so we don't run anything at Hexion singularly with AI, you
know, you know, exclusively we have all human interaction

(17:41):
still, you know, to double checkthe results and make sure that
we're not leading anything in the wrong direction.
Mind you, we, we handle some pretty hazardous chemicals as
our starting materials. And you know, we want to make
sure that we're doing everythingright.
We're working it with temperatures and pressures and
different things to make, you know, to make different

(18:03):
molecules. And because of that, we like to
make sure that we have our, you know, our human interaction in
there as well. So for you, it's not just the
not just issues around AI ethics, but it's actually issues
of relating to scientific accuracy and safety.

(18:25):
It's everything, right? You have to be thoughtful around
any new technology. So I'm a, I'm a very big
believer in utilizing, you know,new technologies quickly and
often. I also believe that a lot of
decisions in, you know, a work environment, a lot of them are
reversible decisions. And my team has heard me say

(18:45):
this a lot if the, if it is a reversible decision.
So usually, if you know the, the, the cost of the
reversibility, it's usually timeor it's money.
If you can afford that cost of reversibility, you should just
walk through the door. And if it doesn't work, you'll

(19:06):
learn something, you'll be able to put it back together and
you'll build Humpty Dumpty back together again with a more
learning and in a better way because of that learning.
And that to me is a, you know, is a really important feature to
being able to grow an organization with new
technologies. That also being said, I will

(19:28):
never be cutting edge when it comes to safety.
Safety is number one at Hexion and safety should be paramount
in every organization. So especially with what we do
and even in AI, you know, capabilities, we want to make
sure we are safe in everything we do everyday.
We have another question from LinkedIn, another interesting

(19:53):
question. And folks, keep your questions
coming. Take advantage of this
opportunity to ask the CEO of a major manufacturing organization
pretty much whatever you want. We're talking with Michael
Leffenfeld, the CEO of Hexion, and this is from Anoop Rama.

(20:14):
And Anoop says, how do you measure your ROI, especially
when you're implementing AI in achemical manufacturing
environment? There's numerous KPIs that come
with that, depending on where you're using a, you know, using
the AI right in, in my team, in my back office team, it's

(20:35):
productivity, right? They're getting so much more
done in less time. They're able to do more, they're
able to think more. You know, there's AI have a
leadership coach that is that often tells me there's the you
have to spend time on the balcony as well as on the dance
floor. And what that means is that you

(20:56):
have to be able to see the forest for the trees at times.
And, you know, in my leadership team, the hire the leader in the
organization, I want them more time spent on the balcony and
less time on the dance floor doing the tactical work.
And then as you move throughout the organization to, you know,
to a, you know, to the roles that are more tactical, they're

(21:19):
spending more time on the dance floor or less time in the
balcony. So AI really enables that
capability to be able to stretchyourselves into a, you know,
into the, the breadth that comeswith an organizational, you
know, work. But if you're talking about AI
and automation, you know, in, inmanufacturing, there's, you

(21:43):
know, you see lower raw materialconsumption, you see faster
batch times, you see better quality and better product at
the end, you see lower energy consumption.
It's truly sustainable, right? AI enables us to run towards
what we call the golden batch, the perfect batch.

(22:05):
And if you can lower the cost ofraw materials, that's great for
the for the customer. But if you can lower the the
consumption of energy or lower emissions or, you know, decrease
scrap or waste, that's better for the world.
And that's really what we care about at Hexia.

(22:25):
Let's drill into this point. How does AI help you lower these
costs and drive towards that perfect batch?
I'll utilize our smart tech technology that goes to a
customer because that I think is, you know, you know, a, a
great example for us. So we have AAI technology that

(22:50):
gets in, that gets an overlayer that gets installed into every
unit operation. So the way that that a wood
panel is made is you take a, youknow, you, you take a tree, you
debark it, you eventually, you know, you treat it, you cut it
into small pieces or strips, then you dry it, you add our

(23:10):
adhesive to it, our resins to it.
And then it goes on a mat and itgets pressed with heat and
pressure into the board that yousee at the end.
And then it gets expected for quality.
So our Smartech systems go into the specific unit operations.
We have parts like right now we have, we have overlayers that go

(23:30):
into the press, which is the heat and pressure that gets
applied. Now that heat is generated by
steam, so you want to make sure you're using the perfect amount
of steam so that you're not generating any CO2 when you're
generating that electricity or that heat that comes along with
it. We have a smart quality product
that basically makes sure that the product is in the, you know,

