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 in crafting AI strategies for Fortune Global 1000 companies, family owned businesses and high growth 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 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 weeks of April. And then you two are going to be conducting an amazing speaking 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 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 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. But there's a huge gap in between practical knowledge, concrete examples and the theory of 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 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 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 able to apply it and where they probably shouldn't. And then Lydia has a a wealth of knowledge and experience on pricing and negotiation and structure. And so we're going to kind of combine that structure with a 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 a quote that I've got to get out 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 have been in pricing any amount of time wanna rip our hair out because it's redundant work, 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 participants how at every step of the way they can either as Brooke said, apply AI to get the the 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 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 it immediately to their own quotes. That's awesome. Yeah, that's that's awesome.
The attendees in this particular sessions, you know, are really 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 you see in terms of business adoption of AI technologies and and products nowadays?
Yeah, I I I think we've all seen just 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 have a competitive advantage but the 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 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 gaps are, suggesting what alternative products are.
All of this to the sales Rep, So that 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. 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, 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.
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 adoption, we're looking at things that are highly repetitive that are what what we would all say is really
annoying, hugely time consuming. So whether you're looking at matching up high volume parts or you're looking at matching competitive parts as part of a a request for proposal. If you're looking for what was the last price that my customer paid for this set of high volume parts 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 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 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 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 of which our senior executives constantly ask for, no longer
becomes a three day ordeal. It becomes 15 minutes of inputting the variables, hit the button 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 sense to talk about where AI is not appropriate. Good point. You know AI is great at figuring out repetitive tasks and helping us 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 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 go about taking the lower value but crucial items such as moving the template from the RFRFP template 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 trade off decisions and how does this 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 also for the skills we need to continue to develop in our careers as we navigate the 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. But also from a human standpoint understanding how to judge the value of that output. That's good that you two were able to kind of separate that to outline which which you know which matters and which kind of doesn't when it comes to AI
expectations. AI obviously this is a system, 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 that's what we want the participants to be interactive in, in this session so that they can 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 seconds after everybody goes, Oh no, we gotta start crunching. Well, no you don't. And when you can save between 30 and 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 strategy and the decision making and 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 because now 70% of your workload is 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 the next week they go home just say, hey, I know exactly what part of this I can use AI for.
And and Brooks is going to spend some time talking about some specific tools so that folks get an 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 what they can go do next to save that 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 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 in the amazing world of pricing and so that's awesome. When When we began talking to businesses and the the course of our firm's work, one of the items that came up was the bid process.
And what we heard repeatedly was that 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 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 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 been able to get eyes on all areas of 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 mechanical blocking and tackling, moving things from one spreadsheet or one data source to another rather than thinking through how all 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, 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 alluded to my last question I was going to ask about industry profitability, but you're 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 overall, but a lot of people think that AI is going to take over the world and take over all these 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
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 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 just as well as it is today, which
might not be ideal? Or do I want a job done really well and really quickly and in a high quality manner? And then can I as a organization go do that for many, many more
customers? So for example, some of the work that was done or changes made recently at Schwab that they have published are they're going on a hiring spree of salespeople and customer service people because they were able to cut down on some of their repetitive back office work, which freed up a lot of resource to be not less human centric, but actually far more human centric when working with their customers. That's cool, yeah, little things like that.
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 very popular workshop and topic of discussion coming up during our Spring Conference, April 23rd through the 26th in Chicago, IL. Before I let you both go, I do have one more question for those who 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 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 well as Strategy advisors dot AI.
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.