April 11, 2024 • 22 mins
We are inundated with news of Artificial Intelligence, but how can it really be applied in our daily Pricing lives to help us?
This highly interactive workshop addresses how to make AI work for you by providing a practical, step-by-step approach to respond to request for proposals or quotes (RFP, RFQ).
Many data components can be quickly executed by AI and can be rerun nearly instantaneously to address changes
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Well hello and thank you again for tuning into another episode
of the Professional Pricing Society Podcast.
My name is Terence and we have an amazing duo with us today To
tackle the topic of AI making our lives a lot easier.
We have Brooks Hamilton who is the founder of Hamilton AI
Strategy Advisors and Austin based consultancy specializing
(00:22):
in crafting AI strategies for Fortune Global 1000 companies,
family owned businesses and highgrowth startups.
We also have Miss Lydia D Liello, CEO and Founder of
Capital Pricing Consultants and a member of the Professional
Pricing Society Board of Advisors.
She is a well known and widely respected speaker leading
executive forums, conferences and workshops worldwide and she
(00:44):
has published frequently in trade and professional journals.
How are we doing today? Really well, thanks.
Tara doing good. Thank you all so much for being
a part of the Professional Pricing Society podcast and we
have a pretty important conference coming up the last
week or the last couple of weeksof April.
And then you two are going to beconducting an amazing speaking
(01:08):
session tackling the topic of stopping the quote Madness used
AI to make life easier. Now I want to just kind of jump
into this conversation and just go ahead and introduce the first
question. You know, when thinking about AI
and thinking about making allowing AI to, you know, make
our lives a lot more easier and more convenient, having things
(01:32):
completed a lot quicker, you know, what will participants get
out of your session that they will be able to apply from this
speaking session? Yeah, that's a a great question,
Terence. I I think this part of this
comes from how we got the idea to do this.
Lydia and I have both seen how organizations have applied the
(01:56):
last round of AI and machine learning to improve
profitability. But we, as we know from the
release of ChatGPT, we we saw that there was a lot of interest
among pricing professionals and there is also a lot of potential
for AI to revolutionize the quoting process.
(02:17):
But there's a huge gap in between practical knowledge,
concrete examples and the theoryof how it might happen.
So what we wanted to do was try to bridge that theory and
practice in order to have that team and the participants in our
(02:39):
workshop be able to take something back the next week.
So that's part of where it came from.
And you know in if we think about how our participants will
will use this is we wanted to show a few examples so they
would have an idea of how they can go about uses, how our other
(03:01):
organizations and roles using this technology.
But we also want to show them how they can fish.
So they should be able to go back and breakdown their process
and then see where they can apply AI, where they might be
(03:23):
able to apply it and where they probably shouldn't.
And then Lydia has a a wealth ofknowledge and experience on
pricing and negotiation and structure.
And so we're going to kind of combine that structure with a
(03:45):
step by step guide on how to implement those AI tools.
So Sarah, as part of of what we,we really want to do as as
Brooks was saying is we want something actionable that people
can go home with the next week and say great, I've got a
request for proposal on RFP or aquote that I've got to get out
(04:05):
the door, where do I start with this?
And so we're actually going to to have it be a very active
workshop so that they are defining what they do in a
quote. Currently we've got a list of of
10 things that take place in a quote, generally speaking, a lot
of which make all of us who havebeen in pricing any amount of
time wanna rip our hair out because it's redundant work,
(04:28):
It's hugely time consuming. You feel like you're married to
excel, right? And then you get 6 different
disparate answers that then you go to the boss and the boss
wants you to run scenarios. So you've got many, many steps
within the the quote itself as well as then the approval
processes. And so we want to show our
(04:49):
participants how at every step of the way they can either as
Brooke said, apply AI to get thethe task that's difficult done
very quickly And and they can have the output of it and then
focus on that for their analytics versus spending all
that time number crunching. And then there'll be processes
(05:10):
where they need to leave it alone because it's analytics and
decision making and thought processes that they need to be
involved in. And then there's other places
where they may be able to implement a little bit of it,
and that's what we really want to take them through, so that
when they go home, they apply itimmediately to their own quotes.
That's awesome. Yeah, that's that's awesome.
The attendees in this particularsessions, you know, are really
(05:34):
gonna receive a lot of insight from YouTube, especially with
your expertise and background regarding AI.
They're going to receive a lot of insight.
