September 18, 2025 • 33 mins
In this episode of Let’s Talk Pricing, host Kevin Mitchell (President of PPS) is joined by Jeet Mukherjee, who will be speaking at this year’s Fall Conference. Jeet shares his vision for the “bionic pricing team”—where human judgment and AI-driven insights combine to unlock stronger performance, protect margins, and transform the way pricing teams partner across the business.
🎙️ Key topics include:
What a “bionic” pricing team looks like in practice
Where companies should start on their AI journey without overwhelming teams
Practical examples of using AI agents to uncover insights and plug margin leaks
How AI changes collaboration between pricing and sales
The evolving skills every pricing professional needs to thrive in the next 5–10 years
Steps leaders can take today to start building their own bionic teams
Don’t miss Jeet’s session at PPS profitABLE25 in Las Vegas, October 21–24, where he’ll go even deeper on these ideas and help you chart the path toward building the pricing team of the future.
👉 Learn more and register: pricingsociety.com/ppslv25
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
You're listening to Let's Talk Pricing, your connection to the
voices, stories, and strategies shaping the pricing world.
Each episode, we go beyond theory into the practical,
timely conversations that help you lead with confidence and
drive results. Let's start the show.
Hello and very good day everyone.
I'm Kevin Mitchell from Professional Pricing Society and
(00:23):
I get to welcome everyone to ourgreat podcast, Let's Talk
Pricing. And today I'm very, very excited
because we're going to be talking with Mr. Jeet Mukherjee,
who is Chief Strategy Officer atHolden Advisors and we're going
to talk about a few things. We're going to talk about the
people side and team building and how that interacts with
(00:45):
artificial intelligence, with AIand, and lots more.
But a lot of us in the professional pricing society
community already know of Jeet and his accomplishments.
He is the author of Pricing withConfidence, which of course
would highly recommend to everyone.
It is a classic book with a lot of great insights.
(01:06):
And if you don't already have a copy of that, I would certainly
give my highest recommendation for Jeet's book Pricing with
Confidence because just of the great lessons that it has for
people in pricing and revenue management and in related spaces
as well. And also we're very happy here
(01:27):
with the PPS team with everything that we have going
on. Coming up in the fall, Obviously
we have the PPS Profitable conference in Las Vegas October
21st or 24th. We have PPS Profitable Barcelona
from December 2nd to the 5th. And in the fall is when we have
two of our major events, which is great for me and my team
(01:48):
because we get to connect with old friends, meet new friends,
hear from experts like Jeet, andbasically this is where we get
everyone together. But we're going to start our
Let's Talk Pricing podcast with Mr. Jeep Mukherjee.
Jeep, I'm always excited to talkwith you.
How are you today, Sir? I'm doing fantastic.
How can I not be I? I love being here and I love
(02:10):
chatting with you, Kevin, you. You and the team have done a lot
for the pricing community and it's always a pleasure being a
part of it. Oh, thank you.
How nice. Appreciate that.
Thank you for your partnership with us as well.
And hey, thanks for all that youdo for people in and around our
space. Also with your experience, with
your expertise and with a lot ofgreat learnings there.
(02:32):
And G, the PPS team and I are honored that you're going to be
joining us in the profitable conference in Las Vegas.
I can't wait for your session. Your session is entitled How to
Build a Bionic Pricing team. Can you tell us a little bit
about how you came about, how you came around with that title
(02:52):
and what exactly you mean by building a bionic pricing team?
Yeah, yeah. Obviously it's a little tongue
in cheek, but you know, I think it's, it's interesting.
As I was looking at consulting, so we were looking internal
first before we looked external.And as we're looking at
internal, we realized that. We're.
(03:14):
The no AHA here, which is we're in the age of AI, we're in the
age of transformation through digitization in a lot of
different ways. And it also impacts our
capabilities and what we focus our attention on.
Yes, there is going to be a needto understand AI and capability
building around AI, of course. But I think what this allows us,
(03:38):
this wave of change that's upon us right now, what it allows us
to do is to focus on higher value activities as a pricing
team, as a pricing professional,which is what I want to focus
our attention on and build capabilities.
And what we're finding with our team on a consulting basis is
(04:00):
getting to insights faster through the use of tools and
mechanisms that's all around us.But then what do we do with
those insights? Because at the end of the day,
and, and Kevin, you've been around for a long time and we
should probably chat about some of the olden days and now and,
and compare it. Because I would make a bet that
even during the days of, you know, some of the older
(04:22):
professionals that were around, it's the same issue that we have
today, regardless of whether we have AI or not, which is they're
scared to make a change on a customer because they're afraid
that they're going to lose the customer.
So, you know, even if I look at consulting and I look at
consultants, you know, AI and other tools, other mechanisms
help us get to insights faster. But the capability that we need
(04:45):
to build is how do we take thoseinsights and Dr. change within
our organization and make it value add from that perspective
to allow us to make the changes that we know we have to make.
And that implementation side that that side of actually
putting things into place and not making excuses for why we
(05:06):
can't do something sometimes, you know, it's human nature
because we don't like doing those difficult things
sometimes. I think that's the focus.
So we'll talk a little bit, you know, in the session about, of
course, technology and what we need to do to get to insides
faster. And then we're going to spend a
little bit of time talking aboutwhat does that really mean?
Where does that energy now move to that we take away from where
(05:30):
it used to be and setting up data and analyzing certain
things. If we can get to insides faster,
where do we go next is the question.
Quick break If you're serious about growing your pricing
career, you need to hear this. We're heading to Las Vegas this
October 21st through 24th for PPS Profitable Pricing's
preeminent conference. Four days of bold strategies,
(05:51):
practical tools, and real world solutions to help you boost
margins, lead with confidence, and accelerate your career.
Connect with pricing leaders, sharpen your skills, and leave
ready to make an immediate impact.
And if you're bringing your team, be sure to check out our
bundled group rates for even more value.
secure.yourspottoday@pricingsociety.comPPS LV25.
(06:16):
All right, back to the show. I think what's interesting is in
spite of the radical changes that we're seeing with the
tools, the emphasis that we haveon data and next steps and
speed. Central to all of that, as you
mentioned, is kind of the human nature, the inertia, our own
(06:37):
internal resistance to change tonew things, which is an
evolutionary thing that goes back from when we became human.
And so it's very interesting to me that in spite of all of the
new things that are going on, westill, as you mentioned, still
have some of those same challenges around change
management, around having to lead our teams to manage our
(07:00):
teams, around having to work with our customers in our
marketplaces. But those challenges are still
there. But of course, with the new
tools, since the information is quicker, the decisions have to
be made more quickly as well. And so it kind of amplifies the
need for the human Gray cells, so to speak out here that we
that we all have. So I think it's very interesting
(07:21):
how it kind of comes full circle.