(23:54):
is the right first time so that you can go back into your line.
If product is not coming out well, you're not generating more
waste. You're able to go back and fix
in the plant itself. And then we have a product
called the smart strander. What a strander is, is basically
the knives that chop up the woodinto the pieces that you need.
So our AI software for that unitoperation allows you to get the

(24:20):
best consumption of that tree you can.
So you're using less trees, you're having less waste and
you're making sure that the product quality is exactly where
it should because you're making sure the knives on the cutter on
the strander is not, they're notdull, they're not vibrating,
they're not causing bad cuts. So you're making sure that every

(24:42):
cut is the right cut. So if you think about first time
right is what AI can enable by taking that data, that
information instead of putting it into a data historian where
it's gone all these years, it now brings analysis,
interpretation and suggestion for it to run properly.
Now you can always run autonomously, but we prefer to

(25:06):
run with a recommendation engine.
We, we do enable the the autonomous, but we prefer the
recommendation engine. So operators and their expertise
of knowing their line and how their line operates make sure it
works. And this does lead to us using
less consumables, which is, you know, which is a savings for

(25:26):
everybody, the world and for thecustomer.
So in this case, the savings andthe efficiencies derive from
real time or near real time dataanalysis and the ability to make
adjustments along the way as opposed to increasing the

(25:50):
knowledge, the general knowledgeof the people working at the at
at Hexion. That's not exactly right.
Yes, it uses data to make sure that you're putting your
parameters in the best place possible, but it's also
providing learning to the operators at the Control Board,

(26:10):
right? So where they're sitting and
adjusting and tuning and doing all the things that need to get
done, the AI tool is telling them, hey, have you thought
about this? And that's a training system.
So you have to think about the AI recommendation tool is also
making your operators learning as they're performing the tasks

(26:31):
because they're getting data analysis now they have a gut
feel, right? Some operators are brilliant
when it comes to that gut feel interpretation as they're seeing
different trends along their production line.
But the AI engine can also now tell them, hey, you didn't
notice this. So let me give an example on a

(26:51):
personal front for me, I'm a type 1 diabetic.
I've been a diabetic for, you know, almost 40 years.
And let's call it about a decadeago, I started using what's
known as a CGMA continuous glucose monitor.
It's it's in my body and it tells me my blood sugar in real
time. I ran my blood sugars like your

(27:13):
body runs its blood sugars, right?
But I learned things that I thought was doing right all
these years when I ate certain things, my blood sugar reacted
differently, right? It's, it's a learning tool that
says, oh, if I eat that, it's going to change my blood sugar
in a different way than I thought.
It takes longer to digest. It takes longer to absorb.

(27:35):
Sometimes exercise raises my blood sugar.
You know, if my parents see thisvideo, I could tell you my
mother used to take me when I before I had soccer tournaments
as a kid, I, they used to take me for ice cream as a surprise
because I was getting all this exercise.
So, but what it turns out is when you start exercising, your
blood sugar actually goes up. So she was giving me ice cream,

(27:56):
which is raising my blood sugar,and exercise, which is raising
my blood sugar wasn't probably the best combination, but I only
learned that with a CGM. And so it's that type of tool
now put into wood panel processing, chemical
manufacturing that gives you that learning ability that's
different than you know. So it's a trainer, not just a

(28:17):
doer, and that's really critical.
So it's the combination of data analysis combined with learning
essentially. Exactly.
We have a question from Drew Jackson on LinkedIn and Drew
says what use cases relating to customer service and client

(28:40):
facing roles do you see AI having the largest positive
impact on? AI in customer facing and
customer service is actually pretty important.
As you can imagine, developing products for the customer comes
with a with a skill of voice a customer, you want to get all

(29:02):
that feedback from them first, but also it helps with strategy,
right? If you, you can use an AI agent
to, or just a simple, you know, AI, you know, system to help
yourself to go through the salesprocess.
What kind of questions could be asked?

(29:22):
You could be, you could sharpen your ability to do the sales
pitch to tune your, your, your, your, your capability to make
sure that you, you know, highlight things that may be
important to them. Because you can use an AI agent
to then say, Hey, what's most important to this customer?
What have they been putting out in their press releases?

