Join Heather Richey and Kaavya Muralidhar to talk about what it takes to be successful with AI in your pricing practice. In this discussion, we will walk through what you need to know about AI, how to tell if your organization is ready to use AI, and how to measure success.
Terrence: Hello, everyone. Thank you so much for joining us on this professional pricing society podcast. My name is Terrence, and it's a great time to have another discussion, especially about AI. And so we have two very special guest speakers with us today who's gonna be diving into this topic with us. About what does it take to be successful with AI?...
Today, we have Kaavya Muralidhar, who is the product manager at PROS. She leads strategy and implementation for AI based price optimization software used by some of the largest enterprises in the world. We also have Heather Richey, who is a strategic consultant with PROS as well. And you guys are gonna be diving into this conversation with I'm very excited to have you first and foremost. I wanna ask before we start, how are we doing today? Everybody doing good so far?
Heather Richey: Fairly fantastic.
Kaavya Muralidhar: Excited to be here
Terrence: Good. Good. Glad to have you guys. So we'll just start with the very first question, then you guys can take the time to, you know, answer these as best as you can, but what I wanna ask, what is the first thing that you want people to know about AI, an artificial intelligence? Where should we start our approach and our thinking when comes to AI?
Heather Richey: Yeah. Terrence, I can take that one. So I think the first thing that I'd want people to know about AI is that it's it's not new. There's this idea and it's feeling out right now in the general public that just because the public is finding applications for AI that this must mean that it's it's new and thus it's inherently risky. But companies have been developing and implementing AI for decades. Specifically in the pricing space, you know, Kaavya and I both worked for PROS and PROS released their first AI pricing product back in the eighties. So there's been decades of development and research that have gone into these types of products so people can be confident in the results, in the AI results in this application.
Terrence: Yeah. And that's a good point. Artificial artificial intelligence is not new, so I'm glad you made that apparent for for listeners.
Kaavya Muralidhar: And, I can add a little more to that. I think one of the important things to know about AI is that ultimately it's a reflection of humanity. I think that a lot of responses we've seen towards AI include both, excitement about its potential as well as fear and nervousness, and I actually think both of those can be valid responses because I think that is a reflection of humanity's intentions and the potential we have to do both, to do both good and, as well as self serving but I think that the what's really exciting about this conversation is us collectively developing ways to use AI as a tool for good as an and as a tool for helping us, save time, progress to a better place, even save lives. So I am really excited about the ways it can help us, move forward as as, community and, as a planet.
Terrence: Okay. That's good. Yeah. AI, you know, it is a means of being more efficient in a lot of aspects. Now since this is a pricing podcast, you know, if a company was looking for an AI solution to fit their pricing application, What should they consider when evaluating different AI solutions?
Kaavya Muralidhar: Yeah, Terrence. Great question. We see this a lot with the people that we work with, I think ultimately when you look at what AI is, it is a system that takes in data it processes the data and learns from it, and it provides outputs along with a software or a system around it to help you interact with those outputs.
So I think when you're a company that is looking at how to evaluate what to look for in an AI solution.
You're really looking for, you're really looking for those same three elements. You're looking for what data it takes in, what it does with the data, and how it does it. So, you know, diving into each of these looking at the data that it takes in, a lot of companies and organizations may have, had a lot of difficulty with collecting data as well as with having the data in a very specific format. So you really wanna look for solutions that are very flexible with their schema of data that have integrations with different CRMs and ERPs. So you're able to flow your data in easily. And then have a lot of automation built in, whether that's automation around data cleansing, automation around scheduling how often your data comes in or, or any modifications you wanna do to your data before it feeds into the AI.
With what it does with the data, this is really the heart of the AI. And I think this is really important to know that not all AI is equal and AI doesn't just mean one thing.
So when you look at what an AI system actually does with the data, it is really important to dive in a step further and consider Is it is it a solution that is really good at accounting for Sparsity? Is it a solution that is really that that is actually using AI to look at trends over time, which is really important, as we've seen in pricing in the last few years.
How does it navigate elements that are really changing frequently, like changing costs or changing market indices And what scientific measures is it using? Is it using the most recent, the most innovative solutions that show you the best statistical measures of success?
So we really urge customers to look into these questions and evaluate AI for what it really is doing. I think that's not That's not a mystery. It is doing what humans can do but better, and it's really important to ask those questions, and look at what it's attempting to do with the data to get the to these outputs.
And I think finally this last point is often something that isn't given very much importance in the conversation around AI, but when you're a company that's actually using AI software for pricing or for anything else, looking at the surrounding system at the how is really important.
So not only do you want your AI to be giving you the perfect best results, but you also want a system that is really explainable. You want your user to be able to go in and actually understand why the AI is recommending what it does. You want them to be able to go in and provide feedback to the system or even be able to change certain parameters to be able to change the data that's going going in. If they have a new strategic business school, you want them to be able to apply that by themselves.
