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Well hello and thank you again 
for tuning into another episode 

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of the Professional Pricing 
Society Podcast. 

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My name is Terence and we have 
an amazing duo with us today To 

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tackle the topic of AI making 
our lives a lot easier. 

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We have Brooks Hamilton who is 
the founder of Hamilton AI 

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Strategy Advisors and Austin 
based consultancy specializing 

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in crafting AI strategies for 
Fortune Global 1000 companies, 

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family owned businesses and high
growth startups. 

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We also have Miss Lydia D 
Liello, CEO and Founder of 

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Capital Pricing Consultants and 
a member of the Professional 

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Pricing Society Board of 
Advisors. 

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She is a well known and widely 
respected speaker leading 

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executive forums, conferences 
and workshops worldwide and she 

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has published frequently in 
trade and professional journals.

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How are we doing today? 
Really well, thanks. 

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Tara doing good. 
Thank you all so much for being 

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a part of the Professional 
Pricing Society podcast and we 

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have a pretty important 
conference coming up the last 

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week or the last couple of weeks
of April. 

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And then you two are going to be
conducting an amazing speaking 

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session tackling the topic of 
stopping the quote Madness used 

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AI to make life easier. 
Now I want to just kind of jump 

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into this conversation and just 
go ahead and introduce the first

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question. 
You know, when thinking about AI

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and thinking about making 
allowing AI to, you know, make 

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our lives a lot more easier and 
more convenient, having things 

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completed a lot quicker, you 
know, what will participants get

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out of your session that they 
will be able to apply from this 

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speaking session? 
Yeah, that's a a great question,

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Terence. 
I I think this part of this 

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comes from how we got the idea 
to do this. 

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Lydia and I have both seen how 
organizations have applied the 

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last round of AI and machine 
learning to improve 

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profitability. 
But we, as we know from the 

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release of ChatGPT, we we saw 
that there was a lot of interest

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among pricing professionals and 
there is also a lot of potential

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for AI to revolutionize the 
quoting process. 

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But there's a huge gap in 
between practical knowledge, 

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concrete examples and the theory
of how it might happen. 

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So what we wanted to do was try 
to bridge that theory and 

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practice in order to have that 
team and the participants in our

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workshop be able to take 
something back the next week. 

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So that's part of where it came 
from. 

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And you know in if we think 
about how our participants will 

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will use this is we wanted to 
show a few examples so they 

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would have an idea of how they 
can go about uses, how our other

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organizations and roles using 
this technology. 

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But we also want to show them 
how they can fish. 

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So they should be able to go 
back and breakdown their process

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and then see where they can 
apply AI, where they might be 

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able to apply it and where they 
probably shouldn't. 

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And then Lydia has a a wealth of
knowledge and experience on 

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pricing and negotiation and 
structure. 

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And so we're going to kind of 
combine that structure with a 

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step by step guide on how to 
implement those AI tools. 

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So Sarah, as part of of what we,
we really want to do as as 

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Brooks was saying is we want 
something actionable that people

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can go home with the next week 
and say great, I've got a 

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request for proposal on RFP or a
quote that I've got to get out 

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the door, where do I start with 
this? 

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And so we're actually going to 
to have it be a very active 

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workshop so that they are 
defining what they do in a 

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quote. 
Currently we've got a list of of

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10 things that take place in a 
quote, generally speaking, a lot

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of which make all of us who have
been in pricing any amount of 

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time wanna rip our hair out 
because it's redundant work, 

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It's hugely time consuming. 
You feel like you're married to 

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excel, right? 
And then you get 6 different 

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disparate answers that then you 
go to the boss and the boss 

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wants you to run scenarios. 
So you've got many, many steps 

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within the the quote itself as 
well as then the approval 

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processes. 
And so we want to show our 

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participants how at every step 
of the way they can either as 

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Brooke said, apply AI to get the
the task that's difficult done 

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very quickly And and they can 
have the output of it and then 

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focus on that for their 
analytics versus spending all 

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that time number crunching. 
And then there'll be processes 

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where they need to leave it 
alone because it's analytics and

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decision making and thought 
processes that they need to be 

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involved in. 
And then there's other places 

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where they may be able to 
implement a little bit of it, 

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and that's what we really want 
to take them through, so that 

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when they go home, they apply it
immediately to their own quotes.

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That's awesome. 
Yeah, that's that's awesome. 