Yeah, that's, that's fascinatingthere.
So since you obviously are an expert in what AI can do for
pricing teams, revenue management teams and how it is
transforming us, how it is becoming this wave of change, as
(07:41):
you said, what how can we use AIin the most effective and
efficient ways? How can we use AI to uncover
insights, to plug leaks, to makeus better at our jobs?
Yeah, I think, you know, first of all, you know, and you'll
remember this, Kevin, back in the day, everybody was talking
(08:03):
about quote UN quote, big data. You know, you can have an Excel
spreadsheet, but everybody wanted to use the phrase big
data. You know, it's almost like
directly correlated the valuation of your company if you
use that phrase. I feel like we're in that world
now. You know, it's it's you have to
use AI. If you don't, your company's not
going to be valued. Pretty soon we'll be
professional pricing society that AI, right?
(08:24):
And every, everything has to have AI in it or use AI in some
way, shape or form. And I think that shiny syndrome
is still there. It's there and, and in spades.
But we, we're, we live in a world of reality, right?
And we have to think through what are we actually using it
for? How are we using it and what's
the right way to use it? And, and for us, what we're
(08:46):
doing. And we can only speak to what
we're doing within Holden and then how we're going to take
some things to to the rest of the market as we help clients
realize the value that they're creating through pricing is
we're looking at it one way where we're saying for security
(09:07):
reasons, if you want to build AIbox within your four walls, what
does that look like? What does that mean from a
infrastructure perspective? What does it mean for a from,
from a investment perspective, from a security perspective,
what does it mean? What can you do?
What kind of skill sets now do you have to keep within your
four walls to be able to help build it, manage it, maintain
(09:29):
it, and all of that good stuff, right?
So we're going to have a case study for that.
But there's a very important case study.
If you deal with a lot of data that's very, very critical that
can't leave your four walls, then you may want to consider
doing something like that. But it comes with pros and cons.
So what are those pros and cons?And we'll talk through those a
little bit. And then you've got the world of
(09:53):
AI that says, hey, it's a, it's a large language model.
We can do analysis that we haven't been able to do before.
Now we can do it with greater accuracy.
We don't need the skill set. We don't need a Python skill
set. We don't need those things.
We can kind of just do it ourselves as consultants or as
practitioners. You've got that world and then
you've got the world of automation, you know, the
(10:14):
agentic AI, you've got the worldof, hey, I don't want to touch
it. You go ahead and do it as long
as it's within these, within these parameters.
And that's sort of the third usecase.
So we're going to kind of walk through those three use cases
because I think there's applications for all three of
them for, for us. And what we are recommending our
(10:34):
clients do is start with that second one, which we think is
the is sort of the, the fastest way to realize value.
If you don't use currently any large language models, if you
want to start using them, that'sa great spot to start where you
can do regressions very quickly.You can do correlation studies
very quickly. The, the simple analytics that
(10:55):
we help our clients through fromjust the basics of pricing and
looking for opportunities. Now our clients are able to do
that, whether they have that skill set or not in a much rapid
fashion. And we can help them sort of
guide them through how they can get to insights quickly.
And that second use case becomesextremely important.
It's a good starting point. Understood and thank you.
(11:18):
And yeah, looking forward to hearing much more about those
three use cases. But since you mentioned that
it's the second one, which is a good first step for a lot of our
people out there using the LLMS,using the large language models
to really get more information more quickly, to do a lot of the
legwork with having the guide rails in place, of course, How
(11:44):
can team see the benefit of that?
What does this mean to pricing managers as they get better at
using large language models, at using the AI tools?
What kind of results can we expect them to see when they do
this? Yeah, one of the one of the
toughest things in analytics as,as you know very well, Kevin,
(12:07):
is, is, is cleaning and staging of the data.
So just a simple being able to find data in your
infrastructure, that's going to be meaningful.
That's the first step, being able to extract that data, bring
it over, you know, clean it, stage it, put it together with
other pieces of data that's everywhere.
This is all internal. Then you may want to bring in
(12:28):
market data that's external and added to that data set.
That process takes so much time right now.
It takes a lot of Labor, dependson how fragmented things are.
It just, it could take you weeks, months, in some cases a
year or more, depending on how how ugly that data set looks
like. And now you're talking about
(12:50):
being able to use AI to be able to 1st locate data that would
help you that makes sense. And then bring it over, clean it
in stages. So the first layer that we're
seeing that we're getting a lot of benefits from is just the
data operations of things. That's the first layer of value
that we're getting, which is tremendous where sometimes it
(13:11):
would take consultants weeks, 3-4 weeks is now taking them a
week and a half to do. And we're not a the best
barometer for that, for that value because you know, we're
also learning about the data because we're go outsiders going
into a client environment, right.
But if you're already in your environment, that might, that
could be even quicker for you. So now you've got the ability to
(13:34):
do your data OPS in much rapid fashion.
And then the accuracy is higher because you're able to identify
data elements that you know it'sgoing to, you know, fit well
within what you're trying to do as an outcome.
The second piece is the analysisitself, where now you focus on
outcomes. What are you looking for?
What do you want to do instead of trying to figure out what
(13:55):
kind of analysis can I run and how will this analysis look and
how do I justify running this analysis versus that analysis?
All of that is taken out. Now you can focus on outcomes.
I'm looking for correlation. I'm looking to see what affects
my margin. I'm looking to see that.
So that natural language processing, that ability to
speak to a agent and be able to have the agent run what is
(14:19):
accurate and be able to give youand justify the outputs and, and
tell you how it came to those outputs.
Those are all, I mean, you're, you're saving weeks, if not
months at that point of the analysis part.
And now it's much more effectivewhen you take that to your
organization. This October 21st through 24th,
PPS Profitable takes over Las Vegas.
(14:40):
It's four days packed with cutting edge strategies, hands
on workshops and real world insights to help you lead
pricing with confidence and deliver measurable results.
You'll connect with top pricing leaders from around the world,
build powerful peer networks, and walk away with tools you can
put to work the very next day. Learnmoreandreserveyourspot@pricingsociety.com/P
PS (15:03):
LV25 Now let's get back into the conversation.
We will remember that we can improve data operations, we can
improve our accuracy and our speed there with AI tools, but
also we can use the LLM, the large language model to focus on
our outcomes desired, the correlations that we're looking
(15:26):
for, our KPI's. Basically we can have everything
kind of wrapped up where we can get everything moving in the
right direction here. Yeah, and it can sorry real 11
quick thing Kevin, it's it's also, you know, it's the outcome
and it's also a problem statement.
