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IIIMPACT is a Product UX Design and Development Strategy Consulting Agency.
We emphasize strategic planning, intuitive UX design, and better collaboration between business, design to development. By integrating best practices with our clients, we not only speed up market entry but also enhance the overall quality of software products. We help our clients launch better products, faster.
We explore topics about product, strategy, design and development. Hear stories and learnings on how our experienced team has helped launch 100s of software products in almost every industry vertical.
Somebody was almost complaining where it's like we're all being siphoned into these buckets of habits and things.
Speaker 2:It's not humanly possible for anyone to actually have done that without AI. That's the game changer here is that it really turns data analysis on its head.
Speaker 3:All that data together, again, just can pull out insights, which you don't have to have the questions prepared already. You don't have to go, okay. Well, these are my questions. Can I answer these questions for me? It can give you insights, you know, that you might may not even be aware of.
Speaker 1:Hello, everybody. Welcome back to, a new year, 2025. We are back at our great AI podcast with your hosts, myself, Makoto Kern, Brinley, and Joe. Hey.
Speaker 3:Hey. Guys, Dan.
Speaker 2:We are in 2025 now. Yes. Yeah. GT 2,000.
Speaker 1:We have flying robots and walking cars.
Speaker 3:Yeah.
Speaker 1:Yeah. But it's great to be back and and into this new year and excited to see where, AI and all these good, technology things are gonna take us this year.
Speaker 2:Yeah. It's always fun to discuss it. So
Speaker 3:Yeah. Forward to it.
Speaker 1:For sure. And so with this, our first podcast of the year, what we're gonna be talking about today is, basically, you're sitting on digital gold. The data and the things that surround and power the your, AI for your company, that's something that we'll be talking about of how to identify what that data looks like and and and a lot of the things around that you can actually leverage that to make your AI a lot more powerful and useful for your organization. So with that, I think we'll, Joe, if you wanna kick it off and tell us a little bit more about that.
Speaker 3:Yeah. For sure. So we kind of got about 5 different sections to go through here to subgroup to sort of understanding what we are already talking about here. Like, what is data? How does that relate to AI?
Speaker 3:And really what this means for any sort of business or anyone who has existing data that they've been sitting on for years potentially. That's, you know, traditional systems, databases, you know, capturing software, anything that you've been using that you've been storing data with over time. You know, it's really what we're talking about just in terms of data. And, traditionally, you know, trying to get insights or extract value out of that data has been a very hard, challenging thing to do. You'd either have to sort of, you know, grab extremely expert systems to help you with this or data scientists or get a whole bunch of IT guys with a lot of understanding of how to go through that data.
Speaker 3:And there's 2 parts to it because it's really, you know, understanding what data you actually have, what is this data and really what value can we actually extract out of this. And sometimes you may not know both sides of that. They both have their own sort of domains of understanding. You may even really understand your data really well, but you're like, well, how can we use this data? Like, you know, what can actually do for us?
Speaker 3:And so what's really changed in the last, like, few years, especially recently with just how AI has exploded, is the ability to actually answer both sides of the sort of the the question and the answer of what data do I have and what value and insights can I extract out of it? And AI has become an amazing tool that's most importantly cost efficient and effective to implement to actually answer those. Previously, again, as I said, really expensive, really challenging. Whereas now it's just becoming way more accessible and really, really a low barrier of entry to you and have to sort of, you know, hire an AI expert or or someone who's, you know, been in AI for 20 years. You know, there's software out there, which is just bolt from the ground up to really understand.
Speaker 3:And you can just plug on top of your existing systems, and it can just start going through that data and understanding it for you. And you can just see what happens. You know, it's such a low barrier to entry that you can kind of just start implementing these systems and to see what comes out of it and to see where you strike gold. Because, again, this is what we're kind of trying to say here is that you've got your whole fields of data, but there's gold inside there. Right?
Speaker 3:There's oil. Like, you know, like, you know, data is like the new oil of the modern age, and you can strike it really rich, you know, from a value perspective for your business. And this whole time, you've just been unaware that's been sitting there. It can be, you know, an interesting way of thinking of it because, again, you may not even be aware of it. This is something that you can just discover yourself.
Speaker 3:So that's kind of what we're talking about with, you know, data and knowledge. Yeah. I don't know if Makoto and Burton, you know, as a sort of as I have that introduction, just sort of passing it back to you guys, you know, from a business perspective, how do you see that sort of adding value to it? Like, if you were, you know, to sort of take the role of a business and sort of asking me these questions, what are the questions I have around this? Like, you know, can you give me some use cases, or what do you think about it, or how do you go about it?