(29:42):
Their their their their shareholder documents, what are
their mission critical, you know, goals for the specific
year. And then you can craft your
sales pitch, you know, directly.So you can use it as a, you
know, as a, you know, give and take to build that strategy on
how to deliver, you know, sales excellence along that.

(30:06):
But you can also use it for strategic marketing, you know,
and and data gathering. You use it for, like I said, for
voice of customer, for customer service help.
So you have at your fingertips, you know, you know, you can
gather that information that Hexion has 100 plus years of
information on some of our products that we can pull, that,

(30:28):
you know, that dated informationin a much more, you know,
realizable way to make sure thateverybody can answer questions
in the most informed positions. To what extent have you applied
automation in areas such as yourcontact center?
We have enabled our teams with tools to be able to reach
through to our SDSS, our safety data sheets right into the, you

(30:54):
know, into the, the specific product stewardship sheets that
are needed. We want to make sure that they
have permit access, they're ableto reach much further than they
used to into the trove of information that Hexion has for
every one of its products and every one of its, you know, it's
solution offerings to our customers to make sure that they

(31:15):
have the best service that they can give our customers, you
know, immediately. We have a question from Alfonso
Velasco, who says Ain manufacturing can be applied
both on the front line, the shopfloor and in the back office.
Accounting, sales, customer service, logistics.
What are the most exciting use cases Hexion has deployed in the

(31:39):
latter? In the back office, AI is a
critical tool in our supply chain excellence, in our, you
know, in our procurement excellence to make sure that
we're looking at especially in today's day and age where trade
lanes are changing quickly with all the geopolitical

(31:59):
macroeconomic movers that are happening in today's day and
age. So using AI to enable those
those pathways to make sure thatwe can serve our customers
without incident, without delay,without any challenge is an area
where AI and, you know, that enablement really does, you

(32:20):
know, you know, return on that investment pretty quickly.
And then they're just simple things, right?
Reporting financial information using AI allows us to much, much
more quickly put together reports and analysis that are
needed for a large organization.We're not public, we're a
privately held organization, Butyou know, public organizations

(32:43):
as well are constantly churning that information for the needed
shareholder. And you know, we use, we use
tools like that, you know, everyday.
And this is from Greg Walters, who says, OK, Speaking of
learning instead of the AI teaching the human, isn't the AI
itself learning beyond the human?

(33:05):
And I guess we could extrapolatethat question to the whole issue
of the AI taking in your data. And do you have concerns about
that? The AI systems that we use are
not publicly trainable to, you know, to open AI or, or Gemini

(33:26):
or, you know, the systems we, we, they are kept internally.
They are firewalled and we do make sure that there are data
and our know how and our, you know, and our intellectual
property stay very secure. So I, I, I'm not super worried
about that as an issue, but look, the AI should learn.
You want it to learn, you want it to learn on the data of our

(33:47):
processes and our and our, you know, different actions and
throughout the organization and every function and make sure
that we're utilizing it into so that it's continually learning
new scenarios, new ways we do things, new things we're looking
at because as we're a growing organization, we may try to
enter into different markets anddifferent industries as we go

(34:10):
about. So we want a learned system.
We just don't want a uncontrolled system and we want
a secure system around that. You want a system that learns,
but that doesn't share outside the boundaries of your
organization. Yes.
And then we made sure we we put those firewalls in place as

(34:31):
needed. We have some great questions on
Twitter. Isn't the audience fantastic?
I mean, the questions we get arejust wonderful.
So again, this is from Arsalan Khan.
He says a company is not an island.
How do you encourage your ecosystem of vendors and
partners to become AI ready too?And I'll just add to that one.

(34:55):
And what happens if your ecosystem of partners does not
become AI ready to that extent? As an organization of Hexion, as
we transform into basically a cutting edge organization
delivering new products and new solutions for our, for our
providers, it's sort of, you know, rising tide raises all

(35:20):
ships, right? We make sure that we're, we
communicate what we what we do and how we do it and we share
this information and AI is an area that everybody is
inquisitive about and everybody sees value in.
I I truly believe that is not a question through the through

(35:40):
organizations. Now some move at different
paces, of course, but you know, it probably also, you know, the
AI use ad hexion does allow us to, you know, to move quick.
And I think that those that are providing us, they see the
growth that we're making, they see the differentiation we're

(36:00):
we're leading and they want to make sure that they could stay
along. So you do see them try and adopt
or they ask questions on how they can adopt and we partner
with them so that we can help them transform as well.
Sounds like you are very much a carrot rather than a stick kind
of leader. I would say that I'm not sure