So if you have a problem statement that you're struggling
with your organization struggling with it does a good
job of kind of asking questions and getting down to root sort of
(15:50):
analysis. The, the thing I will tell you
in this environment that we didn't touch on is it's
important to know what your capabilities are to build these,
build these agents because your capability may not be there to
build them. You may want to use a third
party to build it or you may want to use a another player to
use their agents. So ChatGPT we know has some
(16:13):
capabilities to do that. We have other large language
models that have the ability to do that.
So you can kind of use that instead of building your own
That's there. I know some of our partners,
software partners are building their own and allowing us to use
it. So Price FX is a good, good
example of that where they're building their agents and
they're releasing that, which I think they've done a really good
(16:35):
job of. So you don't have to be an
expert to build these things. You have to be an expert of
knowing what you need. And then I think there are
options that are out there that it is best for you in that
environment. Yeah.
I think it's interesting as you mentioned that there are
depending on your company, your bandwidth, your operations and
(16:57):
your culture, there are a lot ofdifferent ways that you can do
this. It can be an internally
developed thing, It can be an off the shelf thing.
It can be part of a pricing optimization software package as
you mentioned. So there are a lot of different
ways to approach this. And of course, this is 1 where
we are not able to ask AI which one should I do?
That's going to have to be an internal decision.
(17:19):
That's going to have to be a management decision with a lot
of variables in there to pick the best tool for your
organization and for, for your needs there.
And another question for you, Jeet is of course we as pricing
people know that we can be seen as kind of an ivory tower to the
(17:41):
people who do the very difficultjobs of sales out on the street
and for other departments, be itoperations, finance, marketing,
product management, what, what have you there.
So how can we build this bionic team to also foster better
relations with sales, with marketing, with finance, with
(18:01):
other departments, internal partners that we have to work
with because we know as pricing people, it is our job to kind of
be at the Nexus there and to be in close touch with a lot of
different parties within our ownorganization.
Sometimes those relationships might have a modicum of head
butting, you know, some agency issues and things like that as
(18:22):
well. So in what ways can this bionic
pricing team help us to make those relationships better to
explain what's going on kind of behind the scenes from our non
ivory towers since we know the truth there?
Yeah, Yeah. I think that's, that's the
$1,000,000 question that it seems like it's always there.
(18:42):
You know, it's with a, with AR without AI.
It's just that that's a big question.
And one of the biggest things wefind, especially when we work
with our clients is if the client has a good strong pricing
person who is a good influencer.They don't have to be the
smartest person in the world that knows everything and that
can do large language models by himself and writing Python codes
(19:05):
and you know, at night and influencing everybody.
But it's it's amazing that people think that they need to
be a certain way. So they kind of try to behave
that way. But the biggest attribute that
we see is really that influence piece.
The pricing has always been cross functional.
And the biggest attribute you can have as a strong pricing
professional is your ability to influence.
(19:27):
And that goes for a lot of other, you know, a lot of other
roles as well. But pricing is extremely
important that you have to know how to how to influence a
salesperson, how to influence finance, how to influence
marketing, how to influence, youname it, fill in the blank.
And if you're not able to do that, I don't care how smart you
are, how capable you are, how many analytics you can run.
(19:50):
It's just you're not going to beas successful.
And I think with AI, that attribute becomes even more
important because now what you can do is I've seen some folks
focus on KPIs that are importantto finance and not bring in
other KPIs. Just focus on those.
Now you've got this ability to have data at your fingertips and
(20:11):
be able to. Look at data in different ways
and the outputs in different ways to influence different
different groups and what they care about.
So the question becomes what is finance care about?
What is what is sales care about?
Sales isn't a very difficult situation.
As an example that we all know they are tasked with raising
(20:31):
prices, for example. They're tasked with holding
their line. They're tasked with very
difficult negotiation. So the question is, do they have
everything that they need to be successful in those sales
scenarios? What do they need?
Now you're able to get them whatthey need at faster rates than
ever before. So now you have to really,
really focus in on your sort of characteristic of can I
(20:54):
understand Kevin as a salesperson, what he needs to be
successful and how fast can I give Kevin all of that so he can
be successful then the whole organization be successful.
I think those are those are the attributes that really now we
can focus on after we get through this sort of development
of the AI and development of theoutputs that we know are going
(21:15):
to be necessary for us to be good.
Yeah, absolutely. And as we know, good pricing
people have to speak all of these different languages and
deal with all of these differentparties in a matter that they
can understand. And the example that we always
talk about is you cannot talk with a 25 year veteran
(21:36):
salesperson the same way that you might talk to a new finance
MBA. Those are different languages
and of course with senior management, you have to have
your elevator pitch your 32nd one minute speech on this is
everything that we're doing, This is why we're doing it.
This is The Who, what, when, where, why and how and that's
(21:56):
got to be down pat. So that's a different language
that we'd have to speak to a different group.
But I think the good news in allof this Jeep is that we as
pricing professionals, we as revenue managers, kind of it's
another full circle example where this probably for a lot of
us has been part of our job for a long time to deal with all of
(22:17):
these parties. But the new tools, the agentic
AI, the LMS, all of these other things essentially make those
transactions that much more quick, that much more fruitful,
hopefully, and really gives us some quicker, better insights
there if we're using the tools properly.
So really it is a case where this might be a little eye
(22:41):
opening. And of course, with human nature
being human nature, it might be a little concerning for a lot of
pricing people. But then again, this is
something that a lot of us have been doing in a different way
with different tools for a long time as well.
So I think that's, that's interesting there.
And of course, with self preservation being first on our,
(23:06):
our Maslow hierarchy, so to speak, I know that I hear a wide
variety of thinking about what AI means for us as the human
beings, as a practitioners, as the experts in this space.
I would say that that varies from this is going to be a tool,
(23:26):
much in the same way that 100 years ago a typewriter was a
tool or a calculator or the PC or the Internet.
It's just that and expanding on that.
And that probably goes all the way to the other end to Skynet
in the Terminator movies, where it's going to take over and do
its own thing. So what are your thoughts about
(23:46):
that for pricing practitioners, for revenue managers, as these
tools are coming in, Should we be worried about AI replacing
human judgement, about replacinghuman beings here?
Yeah, yeah, I think it's one of the most fascinating questions.
And I, I, I love, I absolutely love chatting about it because
there, there are, you know, in my head, I kind of go through
(24:09):
notes and there's different sortof branches of the tree that you
can imagine as we can't go through this journey.
First of all, the thing that I tell myself all the time is
we're so early to this change tothis sort of market life cycle.
You know, we, we're not at the mature level.