So it's going to be really good.It's going to be a very
intriguing, very popular workshop.
Let me ask you this, what do yousee in terms of business
adoption of AI technologies and and products nowadays?
(05:56):
Yeah, I I I think we've all seenjust how much buzz there is and
what I would actually say on that is the buzz hasn't even
really started yet. So we're we're not heading into
a a a closed down cycle instead there's there's a ton of
interest and we know that early adopters are certainly going to
(06:22):
have a competitive advantage butthe areas in which we see it
used are really those where it requires some knowledge and
expertise in in the realm of that business.
So as as an example, when I'm thinking about what products can
(06:45):
I go about offering that are complimentary, well, if I'm a a
great insider and if I have a lot of experience in in that
industry, I know exactly which products to go about offering.
But instead we've seen AI used for translating e-mail order
requests into an order entry system, identifying where the
(07:10):
gaps are, suggesting what alternative products are.
All of this to the sales Rep, Sothat way the Rep can have a
better more informed decision when they go back to work with
their prospect. Other industries where we see a
really tremendous amount of movement is financial services.
(07:33):
There have been, you know, significant moves by JP Morgan
and Schwab to invest in these technologies.
We see it in marketing and in legal and I think everybody's
going to to hear about this in terms of the software side.
So really just a a a tremendous amount of startups popping up,
(07:56):
new use cases being addressed, and businesses piloting these
capabilities. I like what you said, Brooks.
When you said it, it really hasn't even gotten off the
ground yet. You know, people are talking
about it. You know, AI is a buzzword now,
but even though it's been here for a little while, it really
hasn't gotten lifted off the ground to it even.
(08:16):
It's half half of its potential yet, so this should be a very
interesting next few years I should say.
Let me ask you all this as well,what task and you kind of
alluded to a little bit before, but what tasks are best suited
for AI adoption if you could just kind of specify that?
Terence, when we look at what's great, a great fit for for AI
(08:37):
adoption, we're looking at things that are highly
repetitive that are what what wewould all say is really
annoying, hugely time consuming.So whether you're looking at
matching up high volume parts oryou're looking at matching
competitive parts as part of a arequest for proposal.
If you're looking for what was the last price that my customer
(08:59):
paid for this set of high volumeparts and then you want to do a
comparison with that and the competitive price points that
are out there. Anywhere that you have large
sets of data that you are performing a A repetitive
function against, it's a prime opportunity to use AI because
(09:21):
what what the pricer is interested in is the output of
that, right. What we need to make decisions
is the output of that, of that data crunching.
And so those are some places where you can really get value
and significantly decrease your time.
Another place is in things like once you get the RFP put
(09:44):
together, every pricer that listens to this podcast and
comes to our workshop is gonna know what it's like to sit in
front of the boss to get permission to send the quote out
the door and invariably the CEO or the VP or whoever is gonna
say yes. But what if the volume was X
instead of Y on these ten part numbers?
Well now what that means to the person sitting there is I gotta
(10:06):
go back and spend 4 hours crunching this number to get an
answer. No you don't.
You feed it into the AI tool, let it crunch for 10 minutes.
You got an answer. Now you make a decision as the
human and go back to your boss with the proposed
recommendation. So what if scenarios, the change
in volume scenarios, the change,the price point scenarios, all
(10:29):
of which our senior executives constantly ask for, no longer
becomes a three day ordeal. It becomes 15 minutes of
inputting the variables, hit thebutton and and let the AI tool
crunch that. So those are really strong
places, not only the the data sets themselves, but also when
you get into the what ifs. I I think it also kind of makes
(10:52):
sense to talk about where AI is not appropriate.
Good point. You know AI is great at figuring
out repetitive tasks and helpingus with it.
But we need to be the ones who are making the value judgments
and evaluating what we're going to send to our customers and how
(11:15):
it fits into the larger picture of our go to market strategy as
well as the immediate market pressures that we may be dealing
with and as well as corporate objectives.
So the objective is how do we goabout taking the lower value but
crucial items such as moving thetemplate from the RFRFP template
(11:40):
information to our internal analysis template back to the
customer's RFP template which just everybody does not enjoy.
Instead focus on questions like where are the right substitute
products, Where can I go after margin, how do I go about making
(12:01):
trade off decisions and how doesthis fit into the bigger
picture. Those are things which you know,
we we should focus on and also highlight, not just for the
preparation of a quote, but alsofor the skills we need to
continue to develop in our careers as we navigate the
(12:24):
professional landscape with AI in IT.