So looking at self-service transparency explainability, all of these are really key to being able to use AI successfully, and I would say part of the AI software as a whole. So when you look at these three, I think you've really done a comprehensive view into, into understanding what an AI system can offer you.
Terrence: That's good. That there's a lot that goes into And based on my understanding of what you just said, there's different levels, or different types of AI. And then, of course, I would imagine different companies can use AI for different reasons, but the three different points you made made mention of You know, that makes a whole lot of sense to in intake the data what it what it uses the data for. And how does it present the output? So it's a lot to think about when when considering AI, And then you have to also ask yourself the question as a company. How do you even know you are ready to use AI as a company?
Heather Richey: That's a great question to think about right now because everyone wants to use it, but not everyone's necessarily ready to use it. Because I think it's all about really the the mindset of of your company. You know, once you've identified a part of your business that that can be improved with AI, you also need to identify at a high level, what type of data you you might need to feed into the tool, who's gonna be impacted by by this process change and are you as a company open to that change? You know, knowing what type of data you need, again, at a very high level, it tell you what teams need to be involved. So knowing this will let you know at, you know, at the very beginning, who to get in contact with, who to bring into certain key meetings, and who you need to get buy in from, you know, at at the beginning.
You also need to know who's going to be impacted by this changed because it lets you know who's going to be available for more high value work. So once you have an AI solution actually in place, it's going to speed up many of your processes. It's going to take the place of some of those those processes, and that means that your team can actually spend time working on things like analytics and strategy, and those really high value, high level thinking, detailed work. So, I think that's really important to understand who's gonna impacted why that that you can start working on additional future projects that, you know, can take advantage of those skills, and that can be used for motivation for for these types of projects.
And then my last my last thing I think that you need to consider is, you know, are you open to change So it's really important for these types of, those these types of projects that your teams are are open to change. And that's not over the change in their current process, but also potential changes to strategy, to the new processes that will be built, you know, based on your your findings from AI. One of my favorite things that I that I loved when I was actually on the implementation side of of the house was being a part of an AI pricing implementation when the customers would understand something new or learn something new from the results that they probably couldn't have, you know, easily found those connections otherwise.
So seeing connections in their data that actually influenced influenced a change in their strategy that they then implemented and then that fed back into the AI tool. So just being really open to change, open to to the results and what you can do with them, I think, is extremely helpful.
Terrence: That's that's really good. And it's it's unfortunate, but a lot of companies may not be open to change. And that's a real thing. But as we know, the, I guess, companies or even just the individual people who are open to change are typically the ones that are gonna be more successful than than not, because they are adjusting to the changing times. Now when you think about change in transformations. What can companies do to be successful regarding their digital transformation of the growing and changing times?
Heather Richey: Yeah. My last response, I mentioned teams countless times, mentioned a bunch of different teams. It's critical that you have alignment. Like, alignment is key for digital transformation, and you need both vertical and horizontal alignment. So the goals of your your AI project within this digital transformation, they need to align with the vertical goals of your company because otherwise they they won't be successful.
But you also need to have alignment between your team horizontally. So if your sales team has has one goal with how to use AI, but it needs to be integrated into your architecture that your IT team is in charge of, well, you need to make sure that the goals of your IT team aligned with, you know, this tool that you wanna implement for sales or for you another part of of your business. So as long as you have this this this horizontal alignment, you can really avoid those conflicting initiatives that I see, you know, teams not be as successful with a digital transformation. And also having specifically that horizontal alignment, it really helps your teams build build trust, and you can't have success without that that trust between the different teams.
Kaavya Muralidhar: And I think to add to Heather's point, it's really important to trust your team members. I think An example of a negative digital transformation would be just going in and insisting that all of your teams constantly and only do exactly what this new technology tells them without really taking their expertise, their intentions, their motivations, and and what they've learned so far in their trade into account. And I think a lot of when we look at other industries that have adopted AI successfully, for example, you can look at, health care where physicians may use AI to help detect cancerous lesions. This isn't a replacing of a physician with AI. Absolutely not.
What this is really doing is using AI to help make their work faster, more efficient, and being able to point out things that maybe they may not have noticed but you still continue to give power to the physician to interpret and to make the final decision. So in this case, an AI system may be able to point out certain variables that in an MRI image that may be suspicious or may point, towards it being a cancerous lesion. But the physician really gets to look at that in context of the patient overall and interpret it and make that final call. I think we've seen similar examples with, IBM Watson, IoT, for example, where, IBM Watson is used to be able to identify if certain machines or equipment may be malfunctioning and any prescriptive guidance that a technician might do But again, it's providing recommendations as to certain action steps that they can take along with a confidence score and along with the reasons why it thinks those are the right action, and the technician can look at those.