(36:21):
all my employees would say that.But look, there's a different in
this area. It, you know, when you're trying
to get people to grow, you're trying to learn, you're trying
to give them the skills to be successful.
You know, it's, you know, Steve Jobs said, you know, you know,
what happens if you know what happens if you're, you know, if

(36:42):
you train your people and they leave, but what happens if you
don't train your people and theystay, right?
And that's paraphrasing, but that is it's, we want the best
people in our organization. We want to enable them.
We want to pay for their education.
You know, we do that regularly. We want them to be the best they

(37:03):
can be. And hopefully they stay.
But if they go somewhere else, we'll have a pretty good
reputation for for for great people.
And I think that's just as important because the brand of
Hexion is bigger than just the products we make.
It's also the people that we bring forward into the world.
Very interesting how you talk about the brand as being beyond

(37:26):
just the product, but it's really the the broader
reputation of the organization of the company.
Yes, 100%. You know, Hexion's been around
for 150 years. We weren't always called Hexion,
but we've been around for 150 years and the culture and the
institution and the soul is not something that, you know, I can

(37:50):
change. I can only help make it, you
know, bigger and more modern to where it is today.
And my team is amazing. I have one of the best teams in
the world and they, they don't stop.
You know, we have a, we were saying at Hexion, you know, we
strive, you know, we strive for perfection knowing we're never

(38:10):
going to achieve it because we're going to constantly raise
the bar and constantly try and achieve the next, you know, the
next milestone. And my team over delivers every
time. Your respect for the DNA and the
history of the company really does come through.

(38:31):
Thank you, I hope it does. We have another interesting
question again on Twitter, and this is from Russ Bankson, who
says he's interested in acquiring and growing small
manufacturers. How can he use AI to understand
their current processes and suggest innovations to improve

(38:55):
their processes and new products?
AI isn't a tool that should makethat analysis and the diligence
of those organizations in a really efficient way.
You don't need a big team to do that, but you can benchmark
really well using AI. You can see where the markets

(39:15):
are going and where they're growing and where some headwinds
or tailwinds exist. You know, there are big private
equity firms around the world that utilize AI to help see how,
you know, how they may have bought something and made a
mistake and why they made that mistake, right?
Or how they made an offer for a company that they said no to.

(39:38):
How they didn't make an offer toa company and they said no to
that. And then they saw it later
become something that they've never had in their model, right?
So there, there is a constant growth in that space for using
AI and the information it can generate and enable those to
make more informed investment decisions.

(40:00):
But I I I can't really opine more on that.
We have a question again still on Twitter from Elizabeth Shaw,
who says what does being an AI first company mean for Hexion
today and and what does that look like?
We want everybody to have AI as a enablement tool for

(40:21):
themselves, right? So that's what it means.
It means that everybody that comes into Hexion has to get
comfortable with some use of AI.Now, it could be a native
application in a software that, you know, we already have
installed. It could be, you know, using a
just a, a prompt engine to be able to better, you know, better

(40:43):
build their data, you know, acquisition, or it could be
building, you know, actual agents and actual tools in the
organization. But there is no real limit as
long as AI is a willingness in your, you know, toolkit.
We want to give my job as ACEO is to give all of my team the

(41:05):
tools to be successful. That is really the number one
job in my day is to make sure that every person on my team has
the tools to win and AI needs tobe one of those tools that they
can utilize to help them win. When you repackage AI or use AI

(41:29):
for your customers in order to significantly reduce their
materials usage, that cuts into your bottom line, right?
You're selling them less. How do you reconcile your profit
making goal with your Let's sellthe customers less than we did

(41:51):
before? Goal, Michael, you said it
sounds like you sit on my board,you know it.
That's exactly right. We are putting tools in place at
our customers to make sure they're using the right amount
of our products right. But that's going to happen.

(42:12):
Whether it happens by Hexion doing it or some other
organization doing it, it's going to happen one way or
another. At least I have the tools now
that will enable us to get better because the information
that we're gaining by letting our customers use less wood,
less energy, less resins that wesell, less waxes that we sell.