That's good and I'm, I'm assuming you all will highlight
those in more depth in your in your workshop, but that's good
to know what its, what its use is primarily for and what it's
not for and you know inputting data to get a certain result.
(12:47):
But also from a human standpointunderstanding how to judge the
value of that output. That's good that you two were
able to kind of separate that tooutline which which you know
which matters and which kind of doesn't when it comes to AI
expectations. AI obviously this is a system,
(13:07):
if you will, that can save us a lot of time and be very
convenient. How much time saving can we
expect regarding the quoting process specifically?
Terrence, we've seen numbers anywhere between 30 and 70%
reduction in overall time invested and really that's
(13:27):
that's what we want the participants to be interactive
in, in this session so that theycan learn it.
It doesn't have to be a mind blowing, never ending process to
create a quote right. And and I think that the natural
tendency is everybody gets all excited when a big quote comes
in the door and then two secondsafter everybody goes, Oh no, we
(13:50):
gotta start crunching. Well, no you don't.
And when you can save between 30and 70% especially on the tasks
that are not fun. And and Brooks had brought up a
point when we were talking earlier that that's so critical
when we interview for jobs, right.
There's pieces of our job we love and and that's the the
(14:11):
strategy and the decision makingand how I can help my customer
and what I can do different. Except that none of that applies
when you're buried under excel for six days, right.
So what matters is getting back to those things you loved about
the job to be good and what and making sure you can do them
again. And with a is help you can
(14:32):
because now 70% of your workloadis not spent keying things into
Excel to get it output that you can make a decision on.
So really dramatic time savings.And that's why we want this
workshop to be so interactive, because we want participants to
really feel very comfortable thenext week they go home just say,
(14:54):
hey, I know exactly what part ofthis I can use AI for.
And and Brooks is going to spendsome time talking about some
specific tools so that folks getan idea of what might be
appropriate for what kinds of data sets as well, so that they
can get some education around that also.
So really we want them to walk away totally comfortable with
(15:16):
what they can go do next to savethat 70%. 30 to 70% is a lot of
time and that is a lot of opportunity to be productive
elsewhere. Exactly.
Terry And so if you can fast track something like Excel
spreadsheets, you know and and focus on a different facet of
(15:38):
whatever the project is, you know there's a lot of room for
growth, a lot of room for quick growth as well regarding the
usage of AI especially in the inthe amazing world of pricing and
so that's awesome. When When we began talking to
businesses and the the course ofour firm's work, one of the
(16:02):
items that came up was the bid process.
And what we heard repeatedly wasthat not all bids were responded
to. Not all bids are responded to in
the timeline that the client requested because many of them
were coming in at the same time.For those that did get out the
(16:26):
door, some of them were really well analyzed and thought
through and responded to and others were just needed to get
out the door. And what we had heard from prior
work was if if you you know in order to win, you first need to
(16:49):
submit a bid and if our clients can just submit every bid that
they had received a request for,they'd probably have a higher
revenue rate. Similarly, they would be able to
be more profitable had they beenable to get eyes on all areas of
(17:12):
that quote and really think it through as they were responding
to it. But just because so much of the
quote time, typically 85 to 90%,sorry, 85 to 95% of the time
working on a quote is mechanicalblocking and tackling, moving
things from one spreadsheet or one data source to another
(17:37):
rather than thinking through howall of this happens.
So the overall idea is speed, the blocking and tackling part,
move that to whatever tool you need in order to make that go
fast. And then see, just by completing
the tasks faster and more quickly and with higher quality,
(18:02):
how is that going to grow your top line and your bottom line at
the same time as having an employee base that is a little
more satisfied with the work that they're doing day-to-day?
You kind of also kind of alludedto my last question I was going
to ask about industry profitability, but you're
(18:23):
absolutely right. I mean this, this is speeding up
the the, the blocking process. Is that how you mentioned?
Yeah, speeding up that process is going to open up a lot of
room for us to tackle, you know,other more important tasks and
I'm glad you worded it in the way you did.
I wanted to ask you one last question as well.
We kind of alluded to it throughout the conversation
(18:44):
overall, but a lot of people think that AI is going to take
over the world and take over allthese people's jobs or whatever.
But also a lot of people saying people are not viewing AI as a
tool to increase profitability, industry profitability.