They're definitely probably gonna see things that maybe they hadn't they wouldn't have been able to come up with by themselves. But now they have this tool that's really providing them that experience. At the same time, the technician really is there in that moment, and They have the power to really use that AI interpreted according to their expertise and make the final call. And we see this a lot with, you know, successful pricing digital transformations as well. This isn't a replacing of the pricing team, and we never want it to be we never expected to be presented as such.
It is a tool to help our pricing and our sales teams really be able to use the power of data to use the power of what we've seen historically and to use the power of something that can think think faster and think think better than them in certain ways, along with supplementing their own expertise of what they're seeing in the industry and what they're seeing with the customer or the prospect in front of them. So we really believe that this is a partnership and in a digital transformation, what we're doing is we're not replacing. We are adding, and we are augmenting the power of what the company can already do.
Terrence: That's really good. That's really good. That and that makes perfect sense as well. It's not something that's necessarily meant to be a replacement, but it's a tool.
Help strengthen, the organization to help be allow them to become more efficient, quicker, And also, like you said, just to think differently in certain ways. And then also Heather, I'm glad you mentioned the whole aspect of teamwork. You know, regarding digital transformations or even just reaching a goal, as a company, a lot of departments have to be on the same page. And that can be a process moving you know, to to get all the all the departments on the same page, but that's how the dream works.
Teamwork makes the dream work, and that's how you know, transformations successfully happen in the long run-in the long, larger scheme of things. Let me ask you guys this once a company has decided that there ready for AI, and they've already selected AI solution, and the solution has been implemented. How do they know it's going to be successful?
Kaavya Muralidhar: Yeah. I could take that turns. So, this is a great question because it is crucially important to constantly be measuring adoption and be measuring value. Additional transformation or adopting an AI solution isn't just a plug and forget about it forever, kind of play here. We really want to make sure that our, we we wanna make sure that companies continue to really measure what value they're seeing from it and know that for themselves.
So some ways to measure value, I think there are some core questions to ask. One is even during your implementation or before you really start using it, the importance of engaging with the system, looking at the results, really looking at why those result why those particular results or recommendations are showing up, and for you and your team to feel comfortable and confident of those. Because that provides a really strong starting ground to than six months later or one year later when you're looking at adoption, which is I've, you know, I've had my system in place providing AI based recommendations for pricing. How do I know that this is a these recommendations are actually being used, or are they being ignored?
So when you when you know that the results are reasonable because you've, you know, validated them yourself, now you have this next step or of checking whether your team or your company is really adopting those those recommendations. And, if not, you have a starting point to know Well, you know, which which department or which team are you not really seeing adoption? And what's going on? I think it's really important to bring curiosity this kind of question. Is there is there something the team knows that maybe isn't included in the data that we need to start bringing in or is is there something else going on where they aren't necessarily familiar with the explainability tools. So, and having those allow them to much interact with the system much better and have better adoption.
So looking at adoption and diagnosing adoption is really crucial. But beyond that, I think the final step is really measuring value. So you want to know that when you are seeing adoption, you're seeing value. And in the case of pricing, This means that you're meeting your financial goals. Are you making the kinds of revenue or are you making the kinds of margin that you and the uplift that you expect to see when you're following this pricing solution and when you are seeing adoption.
A lot of times we've you know, we've seen with our customers at pros. We have, a measure of where you see adoption, what is the revenue that you're seeing and how do you compare that to, you know, a similarly sized unit or similarly sized, set of sales where you weren't seeing adoption. And is there is there more revenue there or less revenue? And I think this this is an example. It really depends on the company's goals and what they're trying to achieve with their pricing.
But when you have that kind of, that kind of compares it and when you are able to really see for yourself whether this AI solution is providing value to you, much is it providing? Is there opportunity to increase it? Is there opportunity to be maybe be educating your company and, really aligning your teams more I think that is crucial and that's a journey that goes beyond implementing, and really, ensuring the continued growth and success of how an AI solution functions in your company?
Terrence: Very well said, miss Kaavya. I wanna thank you Both so much for being a part of this podcast today for just engaging with me on this discussion about utilizing AI in the realm of pricing. Before I let you go, both of you, I wanna ask, is there any type of resources that listeners can visit or grab ahold of to learn more about you individually or pros and what pros stands for and different things with that nature.
Heather Richey: Yeah. People are welcome to reach out to us. On on LinkedIn to connect and chat, and you also can go to the PROS website pros.com. That's p r o s dot com, to find more information about the company that we work for specifically, and, it's the AI tool that PROS has available.
Terrence: Awesome. Thank you so much guys for your time today. And until next time, we'll see you guys then. Bye bye.