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The attendees in this particular
sessions, you know, are really 

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gonna receive a lot of insight 
from YouTube, especially with 

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your expertise and background 
regarding AI. 

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They're going to receive a lot 
of insight. 

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So it's going to be really good.
It's going to be a very 

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intriguing, very popular 
workshop. 

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Let me ask you this, what do you
see in terms of business 

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adoption of AI technologies and 
and products nowadays? 

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Yeah, I I I think we've all seen
just how much buzz there is and 

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what I would actually say on 
that is the buzz hasn't even 

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really started yet. 
So we're we're not heading into 

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a a a closed down cycle instead 
there's there's a ton of 

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interest and we know that early 
adopters are certainly going to 

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have a competitive advantage but
the areas in which we see it 

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used are really those where it 
requires some knowledge and 

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expertise in in the realm of 
that business. 

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So as as an example, when I'm 
thinking about what products can

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I go about offering that are 
complimentary, well, if I'm a a 

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great insider and if I have a 
lot of experience in in that 

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industry, I know exactly which 
products to go about offering. 

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But instead we've seen AI used 
for translating e-mail order 

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requests into an order entry 
system, identifying where the 

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gaps are, suggesting what 
alternative products are. 

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All of this to the sales Rep, So
that way the Rep can have a 

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better more informed decision 
when they go back to work with 

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their prospect. 
Other industries where we see a 

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really tremendous amount of 
movement is financial services. 

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There have been, you know, 
significant moves by JP Morgan 

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and Schwab to invest in these 
technologies. 

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We see it in marketing and in 
legal and I think everybody's 

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going to to hear about this in 
terms of the software side. 

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So really just a a a tremendous 
amount of startups popping up, 

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new use cases being addressed, 
and businesses piloting these 

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capabilities. 
I like what you said, Brooks. 

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When you said it, it really 
hasn't even gotten off the 

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ground yet. 
You know, people are talking 

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about it. 
You know, AI is a buzzword now, 

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but even though it's been here 
for a little while, it really 

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hasn't gotten lifted off the 
ground to it even. 

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It's half half of its potential 
yet, so this should be a very 

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interesting next few years I 
should say. 

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Let me ask you all this as well,
what task and you kind of 

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alluded to a little bit before, 
but what tasks are best suited 

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for AI adoption if you could 
just kind of specify that? 

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Terence, when we look at what's 
great, a great fit for for AI 

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adoption, we're looking at 
things that are highly 

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repetitive that are what what we
would all say is really 

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annoying, hugely time consuming.
So whether you're looking at 

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matching up high volume parts or
you're looking at matching 

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competitive parts as part of a a
request for proposal. 

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If you're looking for what was 
the last price that my customer 

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paid for this set of high volume
parts and then you want to do a 

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comparison with that and the 
competitive price points that 

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are out there. 
Anywhere that you have large 

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sets of data that you are 
performing a A repetitive 

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function against, it's a prime 
opportunity to use AI because 

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what what the pricer is 
interested in is the output of 

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that, right. 
What we need to make decisions 

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is the output of that, of that 
data crunching. 

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And so those are some places 
where you can really get value 

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and significantly decrease your 
time. 

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Another place is in things like 
once you get the RFP put 

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together, every pricer that 
listens to this podcast and 

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comes to our workshop is gonna 
know what it's like to sit in 

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front of the boss to get 
permission to send the quote out

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the door and invariably the CEO 
or the VP or whoever is gonna 

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say yes. 
But what if the volume was X 

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instead of Y on these ten part 
numbers? 

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Well now what that means to the 
person sitting there is I gotta 

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go back and spend 4 hours 
crunching this number to get an 

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answer. 
No you don't. 

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You feed it into the AI tool, 
let it crunch for 10 minutes. 

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You got an answer. 
Now you make a decision as the 

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human and go back to your boss 
with the proposed 

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recommendation. 
So what if scenarios, the change

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in volume scenarios, the change,
the price point scenarios, all 

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of which our senior executives 
constantly ask for, no longer 

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becomes a three day ordeal. 
It becomes 15 minutes of 

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inputting the variables, hit the
button and and let the AI tool 

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crunch that. 
So those are really strong 

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places, not only the the data 
sets themselves, but also when 

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you get into the what ifs. 
I I think it also kind of makes 

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sense to talk about where AI is 
not appropriate. 