So what what do you see as the overall impact of AI adoption on
(19:04):
industry profitability? Yeah.
As we see these AI tools come out and be able to perform
certain tasks, different organizations will respond in
ways that best reflect their culture and their financial
(19:26):
circumstances. I'd really encourage
organizations to think about this from their customers
perspective. So as a customer, do I really
want something that is done justas well as it is today, which
(19:47):
might not be ideal? Or do I want a job done really
well and really quickly and in ahigh quality manner?
And then can I as a organizationgo do that for many, many more
(20:07):
customers? So for example, some of the work
that was done or changes made recently at Schwab that they
have published are they're goingon a hiring spree of salespeople
and customer service people because they were able to cut
(20:28):
down on some of their repetitiveback office work, which freed up
a lot of resource to be not lesshuman centric, but actually far
more human centric when working with their customers.
That's cool, yeah, little thingslike that.
(20:49):
Little little changes like that can make a word of a difference
for the customer and for the company at large.
So stop according to madness. Use AI to Make life Easier is
the workshop that will be a verypopular workshop and topic of
discussion coming up during our Spring Conference, April 23rd
(21:11):
through the 26th in Chicago, IL.Before I let you both go, I do
have one more question for thosewho are interested in attending
who may be listening right now. Where can they go to learn more
about Brooks Hamilton and Lydia D Lillo?
And you know, the company that they're with, what they stand
(21:31):
for? Where can they go to learn more
about that? All right.
So for Lydia, they can go to Capital Pricing Consultants with
an s.com and they certainly can also go to the Professional
Pricing Society workshops and Brooks for.
Strategy Day on LinkedIn as wellas Strategy advisors dot AI.
(21:59):
All right. Thank you both so much again for
your time, your discussion. This podcast is serving as a
teaser for the workshop that's going to be happening this
spring. To learn more about that you can
visit pricingsociety.com and visit the Conferences tab Until
next time you guys have a good one.
Bye, bye.
Well hello and thank you again for tuning into another episode
of the Professional Pricing Society Podcast.
My name is Terence and we have an amazing duo with us today To
tackle the topic of AI making our lives a lot easier.
We have Brooks Hamilton who is the founder of Hamilton AI
Strategy Advisors and Austin based consultancy specializing
(00:22):
in crafting AI strategies for Fortune Global 1000 companies,
family owned businesses and highgrowth startups.
We also have Miss Lydia D Liello, CEO and Founder of
Capital Pricing Consultants and a member of the Professional
Pricing Society Board of Advisors.
She is a well known and widely respected speaker leading
executive forums, conferences and workshops worldwide and she
(00:44):
has published frequently in trade and professional journals.
How are we doing today? Really well, thanks.
Tara doing good. Thank you all so much for being
a part of the Professional Pricing Society podcast and we
have a pretty important conference coming up the last
week or the last couple of weeksof April.
And then you two are going to beconducting an amazing speaking
(01:08):
session tackling the topic of stopping the quote Madness used
AI to make life easier. Now I want to just kind of jump
into this conversation and just go ahead and introduce the first
question. You know, when thinking about AI
and thinking about making allowing AI to, you know, make
our lives a lot more easier and more convenient, having things
(01:32):
completed a lot quicker, you know, what will participants get
out of your session that they will be able to apply from this
speaking session? Yeah, that's a a great question,
Terence. I I think this part of this
comes from how we got the idea to do this.
Lydia and I have both seen how organizations have applied the
(01:56):
last round of AI and machine learning to improve
profitability. But we, as we know from the
release of ChatGPT, we we saw that there was a lot of interest
among pricing professionals and there is also a lot of potential
for AI to revolutionize the quoting process.
(02:17):
But there's a huge gap in between practical knowledge,
concrete examples and the theoryof how it might happen.
So what we wanted to do was try to bridge that theory and
practice in order to have that team and the participants in our
(02:39):
workshop be able to take something back the next week.
So that's part of where it came from.
And you know in if we think about how our participants will
will use this is we wanted to show a few examples so they
would have an idea of how they can go about uses, how our other
(03:01):
organizations and roles using this technology.
But we also want to show them how they can fish.
So they should be able to go back and breakdown their process
and then see where they can apply AI, where they might be
(03:23):
able to apply it and where they probably shouldn't.
And then Lydia has a a wealth ofknowledge and experience on
pricing and negotiation and structure.