Speaker 2:Yeah. I think from what we've seen I don't know if Mikoto, if you want to add something first, but what I had is what we've seen with with clients and inexperience firsthand is so many people are unaware of of what they have, and it's it's in the, you know, the strangest places often. You know, it can be something as straightforward as, you know, database that is recorded from a sales system, and it's straightforward. Or it can be tapping into data based on your business, like social media posts or things that you wouldn't think about mining for valuable data. And even larger clients and bigger corporations that have a lot of internal data, they're often met with that that sort of balance of, well, is it worth hiring people to review this data, and how much, you know, return on investment am I gonna get once I've paid x number of people to review the data?
Speaker 2:Whereas now, as you're saying, Joe, you can plug in and get immediate insights. So whether it's pulling back social media reviews to see, well, people are complaining about, say, specific, maybe long wait times, you know, for your coffee shop, or Yeah. It's pulling back that there's a particular feature of your application that needs to be refined. So the applications are so widespread. And, you know, now that again, just, you know, highlighting what you said, breaking down the barriers to entry, you don't need a whole team to, you know, spend hours and hours sifting through data.
Speaker 2:It's now you know, you can present the data to the right type of AI and actually gain those insights very quickly. That can, you know, be incredibly powerful.
Speaker 1:Yeah. I think the digital pickaxes and and shovels out that, you need to figure out, what those are to, I think, mine the data. It seems like it's pattern recognition for the most part. I think for we're thinking, like, some of our clients' cybersecurity. If you see certain patterns occurring, you have a human identifying whether that is if it's a real or if it's an anomaly or if it's been flagged wrong incorrectly or something where you're trying to maximize the time because of the large amount of, let's say, attacks are happening onto a server or a system, so you want them to be really focused on something that is of, like, high value versus of low value.
Speaker 1:So it's kind of essentially like what a dashboard I see is for human beings. You're trying to identify actionable insights so they can really optimize what they can do. With AI understanding, like, that data and training it to recognize those things, you're essentially building, like, a dashboard AI for itself or for the data itself to maximize who's ever doing that work. And it seems like it could be I mean, it's any type of detection of from, like, manufacturing. Like, if you're building widgets, you recognize some kind of error through vision systems to Mhmm.
Speaker 1:You know, could be I think I'm sure you're gonna talk about this example. Obviously, like, Spotify, Netflix, they have recommendations. Mhmm. And it was interesting where I read something where somebody was almost complaining where it's like we're all being siphoned into these buckets of habits and things because AI is recognizing, oh, you watch these movies, you listen to this type of music, this is going to be for you. Mhmm.
Speaker 1:But you don't have that individualized thing where you're like, oh, this is what everybody else that I know listens to the exact same playlist, the same songs, I want to hear something different. So I think there's it's gonna improve. You know, that's how I see, like, where data can be really useful in understanding that kind of pattern recognition for whatever business you're in.
Speaker 3:Yeah. Exactly. And I've even got, like, you know, just to take a sort of step to a more practical approach. I'll just sort of read through some, you know, example use cases of how AI could help a business, you know, again, using our existing data. This isn't something new.
Speaker 3:You don't have to sort of implement a system that has to start creating this data for you, and you only see value in a few years. You've got the value already. This is just about unlocking it. So this is take the example of a software company, right, that sells software. Now this will go through 5 kind of areas around this that, you know, exactly what we're talking about here, where you can actually extract that value.
Speaker 3:And as Ben mentioned earlier, Mikoto, you mentioned too, things like demand forecasting. Right? You know, predict seasonal demand for your software. You know, maybe you have tech software. Obviously, there's a different different season for that.
Speaker 3:And that seems like a straightforward one. Okay. You know, it's it's tax season, obviously, I'm going to sell more. But it becomes a bit more than that, right, because you can start using you know, it seems like a simple use case, but there's a lot that actually goes around that. There's a lot that's built into that.
Speaker 3:Right? It's your server capacity. It's your support staff. It's your anticipated, you know, website hits, you know, your sales agents, you know, all that feeds into it. And if your software is a bit more ambiguous and you may have spikes throughout the year, maybe, you know, you're writing software that helps restaurants, you know, manage stock inventory and there's certain, you know, as the year goes by and certain events happen or or something a bit more nuanced, you know, you're going to need to understand, okay.