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Good point. 
You know AI is great at figuring

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out repetitive tasks and helping
us with it. 

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But we need to be the ones who 
are making the value judgments 

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and evaluating what we're going 
to send to our customers and how

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it fits into the larger picture 
of our go to market strategy as 

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well as the immediate market 
pressures that we may be dealing

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with and as well as corporate 
objectives. 

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So the objective is how do we go
about taking the lower value but

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crucial items such as moving the
template from the RFRFP template

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information to our internal 
analysis template back to the 

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customer's RFP template which 
just everybody does not enjoy. 

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Instead focus on questions like 
where are the right substitute 

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products, Where can I go after 
margin, how do I go about making

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trade off decisions and how does
this fit into the bigger 

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picture. 
Those are things which you know,

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we we should focus on and also 
highlight, not just for the 

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preparation of a quote, but also
for the skills we need to 

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continue to develop in our 
careers as we navigate the 

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professional landscape with AI 
in IT. 

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That's good and I'm, I'm 
assuming you all will highlight 

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those in more depth in your in 
your workshop, but that's good 

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to know what its, what its use 
is primarily for and what it's 

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not for and you know inputting 
data to get a certain result. 

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But also from a human standpoint
understanding how to judge the 

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value of that output. 
That's good that you two were 

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able to kind of separate that to
outline which which you know 

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which matters and which kind of 
doesn't when it comes to AI 

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expectations. 
AI obviously this is a system, 

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if you will, that can save us a 
lot of time and be very 

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convenient. 
How much time saving can we 

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expect regarding the quoting 
process specifically? 

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Terrence, we've seen numbers 
anywhere between 30 and 70% 

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reduction in overall time 
invested and really that's 

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that's what we want the 
participants to be interactive 

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in, in this session so that they
can learn it. 

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It doesn't have to be a mind 
blowing, never ending process to

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create a quote right. 
And and I think that the natural

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tendency is everybody gets all 
excited when a big quote comes 

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in the door and then two seconds
after everybody goes, Oh no, we 

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gotta start crunching. 
Well, no you don't. 

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And when you can save between 30
and 70% especially on the tasks 

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that are not fun. 
And and Brooks had brought up a 

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point when we were talking 
earlier that that's so critical 

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when we interview for jobs, 
right. 

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There's pieces of our job we 
love and and that's the the 

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strategy and the decision making
and how I can help my customer 

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and what I can do different. 
Except that none of that applies

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when you're buried under excel 
for six days, right. 

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So what matters is getting back 
to those things you loved about 

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the job to be good and what and 
making sure you can do them 

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again. 
And with a is help you can 

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because now 70% of your workload
is not spent keying things into 

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Excel to get it output that you 
can make a decision on. 

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So really dramatic time savings.
And that's why we want this 

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workshop to be so interactive, 
because we want participants to 

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really feel very comfortable the
next week they go home just say,

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hey, I know exactly what part of
this I can use AI for. 

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And and Brooks is going to spend
some time talking about some 

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specific tools so that folks get
an idea of what might be 

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appropriate for what kinds of 
data sets as well, so that they 

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can get some education around 
that also. 

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So really we want them to walk 
away totally comfortable with 

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what they can go do next to save
that 70%. 30 to 70% is a lot of 

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time and that is a lot of 
opportunity to be productive 

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elsewhere. 
Exactly. 

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Terry And so if you can fast 
track something like Excel 

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spreadsheets, you know and and 
focus on a different facet of 

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whatever the project is, you 
know there's a lot of room for 

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growth, a lot of room for quick 
growth as well regarding the 

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usage of AI especially in the in
the amazing world of pricing and

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so that's awesome. 
When When we began talking to 

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businesses and the the course of
our firm's work, one of the 

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items that came up was the bid 
process. 

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And what we heard repeatedly was
that not all bids were responded

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to. 
Not all bids are responded to in

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the timeline that the client 
requested because many of them 

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were coming in at the same time.
For those that did get out the 

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door, some of them were really 
well analyzed and thought 

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through and responded to and 
others were just needed to get 

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out the door. 
And what we had heard from prior

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work was if if you you know in 
order to win, you first need to 

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submit a bid and if our clients 
can just submit every bid that 

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they had received a request for,
they'd probably have a higher 

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revenue rate. 
Similarly, they would be able to

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be more profitable had they been
able to get eyes on all areas of

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that quote and really think it 
through as they were responding 

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to it. 
But just because so much of the 

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quote time, typically 85 to 90%,
sorry, 85 to 95% of the time 

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working on a quote is mechanical
blocking and tackling, moving 

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things from one spreadsheet or 
one data source to another 

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rather than thinking through how
all of this happens. 