And so we're going to kind of combine that structure with a
(03:45):
step by step guide on how to implement those AI tools.
So Sarah, as part of of what we,we really want to do as as
Brooks was saying is we want something actionable that people
can go home with the next week and say great, I've got a
request for proposal on RFP or aquote that I've got to get out
(04:05):
the door, where do I start with this?
And so we're actually going to to have it be a very active
workshop so that they are defining what they do in a
quote. Currently we've got a list of of
10 things that take place in a quote, generally speaking, a lot
of which make all of us who havebeen in pricing any amount of
time wanna rip our hair out because it's redundant work,
(04:28):
It's hugely time consuming. You feel like you're married to
excel, right? And then you get 6 different
disparate answers that then you go to the boss and the boss
wants you to run scenarios. So you've got many, many steps
within the the quote itself as well as then the approval
processes. And so we want to show our
(04:49):
participants how at every step of the way they can either as
Brooke said, apply AI to get thethe task that's difficult done
very quickly And and they can have the output of it and then
focus on that for their analytics versus spending all
that time number crunching. And then there'll be processes
(05:10):
where they need to leave it alone because it's analytics and
decision making and thought processes that they need to be
involved in. And then there's other places
where they may be able to implement a little bit of it,
and that's what we really want to take them through, so that
when they go home, they apply itimmediately to their own quotes.
That's awesome. Yeah, that's that's awesome.
The attendees in this particularsessions, you know, are really
(05:34):
gonna receive a lot of insight from YouTube, especially with
your expertise and background regarding AI.
They're going to receive a lot of insight.
So it's going to be really good.It's going to be a very
intriguing, very popular workshop.
Let me ask you this, what do yousee in terms of business
adoption of AI technologies and and products nowadays?
(05:56):
Yeah, I I I think we've all seenjust how much buzz there is and
what I would actually say on that is the buzz hasn't even
really started yet. So we're we're not heading into
a a a closed down cycle instead there's there's a ton of
interest and we know that early adopters are certainly going to
(06:22):
have a competitive advantage butthe areas in which we see it
used are really those where it requires some knowledge and
expertise in in the realm of that business.
So as as an example, when I'm thinking about what products can
(06:45):
I go about offering that are complimentary, well, if I'm a a
great insider and if I have a lot of experience in in that
industry, I know exactly which products to go about offering.
But instead we've seen AI used for translating e-mail order
requests into an order entry system, identifying where the
(07:10):
gaps are, suggesting what alternative products are.
All of this to the sales Rep, Sothat way the Rep can have a
better more informed decision when they go back to work with
their prospect. Other industries where we see a
really tremendous amount of movement is financial services.
(07:33):
There have been, you know, significant moves by JP Morgan
and Schwab to invest in these technologies.
We see it in marketing and in legal and I think everybody's
going to to hear about this in terms of the software side.
So really just a a a tremendous amount of startups popping up,
(07:56):
new use cases being addressed, and businesses piloting these
capabilities. I like what you said, Brooks.
When you said it, it really hasn't even gotten off the
ground yet. You know, people are talking
about it. You know, AI is a buzzword now,
but even though it's been here for a little while, it really
hasn't gotten lifted off the ground to it even.
(08:16):
It's half half of its potential yet, so this should be a very
interesting next few years I should say.
Let me ask you all this as well,what task and you kind of
alluded to a little bit before, but what tasks are best suited
for AI adoption if you could just kind of specify that?
Terence, when we look at what's great, a great fit for for AI
(08:37):
adoption, we're looking at things that are highly
repetitive that are what what wewould all say is really
annoying, hugely time consuming.So whether you're looking at
matching up high volume parts oryou're looking at matching
competitive parts as part of a arequest for proposal.
If you're looking for what was the last price that my customer
(08:59):
paid for this set of high volumeparts and then you want to do a
comparison with that and the competitive price points that
are out there. Anywhere that you have large
sets of data that you are performing a A repetitive
function against, it's a prime opportunity to use AI because
(09:21):
what what the pricer is interested in is the output of
that, right. What we need to make decisions
is the output of that, of that data crunching.
And so those are some places where you can really get value
and significantly decrease your time.
Another place is in things like once you get the RFP put
(09:44):
together, every pricer that listens to this podcast and
comes to our workshop is gonna know what it's like to sit in
front of the boss to get permission to send the quote out
the door and invariably the CEO or the VP or whoever is gonna
say yes. But what if the volume was X
instead of Y on these ten part numbers?