Speaker 3:Well, how many, you know, support staff will I need? How many more sales agents should I bring in for this next quarter? And it can be hard to answer that question. And if you have the experience in it and you have the existing sort of, you know, use cases built up around this, sure. But if this is something that's a bit more nuanced, you wanna be able to try and do predictions on that.
Speaker 3:Right? And this is what I'm saying around demand forecasting. Like, okay. Let's look at our previous data. Let's see what it looked like.
Speaker 3:Let's see. Okay. What it looked like when we had 5 agents? Were we able to get through all our support cases quickly enough? During this time of the year, what about, you know, in a month's time?
Speaker 3:What about in 2 months' time? What about in 2 years' time? Where do we see these trends going? Can I be a bit preemptive around this? So any kind of forecasting is just great for this because again, it's looking at all different aspects to it and understanding, okay, you know, even the supporting infrastructure around your product, how can you scale that up to sort of meet that demand and how can you predict that demand coming up and be ready for it.
Speaker 3:Right? So that's an example of demand forecasting. You've got the data ready. It's their, you know, perfect use case. And yeah.
Speaker 3:But then we kind of chime in if you have anything to add about these as I go through them. Yeah. Another one, obviously, is churn prediction, analyzing user activity to identify customers who are not fully utilizing a subscriptions or showing signs of dissatisfaction. This one can be extremely nuanced. Right?
Speaker 3:This is a bit more nuanced than even the first one because how can you decide or how can you determine if someone's unhappy with how they're using your software? It's not obvious. It's not like they're, you know, sending through really unhappy support requests and saying, you know, the software sucks and, you know, I hate it. And, you know, they're canceling the subscriptions. You can kind of somewhat see these patterns emerging from their behavior.
Speaker 3:When originally they got their software, they're logging in every, you know, 2 days now to be day and now to be, you know, week. Or it could be even, like, you know, listening on. So log in to every day, but they'd sort of, you know, not really using the full suite of what your software can offer, and they're only using a small niche part. And that niche part may have a lot of competitors within your market, and you sort of realize, okay. You know, if this is the value that most of my subscribers are, you know, pulling out of my software, I really need to, you know, put more marketing and a bit more push behind what else my software can do.
Speaker 3:Because, obviously, we can see that that's not being used to its potential, and we could lose these customers or just sort of seeing, you know, just general trends and how they're interacting with your support staff, even something as nuanced as that is understanding, well, their social media sort of interactions and how social media is sort of talking about your product. And that can be something that's extremely hard. That's a lot of data, right, especially when it comes to social media. You're not talking about, you know, analyzing a few support cases. You're talking about analyzing, you know, 10 different social media platforms across, you know, 10 different sites.
Speaker 3:That data is existing. It's there. We can, you know, pull value out of that. Look in the history and sort of see it. Let's look at our 100 customers that we lost last quarter and actually look at all their trends, look at their social media, look at how they use the software.
Speaker 3:Let's build up a model around what that looks like and then start seeing, can we predict, you know, other customers going down this road too? Let's try and get ahead of that and try and understand, you know, what's happening here and put some resolutions in place for that. So, again, that's just an interesting is amazing.
Speaker 1:That's an interesting point. Just thinking when we're building something for, like, conversion rates for our clients that, you know, they're trying to increase subscription rates and they're trying to let's say they're competing against other companies that are selling the same thing, same service or same, whatever. It could be insurance. It could be energy. It could be whatever.
Speaker 1:And it's interesting to see, like, I'm sure when you've talked to customer service, you're gonna get the same kind of problems that they're going to complain about. Oh, the price is too high. Why did it change? Things like that where the customer services then walk through a set of, like, how to answer these kind of negative things. With the AI, just identifying those things through the feedback or through the data, you can then anticipate that in the future, knowing, like, hey.
Speaker 1:We know that during this time, prices increase, so we're going to send this out automatically to these clients and let them know, like, this is normal or whatever the case is or this is why and be, like, transparent. So it seems like there's there's gonna be a lot of, like, automation that can happen because of that, and that's gonna be really important to mine that data and to know those
Speaker 3:things. And then maybe anything is to sorry. Just talk just a quick sentence around data. Just to sort of push the point to we're not when we're talking around, like, you know, AI looking at your data, it's not like AI looking at your one database or your one CMS system or, you know, it's taking all of these different systems, the data from all of them, putting it all together and sort of applying AI on top of that. And they can sort of understand the intricacies between that data, understand, like, you know, what connects all that together.