We're, we're still very early. So we, we don't know what's
(24:30):
going to happen, right? We can try to, you know, go
through the nodes and try to predict and of course, but we
don't know it's very early. So that's, it's a perfect time
for people to jump in. So let's talk a little bit about
what I think would be some of the negative consequences, which
I do think there will be job losses.
There's going to be job losses more in some industries than
(24:50):
others. I think consulting is going to
go through some changes. I think we're already seeing it.
I think a lot of consulting companies are going through
layoffs and things like that. So there's certain types of
skill set that it's going to automate, especially if you talk
about a gentic, right? The gentic when your automation
of process flows and decision making, you're outsourcing that
(25:12):
to a logic or a algorithm. You're not going to need a
person to do that. You know, at a desk, it's just
you don't. So therefore you will lose that
job. You may not want to lose that
person. So there's a difference between
a job loss and losing a person, you know, And because I truly
(25:32):
believe that as we solve for problems, there are always other
problems that pop up. You know, as, as we've been
through the, the revolution of the Internet, the evolution of
the Internet, there's some issues that the Internet caused
that we never even could think about.
So using our sort of paradigm ofwhat's right and wrong and
(25:53):
applying it to AI, that's not really correct because you don't
know where it's going to go. So we don't know what other
issues it's going to create, what opportunities it's going to
create. And we also don't know what
other problems we can actually solve for as AI gets better.
So we only know what we can solve for now given what our
(26:13):
human, human sort of logic allows us to think through.
But I think in the future we'll be able to solve for problems
that we can't even think of right now because of AI.
So I think that's the positive side.
So if anybody feels like their job is threatened or their role
is threatened, what have you, I think there's that thought
process that is like that I tellmy 23 and 21 year old, which is
(26:35):
think about things that we aren't thinking about, right?
That's, that's the key is how doyou solve a problem that we're
not even thinking about? Because now you have the ability
to do so at a much faster rate. You have the ability to create
companies at a faster rate and solve these problems.
So the good side is really quitehigh.
So the negative side is, yes, there will be job loss, but on
the other side, there will be huge amounts of growth because
(26:57):
we're going to be able to solve problems that we just don't know
of yet. So that's the positive side of
things. And the other thing that, you
know, I think is important that we just talked about, which is
the human ability to make changeis still a problem.
That's still the limiting factorhere.
So I don't care how many processes you, you know, put in
front. I don't care what you do at the
(27:17):
end of the day. You know, somebody's got to buy
that good and somebody's going to have to pay for that.
You know, it has to happen in a certain way.
You're going to have to consume in some way, shape or form the
laws of that. That role is not going to change
those roles and those fundamental facts.
So I think we can be safe to assume that, you know, we can
(27:37):
automate up to a certain point and after that certain point,
you still need humans to be ableto take it that last bit of of
that transaction. And that's the piece that I
think there's going to be a lot of value in it for organizations
as we move forward. Understood.
So there, there will be job shifts, jobs that we think of
(27:59):
that exist now where your jobs to do AB and C that may go away.
Hopefully the people will not goaway and the new job will be
something that shifts to DENF orXY and Z or something like that.
So can certainly understand about the job shifts.
But of course, that's something that happened, has happened all
the time in different ways. With every new technological
(28:22):
mini step we see that that happening.
It is, by the way, it is an incredible opportunities for
young professionals. So if you're a young
professional coming out, you know, now I guess I'm a
dinosaur, right? So if you, if you understand and
know and you know how to apply this to make, you know, create
value and make changes to organization, you might be able
(28:45):
to leapfrog these dinosaurs likemyself and get on top, right?
Because you have that capability, you know how to use
it at a faster rate, better way,what have you.
So it creates a unique opportunity for young
professionals coming in to have a skill set, understand and have
the know how to be able to leapfrog some of the dinosaurs
(29:05):
that don't want to change. Understood.
Yeah. And that's one of those great
opportunities that you talked about that will come around.
But obviously, we will see that people who understand and who
are good at using these tools certainly will have a big leg
up. We'll be able to make their own
path and a lot of organizations and we'll have these great
(29:27):
opportunities for their future to come there.
So would you say that that is one of the things that we're
going to see as pricing teams develop?
Is it going to be kind of this bionic model as you talk about
where the people who become, whobecome most attractive for these
(29:48):
types of jobs will be the peoplewho can take that human
intellect, that need for change management, be able to use the
tools in the best way and move forward that way?
Is that an example of your bionic team there?
That's right. That's right.
I think, I think what we'll see in the short term is a big shift
to everybody working on AI or doing AI something.
It's gonna get very technical and then people are gonna
(30:11):
realize, shoot, we've gotten tootechnical and we're not driving
change in the organization. Then it's gonna, the pendulum is
gonna start swinging back to theother side and it's gonna
probably land on exactly what you said, which is, look, we
need to understand the technicalcomponents of it.
We need to get to insights faster.
But we need that business acumen, that person that knows
(30:32):
how to influence and drive change through the organization
to actually make this change or,you know, price increase or what
have you actually stick within our four walls that that's still
a skill set that's going to be necessary.
So we kind of loop back to pricing people being both the
artists and the scientists. That's right, it's going.
To it's going to be a full circle, you know, and, and it's
(30:54):
and I, I just don't think, you know, honestly.
And maybe I'm a pessimist, Kevin, and, and we've talked
about this before, which is I don't see the world of I've
already made my decision now make the math work so my
decision feels more right. I don't I don't see that quite
changing so quickly. So yeah, you know, you're going
to see the AI output and some executive was going to go.
(31:16):
Can you rerun that a little bit different?
I wanna see it from this angle because it's not picking up on
something. Yes, yeah.
The example I remember from, I think it was a statistics
textbook that I had, we'll say, a few decades ago.
It says that sometimes we use new tools and statistics the way
that a drunk person uses a lightpole.
(31:39):
You don't use it for illumination, you use it for
support. You're looking to support your
decision instead of find out something new and to to get
illuminated there. So yeah, hopefully we'll use the
tools in the right way for illumination and not support of
something that we already. Decided that bias is tough to
get get past sometimes as humans.
(31:59):
That is very tough. Human beings are still going to
be human beings in, in that way.And of of course, hey, that's
why change management is so critical to to all of this.
All right, so G, we are very, very much looking forward to
seeing you at PPS profitable in Las Vegas, hearing more about
how to build a bionic pricing team.
(32:21):
And of course, I would, I highlyrecommend that everyone pick up
pricing with confidence if you have not done so already.
And you can connect with Jeet, I'm sure on LinkedIn or you can
look for Holden Advisors and youwill find him there.
For information from the PPS team and me.
Of course, you can check pricingsociety.com about our
(32:44):
online training, about our upcoming events, about our
membership service certificationand much, much more.