Well now what that means to the person sitting there is I gotta
(10:06):
go back and spend 4 hours crunching this number to get an
answer. No you don't.
You feed it into the AI tool, let it crunch for 10 minutes.
You got an answer. Now you make a decision as the
human and go back to your boss with the proposed
recommendation. So what if scenarios, the change
in volume scenarios, the change,the price point scenarios, all
(10:29):
of which our senior executives constantly ask for, no longer
becomes a three day ordeal. It becomes 15 minutes of
inputting the variables, hit thebutton and and let the AI tool
crunch that. So those are really strong
places, not only the the data sets themselves, but also when
you get into the what ifs. I I think it also kind of makes
(10:52):
sense to talk about where AI is not appropriate.
Good point. You know AI is great at figuring
out repetitive tasks and helpingus with it.
But we need to be the ones who are making the value judgments
and evaluating what we're going to send to our customers and how
(11:15):
it fits into the larger picture of our go to market strategy as
well as the immediate market pressures that we may be dealing
with and as well as corporate objectives.
So the objective is how do we goabout taking the lower value but
crucial items such as moving thetemplate from the RFRFP template
(11:40):
information to our internal analysis template back to the
customer's RFP template which just everybody does not enjoy.
Instead focus on questions like where are the right substitute
products, Where can I go after margin, how do I go about making
(12:01):
trade off decisions and how doesthis fit into the bigger
picture. Those are things which you know,
we we should focus on and also highlight, not just for the
preparation of a quote, but alsofor the skills we need to
continue to develop in our careers as we navigate the
(12:24):
professional landscape with AI in IT.
That's good and I'm, I'm assuming you all will highlight
those in more depth in your in your workshop, but that's good
to know what its, what its use is primarily for and what it's
not for and you know inputting data to get a certain result.
(12:47):
But also from a human standpointunderstanding how to judge the
value of that output. That's good that you two were
able to kind of separate that tooutline which which you know
which matters and which kind of doesn't when it comes to AI
expectations. AI obviously this is a system,
(13:07):
if you will, that can save us a lot of time and be very
convenient. How much time saving can we
expect regarding the quoting process specifically?
Terrence, we've seen numbers anywhere between 30 and 70%
reduction in overall time invested and really that's
(13:27):
that's what we want the participants to be interactive
in, in this session so that theycan learn it.
It doesn't have to be a mind blowing, never ending process to
create a quote right. And and I think that the natural
tendency is everybody gets all excited when a big quote comes
in the door and then two secondsafter everybody goes, Oh no, we
(13:50):
gotta start crunching. Well, no you don't.
And when you can save between 30and 70% especially on the tasks
that are not fun. And and Brooks had brought up a
point when we were talking earlier that that's so critical
when we interview for jobs, right.
There's pieces of our job we love and and that's the the
(14:11):
strategy and the decision makingand how I can help my customer
and what I can do different. Except that none of that applies
when you're buried under excel for six days, right.
So what matters is getting back to those things you loved about
the job to be good and what and making sure you can do them
again. And with a is help you can
(14:32):
because now 70% of your workloadis not spent keying things into
Excel to get it output that you can make a decision on.
So really dramatic time savings.And that's why we want this
workshop to be so interactive, because we want participants to
really feel very comfortable thenext week they go home just say,
(14:54):
hey, I know exactly what part ofthis I can use AI for.
And and Brooks is going to spendsome time talking about some
specific tools so that folks getan idea of what might be
appropriate for what kinds of data sets as well, so that they
can get some education around that also.
So really we want them to walk away totally comfortable with
(15:16):
what they can go do next to savethat 70%. 30 to 70% is a lot of
time and that is a lot of opportunity to be productive
elsewhere. Exactly.
Terry And so if you can fast track something like Excel
spreadsheets, you know and and focus on a different facet of
(15:38):
whatever the project is, you know there's a lot of room for
growth, a lot of room for quick growth as well regarding the
usage of AI especially in the inthe amazing world of pricing and
so that's awesome. When When we began talking to
businesses and the the course ofour firm's work, one of the
(16:02):
items that came up was the bid process.