Speaker 3:And all that together, you know, looking at each of those data points individually may not help you understand, you know, why that customer left you. But looking at it altogether, certainly, like, there are actually connection points between all the different data points and understanding, okay, their usage across all this data, how they interact with the system, how they interact with your support, how they interacted with their sales, their usage, you know, all these different systems feeding together into one great view of them and their usage. And AI can really extract them the the sort of outcome from that and help you sort of predict going forward. So, again, it's just around, you know, great looking at large amounts of data, which traditionally would be extremely hard to go through because there's just so much. It's almost impossible to try and make those connections for yourself.
Speaker 2:Yeah. That's what I was going to add is that I think it's important for for anyone listening to understand that AI has surpassed the limit of what a human team can do. So you think, you know, if you're bringing certain models to the table to do analysis, you're bringing with them a whole lot of general knowledge. So what are other common trends that people have documented in a whole lot of different industries? It's almost like bringing someone into the team that has a whole lot of experience across different sectors that can reference all those use cases immediately, and then you say to them, right, what would have taken, you know, a team of a 100 a few years to go through on really large datasets, this is going to go through in, you know, hours and then come back with all these insights that it is just it's not humanly possible for anyone to actually have done that without AI.
Speaker 2:That's the game changer here, is that it really turns data analysis on its head and why your data that you may have been sitting on for ages and not have really reaped the rewards from it is now this new currency in really the success of your business. And I think that's that's an important kind of take home is that, you know, now there are tools that do make it a game changer.
Speaker 3:Yeah. Exactly. And there are a few more to go through. Again, something like customer feedback analysis using AI to analyze customer reviews, support tickets, to identify frequent complaints about your software. And this can be really great because, again, you know, this isn't necessarily churn.
Speaker 3:It's just understanding where to focus, where to put your energy as a sort of, you know, a resource of your company. Where should you focus on next? And that can be things like prioritizing feature updates, fixes, you know, based on that sort of customer feedback. And, again, based on usage too, you may think a certain area of your software is not being used at all, but when you looking at the data and actually understanding the usage patterns, even if customers maybe only go there, like, you know, very rarely, it's still really important to them. And just understanding, okay, maybe we should actually put more features or functionality or effort into sort of improving this feature because we can, from the data, actually get insights that, okay, you know, it's not used a lot, but when we see it not when it's down or there's bugs, you know, sanity, customers are just emailing it very quickly, which shows that they actually have a, you know, a large investment into that part of your software.
Speaker 3:And that's again something very nuanced when you've been able to sort of detect yourself despite looking at, you know, traditional ways of looking at system usage. Oh, these are my top serve, you know, most clicked on buttons on the application or screens that have been visited. This one's way down. Number 20, you know, wishing to worry about that. But, yeah, I can help you really understand, though.
Speaker 3:There's sometimes a lot of value in what you really have that just wouldn't have been evidence without sort of understanding the bigger picture and how it all feeds together Yeah. With your customers and how they interact with you.
Speaker 2:That that is good because I think something that we've spoken about in the past, you know, personal bias really builds on this. I mean, you think if if you've got a large support team, you've got a 100 support staff. Now they're tasked with determining what is a complaint or what is a feature request. Now they all have completely different views and completely different sensitivities as to what constitutes a complaint and, you know, what is just a bit of feedback. So, you know, even with that example, you can aggregate all of that feedback that's being documented.
Speaker 2:It may have not been flagged correctly. Some may have seen one thing as a feature request, another thing as a complaint when wasn't the case. That can all be grouped and correctly categorized under a a sort of standard measurement almost, which, you know, is just creating these these insights that you wouldn't have been able to get by relying on the manual kind of feedback systems that are traditionally used.
Speaker 3:Yep. Exactly. And, again, just around the same point, it's always, again, not just about looking at one set of data. It's like looking at the whole picture. So let's say in that example, I just had about trying to turn where to put your effort into your system.
Speaker 3:It's looking at not just the usage in your system, like maybe your Panda Analytics. It's not just looking at your support queries coming in, but it's looking at other areas too, which you may not even be aware of, like, you know, where our customers click on your on your website. What documentation are they visiting? What level of users are the expert users visiting one area of a system or new users visiting one area of a system? All that data together, again, just can pull out insights, which you don't have to have the questions prepared already.
Speaker 3:You don't have to go, okay. Well, these are my questions. Can I answer these questions for me? It can give you insights, you know, that you might may not even be aware of and actually, like, sort of help you prioritize parts of your business, which you may not even have been aware of needed prioritization. That's really the power of it to sort of, you know, again, come up with the questions and answers at the same time for you, which you weren't even aware already there.