But please reach out to me to have any, any questions, connect
with Jeet, of course, if you have any questions or if you
want more insights about what we've been, what we've been
talking about today. But I want to thank everyone for
tuning in to this edition of theLet's Talk Pricing podcast.
(33:08):
And Jeet, thank you so much. Always good to see you.
Looking forward to seeing you again as well, Sir.
Thank you very much. Really appreciate it.
All right. So thank you again to Mr. Jeet
Mukherjee, thanks to the PPS team, and we will hope to see
you at another edition of the Let's Talk Pricing podcast
coming soon.
You're listening to Let's Talk Pricing, your connection to the
voices, stories, and strategies shaping the pricing world.
Each episode, we go beyond theory into the practical,
timely conversations that help you lead with confidence and
drive results. Let's start the show.
Hello and very good day everyone.
I'm Kevin Mitchell from Professional Pricing Society and
(00:23):
I get to welcome everyone to ourgreat podcast, Let's Talk
Pricing. And today I'm very, very excited
because we're going to be talking with Mr. Jeet Mukherjee,
who is Chief Strategy Officer atHolden Advisors and we're going
to talk about a few things. We're going to talk about the
people side and team building and how that interacts with
(00:45):
artificial intelligence, with AIand, and lots more.
But a lot of us in the professional pricing society
community already know of Jeet and his accomplishments.
He is the author of Pricing withConfidence, which of course
would highly recommend to everyone.
It is a classic book with a lot of great insights.
(01:06):
And if you don't already have a copy of that, I would certainly
give my highest recommendation for Jeet's book Pricing with
Confidence because just of the great lessons that it has for
people in pricing and revenue management and in related spaces
as well. And also we're very happy here
(01:27):
with the PPS team with everything that we have going
on. Coming up in the fall, Obviously
we have the PPS Profitable conference in Las Vegas October
21st or 24th. We have PPS Profitable Barcelona
from December 2nd to the 5th. And in the fall is when we have
two of our major events, which is great for me and my team
(01:48):
because we get to connect with old friends, meet new friends,
hear from experts like Jeet, andbasically this is where we get
everyone together. But we're going to start our
Let's Talk Pricing podcast with Mr. Jeep Mukherjee.
Jeep, I'm always excited to talkwith you.
How are you today, Sir? I'm doing fantastic.
How can I not be I? I love being here and I love
(02:10):
chatting with you, Kevin, you. You and the team have done a lot
for the pricing community and it's always a pleasure being a
part of it. Oh, thank you.
How nice. Appreciate that.
Thank you for your partnership with us as well.
And hey, thanks for all that youdo for people in and around our
space. Also with your experience, with
your expertise and with a lot ofgreat learnings there.
(02:32):
And G, the PPS team and I are honored that you're going to be
joining us in the profitable conference in Las Vegas.
I can't wait for your session. Your session is entitled How to
Build a Bionic Pricing team. Can you tell us a little bit
about how you came about, how you came around with that title
(02:52):
and what exactly you mean by building a bionic pricing team?
Yeah, yeah. Obviously it's a little tongue
in cheek, but you know, I think it's, it's interesting.
As I was looking at consulting, so we were looking internal
first before we looked external.And as we're looking at
internal, we realized that. We're.
(03:14):
The no AHA here, which is we're in the age of AI, we're in the
age of transformation through digitization in a lot of
different ways. And it also impacts our
capabilities and what we focus our attention on.
Yes, there is going to be a needto understand AI and capability
building around AI, of course. But I think what this allows us,
(03:38):
this wave of change that's upon us right now, what it allows us
to do is to focus on higher value activities as a pricing
team, as a pricing professional,which is what I want to focus
our attention on and build capabilities.
And what we're finding with our team on a consulting basis is
(04:00):
getting to insights faster through the use of tools and
mechanisms that's all around us.But then what do we do with
those insights? Because at the end of the day,
and, and Kevin, you've been around for a long time and we
should probably chat about some of the olden days and now and,
and compare it. Because I would make a bet that
even during the days of, you know, some of the older
(04:22):
professionals that were around, it's the same issue that we have
today, regardless of whether we have AI or not, which is they're
scared to make a change on a customer because they're afraid
that they're going to lose the customer.
So, you know, even if I look at consulting and I look at
consultants, you know, AI and other tools, other mechanisms
help us get to insights faster. But the capability that we need
(04:45):
to build is how do we take thoseinsights and Dr. change within
our organization and make it value add from that perspective
to allow us to make the changes that we know we have to make.
And that implementation side that that side of actually
putting things into place and not making excuses for why we
(05:06):
can't do something sometimes, you know, it's human nature
because we don't like doing those difficult things
sometimes. I think that's the focus.
So we'll talk a little bit, you know, in the session about, of
course, technology and what we need to do to get to insides
faster. And then we're going to spend a
little bit of time talking aboutwhat does that really mean?
Where does that energy now move to that we take away from where
(05:30):
it used to be and setting up data and analyzing certain
things. If we can get to insides faster,
where do we go next is the question.
Quick break If you're serious about growing your pricing
career, you need to hear this. We're heading to Las Vegas this
October 21st through 24th for PPS Profitable Pricing's
preeminent conference. Four days of bold strategies,
(05:51):
practical tools, and real world solutions to help you boost
margins, lead with confidence, and accelerate your career.
Connect with pricing leaders, sharpen your skills, and leave
ready to make an immediate impact.
And if you're bringing your team, be sure to check out our
bundled group rates for even more value.
secure.yourspottoday@pricingsociety.comPPS LV25.
(06:16):
All right, back to the show. I think what's interesting is in
spite of the radical changes that we're seeing with the
tools, the emphasis that we haveon data and next steps and
speed. Central to all of that, as you
mentioned, is kind of the human nature, the inertia, our own
(06:37):
internal resistance to change tonew things, which is an
evolutionary thing that goes back from when we became human.
And so it's very interesting to me that in spite of all of the
new things that are going on, westill, as you mentioned, still
have some of those same challenges around change
management, around having to lead our teams to manage our
(07:00):
teams, around having to work with our customers in our
marketplaces. But those challenges are still
there. But of course, with the new
tools, since the information is quicker, the decisions have to
be made more quickly as well. And so it kind of amplifies the
need for the human Gray cells, so to speak out here that we
that we all have. So I think it's very interesting
(07:21):
how it kind of comes full circle.
Yeah, that's, that's fascinatingthere.
So since you obviously are an expert in what AI can do for
pricing teams, revenue management teams and how it is
transforming us, how it is becoming this wave of change, as
(07:41):
you said, what how can we use AIin the most effective and
efficient ways? How can we use AI to uncover
insights, to plug leaks, to makeus better at our jobs?