And what we heard repeatedly wasthat not all bids were responded
to. Not all bids are responded to in
the timeline that the client requested because many of them
were coming in at the same time.For those that did get out the
(16:26):
door, some of them were really well analyzed and thought
through and responded to and others were just needed to get
out the door. And what we had heard from prior
work was if if you you know in order to win, you first need to
(16:49):
submit a bid and if our clients can just submit every bid that
they had received a request for,they'd probably have a higher
revenue rate. Similarly, they would be able to
be more profitable had they beenable to get eyes on all areas of
(17:12):
that quote and really think it through as they were responding
to it. But just because so much of the
quote time, typically 85 to 90%,sorry, 85 to 95% of the time
working on a quote is mechanicalblocking and tackling, moving
things from one spreadsheet or one data source to another
(17:37):
rather than thinking through howall of this happens.
So the overall idea is speed, the blocking and tackling part,
move that to whatever tool you need in order to make that go
fast. And then see, just by completing
the tasks faster and more quickly and with higher quality,
(18:02):
how is that going to grow your top line and your bottom line at
the same time as having an employee base that is a little
more satisfied with the work that they're doing day-to-day?
You kind of also kind of alludedto my last question I was going
to ask about industry profitability, but you're
(18:23):
absolutely right. I mean this, this is speeding up
the the, the blocking process. Is that how you mentioned?
Yeah, speeding up that process is going to open up a lot of
room for us to tackle, you know,other more important tasks and
I'm glad you worded it in the way you did.
I wanted to ask you one last question as well.
We kind of alluded to it throughout the conversation
(18:44):
overall, but a lot of people think that AI is going to take
over the world and take over allthese people's jobs or whatever.
But also a lot of people saying people are not viewing AI as a
tool to increase profitability, industry profitability.
So what what do you see as the overall impact of AI adoption on
(19:04):
industry profitability? Yeah.
As we see these AI tools come out and be able to perform
certain tasks, different organizations will respond in
ways that best reflect their culture and their financial
(19:26):
circumstances. I'd really encourage
organizations to think about this from their customers
perspective. So as a customer, do I really
want something that is done justas well as it is today, which
(19:47):
might not be ideal? Or do I want a job done really
well and really quickly and in ahigh quality manner?
And then can I as a organizationgo do that for many, many more
(20:07):
customers? So for example, some of the work
that was done or changes made recently at Schwab that they
have published are they're goingon a hiring spree of salespeople
and customer service people because they were able to cut
(20:28):
down on some of their repetitiveback office work, which freed up
a lot of resource to be not lesshuman centric, but actually far
more human centric when working with their customers.
That's cool, yeah, little thingslike that.
(20:49):
Little little changes like that can make a word of a difference
for the customer and for the company at large.
So stop according to madness. Use AI to Make life Easier is
the workshop that will be a verypopular workshop and topic of
discussion coming up during our Spring Conference, April 23rd
(21:11):
through the 26th in Chicago, IL.Before I let you both go, I do
have one more question for thosewho are interested in attending
who may be listening right now. Where can they go to learn more
about Brooks Hamilton and Lydia D Lillo?
And you know, the company that they're with, what they stand
(21:31):
for? Where can they go to learn more
about that? All right.
So for Lydia, they can go to Capital Pricing Consultants with
an s.com and they certainly can also go to the Professional
Pricing Society workshops and Brooks for.
Strategy Day on LinkedIn as wellas Strategy advisors dot AI.
(21:59):
All right. Thank you both so much again for
your time, your discussion. This podcast is serving as a
teaser for the workshop that's going to be happening this
spring. To learn more about that you can
visit pricingsociety.com and visit the Conferences tab Until
next time you guys have a good one.
Bye, bye.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
New Heights with Jason & Travis Kelce
Football’s funniest family duo — Jason Kelce of the Philadelphia Eagles and Travis Kelce of the Kansas City Chiefs — team up to provide next-level access to life in the league as it unfolds. The two brothers and Super Bowl champions drop weekly insights about the weekly slate of games and share their INSIDE perspectives on trending NFL news and sports headlines. They also endlessly rag on each other as brothers do, chat the latest in pop culture and welcome some very popular and well-known friends to chat with them. Check out new episodes every Wednesday. Follow New Heights on the Wondery App, YouTube or wherever you get your podcasts. You can listen to new episodes early and ad-free, and get exclusive content on Wondery+. Join Wondery+ in the Wondery App, Apple Podcasts or Spotify. And join our new membership for a unique fan experience by going to the New Heights YouTube channel now!