(42:37):
The reason that they can use less is because it's the right
amount in the right conditions to make the best product.
But that data then comes back tous, you know, it's screened, it
doesn't combine with other customers, but that singular
customer then allows me to understand their process better

(42:59):
so I can deliver them a formulation that is exceptional
compared to anybody else out in the world that may try and
supply that same product. Because I know intimately how
their process runs, what the conditions are, what their
substrates look like, what the surfaces may be, what the speeds

(43:21):
they're running at. I can use all that information
to custom design my products so they have something every day
that is unique to their platformand allows them to have the best
product in the world in what they're making.
Nobody can say they can do that,and I'm not saying we can do

(43:42):
that just today, but we're on our way there and we are
starting to show that return already even in the early space
of our AI transformation. It sounds to me that the
calculation you're making, and please correct me if I'm wrong,
it sounds the calculation you'remaking is fundamentally a trade,

(44:07):
which on the one hand, we will use data to let you reduce your
cost, let you reduce, become more efficient, can buy less
from us and do an even better job because it's the right
thing. In exchange, we are gaining data
about you, the customer that let's us serve you better than

(44:33):
we could before. And by knowing your business and
what you need that much more thoroughly, we're assuming that
we'll be able to be a better supplier, a better partner to
you. And in aggregate, over time you
the relationship will deepen between our companies and in

(44:55):
fact you will will sell you more.
Exactly, that's 100% correct. It's not exactly a trade, but we
are trading those things. So hopefully so we can expand
our offerings to each customer so that they can have more from
Hexion more support because of what we're doing with the
systems that we supply with the you know, with the product

(45:18):
solution that we that we that wethat we offer that we can expand
that into their other parts of their business as well.
And that's how we look at. It so from your perspective,
it's a deepening of the relationship based on shared
data essentially that benefits both your customer and Hexion.

(45:43):
Yes, everybody talks about how they have an intimate
relationship with their customers and their partners,
but this is a much deeper intimacy because you're able to
help them to be successful in the best way possible.
And by ultimately giving the most sustainable solution they
can possibly have, because they're using less, they're

(46:07):
wasting less, and they're bringing that to the communities
that they serve and the markets that they sell into all that
much betterment. And when you use the term
sustainable in this case, it sounds like what what it what it
actually means is a combination of cost effective as well as

(46:27):
higher quality or better fit to purpose.
That's exactly right. Michael, you are APE owned,
private equity owned company. Has that relationship changed
the way you think of investing in AI?
In other words, either has it restrained the amount that you

(46:50):
could invest in AI, or has it forced an acceleration of that
investment because the time horizon of private equity is
limited and you need to show results.
American Securities is our PE owner and they are absolutely
fantastic. They have empowered me to think

(47:12):
not just within their hold period, but to think about the
business for 100 years forward. They are not limited to the time
constraint of like you mentionedthe the the hold period where
they look for their investment in the cash flow.
They want us to build a businessthat is going to be long
standing for a future owner to basically be able to grow and

(47:38):
continue to grow forward. So American Securities enables
me to be incredibly rapid in my movements.
So I'll give a little bit of history here for American
Securities. They have an entire operations

(48:00):
group, 100 people that they provide to us, they pay for that
allows us to borrow from those people.
They're experts in legal and they're experts in IT, and
they're experts in procurement and supply chain, you name it.
But what that does is it allows me to experiment and try
different things without having to truly invest in hiring

(48:24):
somebody, taking them away from a job they may be in, and then
find out that idea didn't work. And I would never want to pull
somebody out of an organization and then that that and then tell
them they have to go look for a job.
That's the worst thing ACEO could ever have to say to
somebody. And So what American Securities
enables us through their resources group is to

(48:46):
experiment. And their IT team is phenomenal.
They own AI companies now going and buying AI companies.
The valuations may not necessarily lean themselves to a
perfect, you know, synergy with a, with, with, with PE, but they
own AI, they know AI, they're using AI.
So they're really enabling and really looking forward to the

(49:09):
solutions that we're providing because they then share it with
the rest of their organization as well.
So I think it's been a really, you know, synergistic
relationship between Hexion and American Securities because they
are pushing for growth every waythey can.
So clearly the AI first strategythat you've adopted fits with

(49:31):
their thinking and it sounds like you're very much mutually
aligned around that. Yes, very much so.
And I really encourage people towho are listening to think about
this, which is the notion of using the data to create a win,
win situation with your customers where you are helping

(49:55):
them be more sustainable. In exchange, that data enables
you to develop a deeper understanding of their business,
which creates. It's really a 1 + 1 = 3 because
you're you're increasing the size of the pie based on that