Yeah, I think, you know, first of all, you know, and you'll
remember this, Kevin, back in the day, everybody was talking
(08:03):
about quote UN quote, big data. You know, you can have an Excel
spreadsheet, but everybody wanted to use the phrase big
data. You know, it's almost like
directly correlated the valuation of your company if you
use that phrase. I feel like we're in that world
now. You know, it's it's you have to
use AI. If you don't, your company's not
going to be valued. Pretty soon we'll be
professional pricing society that AI, right?
(08:24):
And every, everything has to have AI in it or use AI in some
way, shape or form. And I think that shiny syndrome
is still there. It's there and, and in spades.
But we, we're, we live in a world of reality, right?
And we have to think through what are we actually using it
for? How are we using it and what's
the right way to use it? And, and for us, what we're
(08:46):
doing. And we can only speak to what
we're doing within Holden and then how we're going to take
some things to to the rest of the market as we help clients
realize the value that they're creating through pricing is
we're looking at it one way where we're saying for security
(09:07):
reasons, if you want to build AIbox within your four walls, what
does that look like? What does that mean from a
infrastructure perspective? What does it mean for a from,
from a investment perspective, from a security perspective,
what does it mean? What can you do?
What kind of skill sets now do you have to keep within your
four walls to be able to help build it, manage it, maintain
(09:29):
it, and all of that good stuff, right?
So we're going to have a case study for that.
But there's a very important case study.
If you deal with a lot of data that's very, very critical that
can't leave your four walls, then you may want to consider
doing something like that. But it comes with pros and cons.
So what are those pros and cons?And we'll talk through those a
little bit. And then you've got the world of
(09:53):
AI that says, hey, it's a, it's a large language model.
We can do analysis that we haven't been able to do before.
Now we can do it with greater accuracy.
We don't need the skill set. We don't need a Python skill
set. We don't need those things.
We can kind of just do it ourselves as consultants or as
practitioners. You've got that world and then
you've got the world of automation, you know, the
(10:14):
agentic AI, you've got the worldof, hey, I don't want to touch
it. You go ahead and do it as long
as it's within these, within these parameters.
And that's sort of the third usecase.
So we're going to kind of walk through those three use cases
because I think there's applications for all three of
them for, for us. And what we are recommending our
(10:34):
clients do is start with that second one, which we think is
the is sort of the, the fastest way to realize value.
If you don't use currently any large language models, if you
want to start using them, that'sa great spot to start where you
can do regressions very quickly.You can do correlation studies
very quickly. The, the simple analytics that
(10:55):
we help our clients through fromjust the basics of pricing and
looking for opportunities. Now our clients are able to do
that, whether they have that skill set or not in a much rapid
fashion. And we can help them sort of
guide them through how they can get to insights quickly.
And that second use case becomesextremely important.
It's a good starting point. Understood and thank you.
(11:18):
And yeah, looking forward to hearing much more about those
three use cases. But since you mentioned that
it's the second one, which is a good first step for a lot of our
people out there using the LLMS,using the large language models
to really get more information more quickly, to do a lot of the
legwork with having the guide rails in place, of course, How
(11:44):
can team see the benefit of that?
What does this mean to pricing managers as they get better at
using large language models, at using the AI tools?
What kind of results can we expect them to see when they do
this? Yeah, one of the one of the
toughest things in analytics as,as you know very well, Kevin,
(12:07):
is, is, is cleaning and staging of the data.
So just a simple being able to find data in your
infrastructure, that's going to be meaningful.
That's the first step, being able to extract that data, bring
it over, you know, clean it, stage it, put it together with
other pieces of data that's everywhere.
This is all internal. Then you may want to bring in
(12:28):
market data that's external and added to that data set.
That process takes so much time right now.
It takes a lot of Labor, dependson how fragmented things are.
It just, it could take you weeks, months, in some cases a
year or more, depending on how how ugly that data set looks
like. And now you're talking about
(12:50):
being able to use AI to be able to 1st locate data that would
help you that makes sense. And then bring it over, clean it
in stages. So the first layer that we're
seeing that we're getting a lot of benefits from is just the
data operations of things. That's the first layer of value
that we're getting, which is tremendous where sometimes it
(13:11):
would take consultants weeks, 3-4 weeks is now taking them a
week and a half to do. And we're not a the best
barometer for that, for that value because you know, we're
also learning about the data because we're go outsiders going
into a client environment, right.
But if you're already in your environment, that might, that
could be even quicker for you. So now you've got the ability to
(13:34):
do your data OPS in much rapid fashion.
And then the accuracy is higher because you're able to identify
data elements that you know it'sgoing to, you know, fit well
within what you're trying to do as an outcome.
The second piece is the analysisitself, where now you focus on
outcomes. What are you looking for?
What do you want to do instead of trying to figure out what
(13:55):
kind of analysis can I run and how will this analysis look and
how do I justify running this analysis versus that analysis?
All of that is taken out. Now you can focus on outcomes.
I'm looking for correlation. I'm looking to see what affects
my margin. I'm looking to see that.
So that natural language processing, that ability to
speak to a agent and be able to have the agent run what is
(14:19):
accurate and be able to give youand justify the outputs and, and
tell you how it came to those outputs.
Those are all, I mean, you're, you're saving weeks, if not
months at that point of the analysis part.
And now it's much more effectivewhen you take that to your
organization. This October 21st through 24th,
PPS Profitable takes over Las Vegas.
(14:40):
It's four days packed with cutting edge strategies, hands
on workshops and real world insights to help you lead
pricing with confidence and deliver measurable results.
You'll connect with top pricing leaders from around the world,
build powerful peer networks, and walk away with tools you can
put to work the very next day. Learnmoreandreserveyourspot@pricingsociety.com/P
PS (15:03):
LV25 Now let's get back into the conversation.
We will remember that we can improve data operations, we can
improve our accuracy and our speed there with AI tools, but
also we can use the LLM, the large language model to focus on
our outcomes desired, the correlations that we're looking
(15:26):
for, our KPI's. Basically we can have everything
kind of wrapped up where we can get everything moving in the
right direction here. Yeah, and it can sorry real 11
quick thing Kevin, it's it's also, you know, it's the outcome
and it's also a problem statement.
So if you have a problem statement that you're struggling
with your organization struggling with it does a good
job of kind of asking questions and getting down to root sort of
(15:50):
analysis. The, the thing I will tell you
in this environment that we didn't touch on is it's
important to know what your capabilities are to build these,
build these agents because your capability may not be there to
build them. You may want to use a third
party to build it or you may want to use a another player to
use their agents. So ChatGPT we know has some
(16:13):
capabilities to do that. We have other large language
models that have the ability to do that.