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So the overall idea is speed, 
the blocking and tackling part, 

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move that to whatever tool you 
need in order to make that go 

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fast. 
And then see, just by completing

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the tasks faster and more 
quickly and with higher quality,

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how is that going to grow your 
top line and your bottom line at

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the same time as having an 
employee base that is a little 

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more satisfied with the work 
that they're doing day-to-day? 

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You kind of also kind of alluded
to my last question I was going 

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to ask about industry 
profitability, but you're 

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absolutely right. 
I mean this, this is speeding up

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the the, the blocking process. 
Is that how you mentioned? 

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Yeah, speeding up that process 
is going to open up a lot of 

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room for us to tackle, you know,
other more important tasks and 

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I'm glad you worded it in the 
way you did. 

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I wanted to ask you one last 
question as well. 

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We kind of alluded to it 
throughout the conversation 

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overall, but a lot of people 
think that AI is going to take 

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over the world and take over all
these people's jobs or whatever.

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But also a lot of people saying 
people are not viewing AI as a 

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tool to increase profitability, 
industry profitability. 

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So what what do you see as the 
overall impact of AI adoption on

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industry profitability? 
Yeah. 

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As we see these AI tools come 
out and be able to perform 

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certain tasks, different 
organizations will respond in 

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ways that best reflect their 
culture and their financial 

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circumstances. 
I'd really encourage 

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organizations to think about 
this from their customers 

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perspective. 
So as a customer, do I really 

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want something that is done just
as well as it is today, which 

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might not be ideal? 
Or do I want a job done really 

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well and really quickly and in a
high quality manner? 

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And then can I as a organization
go do that for many, many more 

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customers? 
So for example, some of the work

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that was done or changes made 
recently at Schwab that they 

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have published are they're going
on a hiring spree of salespeople

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and customer service people 
because they were able to cut 

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down on some of their repetitive
back office work, which freed up

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a lot of resource to be not less
human centric, but actually far 

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more human centric when working 
with their customers. 

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That's cool, yeah, little things
like that. 

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Little little changes like that 
can make a word of a difference 

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for the customer and for the 
company at large. 

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So stop according to madness. 
Use AI to Make life Easier is 

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00:21:00,720 --> 00:21:06,040
the workshop that will be a very
popular workshop and topic of 

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00:21:06,040 --> 00:21:11,000
discussion coming up during our 
Spring Conference, April 23rd 

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00:21:11,000 --> 00:21:17,560
through the 26th in Chicago, IL.
Before I let you both go, I do 

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have one more question for those
who are interested in attending 

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00:21:21,120 --> 00:21:24,520
who may be listening right now. 
Where can they go to learn more 

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00:21:24,520 --> 00:21:27,960
about Brooks Hamilton and Lydia 
D Lillo? 

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And you know, the company that 
they're with, what they stand 

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00:21:31,320 --> 00:21:32,920
for? 
Where can they go to learn more 

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00:21:32,920 --> 00:21:35,160
about that? 
All right. 

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So for Lydia, they can go to 
Capital Pricing Consultants with

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00:21:39,680 --> 00:21:44,480
an s.com and they certainly can 
also go to the Professional 

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00:21:44,480 --> 00:21:49,440
Pricing Society workshops and 
Brooks for. 

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00:21:49,720 --> 00:21:57,200
Strategy Day on LinkedIn as well
as Strategy advisors dot AI. 

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All right. 
Thank you both so much again for

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00:22:01,760 --> 00:22:05,040
your time, your discussion. 
This podcast is serving as a 

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00:22:05,040 --> 00:22:08,200
teaser for the workshop that's 
going to be happening this 

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00:22:08,200 --> 00:22:09,840
spring. 
To learn more about that you can

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00:22:09,840 --> 00:22:13,520
visit pricingsociety.com and 
visit the Conferences tab Until 

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00:22:13,520 --> 00:22:15,280
next time you guys have a good 
one. 

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00:22:15,360 --> 00:22:15,680
Bye, bye.