(50:17):
data. That's exactly what it is.
It's by us having that data, it allows us to generate products
that are, you know, that are specific, that are designed for
them. You know, the adhesives in the
wood panel industry, in the woodindustry are made in what's
known as batch chemistry. So big stir tank reactors

(50:41):
opposed to continuous flow wherelike Petro chemicals and oil and
gas are refined, things like that.
So because we do things in batch, we have to do sometimes
to a specific wood and a wood panel plant, we can send six
deliveries a day, have to run 3 batches a day.
So with their constant change in, you know, environmental

(51:05):
conditions in their wood substrate conditions, with
their, you know, with their lineconditions, with their, you
know, their, the heat conductivity and their press may
get worse and worse over time, may need to do some
modifications. We know all that.
So I can give them a resin that's going to perform at the

(51:25):
optimal conditions, and those optimal conditions make sure
that they have the best product every time.
And that's the goal. And that data is symbiotic
between us and them to make surethat we're all getting to the
best place we can, every single time.
We have a question again from Elizabeth Shaw, who is asking

(51:47):
about the business model. How has AI changed your business
model? The business model has not
changed just yet, but we do see there will be ways to expand the
business model between with a service component as not just a
chemical materials sales process.

(52:09):
So we do see enablement in beingable to deliver, you know value
in other ways through service and through other exceptional
models throughout. But I do see Hexion and we
already have started expanding beyond just the, you know, we
sell you a product and you use it to go forward.

(52:30):
We are now getting into a more integrated value proposition
with our customers. If you could go back to the
start of this AI transformation,what would you do differently?
I can't tell you exactly what I've stubbed my toe on, but
we've stubbed our toe lenty of times.
But like I said, they're reversible decisions and you

(52:53):
learned from those reversible decisions.
So I was able to go back, reverse and do it again in a
smarter way. So, you know, I don't think you
know it because AI is, you know,we're sort of building the plane
while flying it, right? Because AI is evolving so
quickly. It is amazing the speed and the
capabilities that are coming along with AI.

(53:16):
So we're just, you know, we're, we're trying as we go and but
we're not afraid to make mistakes.
That's OK. You know, you learn from them
and you try again, as long as you're making sure everybody is
safe along the way. So many companies are stuck in
AI pilot purgatory. What advice do you have for
other business leaders who want to get out of the pilots and

(53:40):
into practical AI benefit? Just try it.
Speed is important. It it's, you really do have to
keep up with what's going on in the world.
It's moving incredibly fast. And if you can't do that, you're
going to be left behind because somebody is going to, you know,
is going to move faster than you.

(54:01):
There's a, there's a famous saying, right?
Every great idea is one in a million, but people don't do the
math past that step. Every great idea is one in a
million, but there's 8 billion people on the planet.
So at any moment in time, 8000 people have that same exact
great idea. So if you're not moving fast, if

(54:23):
you're not executing quickly, ifyou're not bringing the smartest
people to the problem and working as a team because you
get there faster as a group, you're going to get left behind
because that other person, one of those 8000 people, is going
to be faster than you and you'regoing to miss out on your
opportunity. So to me, that is critical.
So in other words, just get it done, get it done.

(54:46):
Just try it and get it done. And with that, we're out of
time. A huge thank you to Hexion CEO
Michael Leffenfeld. Michael, thank you so much for
being with us. I'm very grateful to you.
Michael, thank you so much. This was wonderful.
I really appreciate it. And anybody who asks questions,

(55:06):
if they have more questions, canreach out to me on LinkedIn or
reach out to our Marcom team andwe'll get you answers.
That's an invitation. And folks, if you're listening,
definitely connect with Michael and LinkedIn.
Connect to me on LinkedIn. We have great shows coming up.
Do not forget to subscribe to the CXO Talk newsletter.

(55:27):
Go do that now so we can notify you and you can participate.
Thanks for everybody who watchedand asked such awesome
questions. You guys are fantastic.
See you again next time. Have a great day, everybody.
Bye bye.

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How AI Changed a 150-Year-Old Industrial Manufacturing Company! | CXOTalk #901

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.css-14f5ked{margin:0;word-break:break-word;display:-webkit-box;-webkit-box-orient:vertical;box-orient:vertical;-webkit-line-clamp:2;overflow:hidden;}How AI Changed a 150-Year-Old Industrial Manufacturing Company! | CXOTalk #901