So you can kind of use that instead of building your own
That's there. I know some of our partners,
software partners are building their own and allowing us to use
it. So Price FX is a good, good
example of that where they're building their agents and
they're releasing that, which I think they've done a really good
(16:35):
job of. So you don't have to be an
expert to build these things. You have to be an expert of
knowing what you need. And then I think there are
options that are out there that it is best for you in that
environment. Yeah.
I think it's interesting as you mentioned that there are
depending on your company, your bandwidth, your operations and
(16:57):
your culture, there are a lot ofdifferent ways that you can do
this. It can be an internally
developed thing, It can be an off the shelf thing.
It can be part of a pricing optimization software package as
you mentioned. So there are a lot of different
ways to approach this. And of course, this is 1 where
we are not able to ask AI which one should I do?
That's going to have to be an internal decision.
(17:19):
That's going to have to be a management decision with a lot
of variables in there to pick the best tool for your
organization and for, for your needs there.
And another question for you, Jeet is of course we as pricing
people know that we can be seen as kind of an ivory tower to the
(17:41):
people who do the very difficultjobs of sales out on the street
and for other departments, be itoperations, finance, marketing,
product management, what, what have you there.
So how can we build this bionic team to also foster better
relations with sales, with marketing, with finance, with
(18:01):
other departments, internal partners that we have to work
with because we know as pricing people, it is our job to kind of
be at the Nexus there and to be in close touch with a lot of
different parties within our ownorganization.
Sometimes those relationships might have a modicum of head
butting, you know, some agency issues and things like that as
(18:22):
well. So in what ways can this bionic
pricing team help us to make those relationships better to
explain what's going on kind of behind the scenes from our non
ivory towers since we know the truth there?
Yeah, Yeah. I think that's, that's the
$1,000,000 question that it seems like it's always there.
(18:42):
You know, it's with a, with AR without AI.
It's just that that's a big question.
And one of the biggest things wefind, especially when we work
with our clients is if the client has a good strong pricing
person who is a good influencer.They don't have to be the
smartest person in the world that knows everything and that
can do large language models by himself and writing Python codes
(19:05):
and you know, at night and influencing everybody.
But it's it's amazing that people think that they need to
be a certain way. So they kind of try to behave
that way. But the biggest attribute that
we see is really that influence piece.
The pricing has always been cross functional.
And the biggest attribute you can have as a strong pricing
professional is your ability to influence.
(19:27):
And that goes for a lot of other, you know, a lot of other
roles as well. But pricing is extremely
important that you have to know how to how to influence a
salesperson, how to influence finance, how to influence
marketing, how to influence, youname it, fill in the blank.
And if you're not able to do that, I don't care how smart you
are, how capable you are, how many analytics you can run.
(19:50):
It's just you're not going to beas successful.
And I think with AI, that attribute becomes even more
important because now what you can do is I've seen some folks
focus on KPIs that are importantto finance and not bring in
other KPIs. Just focus on those.
Now you've got this ability to have data at your fingertips and
(20:11):
be able to. Look at data in different ways
and the outputs in different ways to influence different
different groups and what they care about.
So the question becomes what is finance care about?
What is what is sales care about?
Sales isn't a very difficult situation.
As an example that we all know they are tasked with raising
(20:31):
prices, for example. They're tasked with holding
their line. They're tasked with very
difficult negotiation. So the question is, do they have
everything that they need to be successful in those sales
scenarios? What do they need?
Now you're able to get them whatthey need at faster rates than
ever before. So now you have to really,
really focus in on your sort of characteristic of can I
(20:54):
understand Kevin as a salesperson, what he needs to be
successful and how fast can I give Kevin all of that so he can
be successful then the whole organization be successful.
I think those are those are the attributes that really now we
can focus on after we get through this sort of development
of the AI and development of theoutputs that we know are going
(21:15):
to be necessary for us to be good.
Yeah, absolutely. And as we know, good pricing
people have to speak all of these different languages and
deal with all of these differentparties in a matter that they
can understand. And the example that we always
talk about is you cannot talk with a 25 year veteran
(21:36):
salesperson the same way that you might talk to a new finance
MBA. Those are different languages
and of course with senior management, you have to have
your elevator pitch your 32nd one minute speech on this is
everything that we're doing, This is why we're doing it.
This is The Who, what, when, where, why and how and that's
(21:56):
got to be down pat. So that's a different language
that we'd have to speak to a different group.
But I think the good news in allof this Jeep is that we as
pricing professionals, we as revenue managers, kind of it's
another full circle example where this probably for a lot of
us has been part of our job for a long time to deal with all of
(22:17):
these parties. But the new tools, the agentic
AI, the LMS, all of these other things essentially make those
transactions that much more quick, that much more fruitful,
hopefully, and really gives us some quicker, better insights
there if we're using the tools properly.
So really it is a case where this might be a little eye
(22:41):
opening. And of course, with human nature
being human nature, it might be a little concerning for a lot of
pricing people. But then again, this is
something that a lot of us have been doing in a different way
with different tools for a long time as well.
So I think that's, that's interesting there.
And of course, with self preservation being first on our,
(23:06):
our Maslow hierarchy, so to speak, I know that I hear a wide
variety of thinking about what AI means for us as the human
beings, as a practitioners, as the experts in this space.
I would say that that varies from this is going to be a tool,
(23:26):
much in the same way that 100 years ago a typewriter was a
tool or a calculator or the PC or the Internet.
It's just that and expanding on that.
And that probably goes all the way to the other end to Skynet
in the Terminator movies, where it's going to take over and do
its own thing. So what are your thoughts about
(23:46):
that for pricing practitioners, for revenue managers, as these
tools are coming in, Should we be worried about AI replacing
human judgement, about replacinghuman beings here?
Yeah, yeah, I think it's one of the most fascinating questions.
And I, I, I love, I absolutely love chatting about it because
there, there are, you know, in my head, I kind of go through
(24:09):
notes and there's different sortof branches of the tree that you
can imagine as we can't go through this journey.
First of all, the thing that I tell myself all the time is
we're so early to this change tothis sort of market life cycle.
You know, we, we're not at the mature level.
We're, we're still very early. So we, we don't know what's
(24:30):
going to happen, right? We can try to, you know, go
through the nodes and try to predict and of course, but we
don't know it's very early. So that's, it's a perfect time
for people to jump in. So let's talk a little bit about
what I think would be some of the negative consequences, which
I do think there will be job losses.
There's going to be job losses more in some industries than
(24:50):
others. I think consulting is going to
go through some changes. I think we're already seeing it.
I think a lot of consulting companies are going through
layoffs and things like that. So there's certain types of
skill set that it's going to automate, especially if you talk
about a gentic, right? The gentic when your automation
of process flows and decision making, you're outsourcing that
(25:12):
to a logic or a algorithm. You're not going to need a
person to do that. You know, at a desk, it's just
you don't. So therefore you will lose that
job. You may not want to lose that
person. So there's a difference between
a job loss and losing a person, you know, And because I truly
(25:32):
believe that as we solve for problems, there are always other
problems that pop up. You know, as, as we've been
through the, the revolution of the Internet, the evolution of
the Internet, there's some issues that the Internet caused
that we never even could think about.
So using our sort of paradigm ofwhat's right and wrong and
(25:53):
applying it to AI, that's not really correct because you don't
know where it's going to go. So we don't know what other
issues it's going to create, what opportunities it's going to
create. And we also don't know what
other problems we can actually solve for as AI gets better.
So we only know what we can solve for now given what our
(26:13):
human, human sort of logic allows us to think through.
But I think in the future we'll be able to solve for problems
that we can't even think of right now because of AI.
So I think that's the positive side.
So if anybody feels like their job is threatened or their role
is threatened, what have you, I think there's that thought
process that is like that I tellmy 23 and 21 year old, which is
(26:35):
think about things that we aren't thinking about, right?
That's, that's the key is how doyou solve a problem that we're
not even thinking about? Because now you have the ability
to do so at a much faster rate. You have the ability to create
companies at a faster rate and solve these problems.
So the good side is really quitehigh.
So the negative side is, yes, there will be job loss, but on
the other side, there will be huge amounts of growth because
(26:57):
we're going to be able to solve problems that we just don't know
of yet. So that's the positive side of
things. And the other thing that, you
know, I think is important that we just talked about, which is
the human ability to make changeis still a problem.
That's still the limiting factorhere.
So I don't care how many processes you, you know, put in
front. I don't care what you do at the
(27:17):
end of the day. You know, somebody's got to buy
that good and somebody's going to have to pay for that.
You know, it has to happen in a certain way.
You're going to have to consume in some way, shape or form the
laws of that. That role is not going to change
those roles and those fundamental facts.
So I think we can be safe to assume that, you know, we can
(27:37):
automate up to a certain point and after that certain point,
you still need humans to be ableto take it that last bit of of
that transaction. And that's the piece that I
think there's going to be a lot of value in it for organizations
as we move forward. Understood.
So there, there will be job shifts, jobs that we think of
(27:59):
that exist now where your jobs to do AB and C that may go away.
Hopefully the people will not goaway and the new job will be
something that shifts to DENF orXY and Z or something like that.
So can certainly understand about the job shifts.
But of course, that's something that happened, has happened all
the time in different ways. With every new technological
(28:22):
mini step we see that that happening.
It is, by the way, it is an incredible opportunities for
young professionals. So if you're a young
professional coming out, you know, now I guess I'm a
dinosaur, right? So if you, if you understand and
know and you know how to apply this to make, you know, create
value and make changes to organization, you might be able
(28:45):
to leapfrog these dinosaurs likemyself and get on top, right?
Because you have that capability, you know how to use
it at a faster rate, better way,what have you.
So it creates a unique opportunity for young
professionals coming in to have a skill set, understand and have
the know how to be able to leapfrog some of the dinosaurs
(29:05):
that don't want to change. Understood.
Yeah. And that's one of those great
opportunities that you talked about that will come around.
But obviously, we will see that people who understand and who
are good at using these tools certainly will have a big leg
up. We'll be able to make their own
path and a lot of organizations and we'll have these great
(29:27):
opportunities for their future to come there.
So would you say that that is one of the things that we're
going to see as pricing teams develop?
Is it going to be kind of this bionic model as you talk about
where the people who become, whobecome most attractive for these
(29:48):
types of jobs will be the peoplewho can take that human
intellect, that need for change management, be able to use the
tools in the best way and move forward that way?
Is that an example of your bionic team there?
That's right. That's right.
I think, I think what we'll see in the short term is a big shift
to everybody working on AI or doing AI something.
It's gonna get very technical and then people are gonna
(30:11):
realize, shoot, we've gotten tootechnical and we're not driving
change in the organization. Then it's gonna, the pendulum is
gonna start swinging back to theother side and it's gonna
probably land on exactly what you said, which is, look, we
need to understand the technicalcomponents of it.
We need to get to insights faster.
But we need that business acumen, that person that knows
(30:32):
how to influence and drive change through the organization
to actually make this change or,you know, price increase or what
have you actually stick within our four walls that that's still
a skill set that's going to be necessary.
So we kind of loop back to pricing people being both the
artists and the scientists. That's right, it's going.
To it's going to be a full circle, you know, and, and it's
(30:54):
and I, I just don't think, you know, honestly.
And maybe I'm a pessimist, Kevin, and, and we've talked
about this before, which is I don't see the world of I've
already made my decision now make the math work so my
decision feels more right. I don't I don't see that quite
changing so quickly. So yeah, you know, you're going
to see the AI output and some executive was going to go.
(31:16):
Can you rerun that a little bit different?
I wanna see it from this angle because it's not picking up on
something. Yes, yeah.
The example I remember from, I think it was a statistics
textbook that I had, we'll say, a few decades ago.
It says that sometimes we use new tools and statistics the way
that a drunk person uses a lightpole.
(31:39):
You don't use it for illumination, you use it for
support. You're looking to support your
decision instead of find out something new and to to get
illuminated there. So yeah, hopefully we'll use the
tools in the right way for illumination and not support of
something that we already. Decided that bias is tough to
get get past sometimes as humans.
(31:59):
That is very tough. Human beings are still going to
be human beings in, in that way.And of of course, hey, that's
why change management is so critical to to all of this.
All right, so G, we are very, very much looking forward to
seeing you at PPS profitable in Las Vegas, hearing more about
how to build a bionic pricing team.
(32:21):
And of course, I would, I highlyrecommend that everyone pick up
pricing with confidence if you have not done so already.
And you can connect with Jeet, I'm sure on LinkedIn or you can
look for Holden Advisors and youwill find him there.
For information from the PPS team and me.
Of course, you can check pricingsociety.com about our
(32:44):
online training, about our upcoming events, about our
membership service certificationand much, much more.
But please reach out to me to have any, any questions, connect
with Jeet, of course, if you have any questions or if you
want more insights about what we've been, what we've been
talking about today. But I want to thank everyone for
tuning in to this edition of theLet's Talk Pricing podcast.
(33:08):
And Jeet, thank you so much. Always good to see you.
Looking forward to seeing you again as well, Sir.
Thank you very much. Really appreciate it.
All right. So thank you again to Mr. Jeet
Mukherjee, thanks to the PPS team, and we will hope to see
you at another edition of the Let's Talk Pricing podcast
coming soon.
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