Speaker 3:So another 2 months to quickly go through here. Just, again, use cases, pricing optimization, adjust subscription pricing dynamically based on demand, customer willingness to pay, competitors offerings. Great use case again, just, you know, looking at the whole industry, looking at your particular software, its trends over time, the type of customer coming in, you know, what else have they interacted with maybe in your product suites that they've really been using it? What was their behavior and usage like in those areas? How did they come into your site?
Speaker 3:This is just all around marketing, understanding your customers. So much data there. And again, yeah, that's a perfect use case to be able to understand pricing optimization. And then just last one, automate reporting and insights, generate real time usage analytics for your customers, showing them how to use your software to improve their ROI, provide internal businesses internal business performance dashboards, track sales trends, customer attention, and product engagement metrics. Again, pretty much what we're just going on, but the ability to sort of provide this or show this data in such a meaningful way, which previously would have just been extremely challenging to try and understand that visually or to try and present that data to customers or try and sort of show that value in a tangible way that you can actually, like, you know, build up action items against that data.
Speaker 3:Previously, it would have been extremely hard to go through. Even if you have the data, even if you know the questions, just what are our next steps? How do we actually use this? And, yeah, I can, again, not just, you know, come up with the answers, but come up with the way to present it to you in a way that actually is actionable. It's not just, you know, a large amount of data or just a bunch of Excel sheets being thrown back at you.
Speaker 3:It's like in a grasp analytics and and ways to actually understand
Speaker 2:The interaction is another value add on top of that because you can have a report that it generates that has a whole lot of insights. But if you wanna pack it and say, well, what does it exactly mean, you know, if you're showing me that metric? What has led to that metric? So it's almost this tool that, you know, can provide insights, but, you know, when set up the correct way, can also be maybe a conduit to a much greater understanding of your data and, you know, you being able to say, well, explain to me why this is like this. And if we change this, how would it be?
Speaker 2:And, you know, sort of explore scenarios. That is incredibly powerful as well.
Speaker 3:Yeah. Exactly. And, again, just to elaborate, you know, this is not something that you have to be a massive organization to use or implement. This does need a huge team of people to try and understand it and understand your existing software and your existing systems to sort of integrate this AI into there's so many great tools out there right now that can help even small businesses really untap this. This isn't something for the big tech giants anymore.
Speaker 3:This is something that is available for everyone to use. And, you know, we've been dealing with this quite a bit recently. We understand it really well. It's a field that we're really interested in. We have practical examples of how we've been able to sort of help businesses understand, okay, this is where they are at Currently, this is where we could take you, and this is the tools and softwares that can get you there.
Speaker 3:And it's such a low cost investment to apply AI in this way and to gain those insights out of it. You don't have to hire an entire company to do this for you. It's really accessible. The hard part of this, though, is understanding which tools are the best use cases to understand, you know, where we at right now and where we wanna be and how this can help us because the market is flooded with AI tools right now. There's a lot of shovelware out there, but there's a lot of really great tools.
Speaker 3:The shovelware offers the moon and offers you, oh, just click on this one button and it'll just give you all these insights. And that can work to a degree. But there's 2 parts to it. It's really about understanding your data, understanding not just, you know, where it came from, but how it relates and where we see AI is the best fit for that use case. And then understanding with our experience the tools that are out there, okay, which tools are best fit for this use case and where you wanna take it.
Speaker 3:You know, is this about understanding your customers? Is it about understanding your software? Is it about understanding the security of your business? Is it about improving efficiency within your business using AI? And understanding, you know, what the best tools to use that really is around understanding how they're actually being created.
Speaker 3:What's the back end technologies of those tools? You know, what were they, you know, built upon? Understanding sort of where AI has come from, what makes AI impactful today, and the whole history, which we have a lot of experience in, is really great for us to be able to understand which are the best tools to fit these use cases.
Speaker 1:Yet, I'm curious about the expertise because, obviously, on our team, we have kind of a a multitude of expertise between just us, us 3, from the design, front end, back end, now AI. And having it still be somewhat user centric is obviously very important because that's ultimately your customer who interacts with it with, whatever you're producing. But I think understanding, like, you know, there is a lower barrier to entry now, but there is so many tools out there. What does that team look like? Is it really just we have to have or a client should use not just just hire a back end LLM developer, but you need to hire kind of, like, that kind of full gambit, whether it's a consulting company like ours or is it a like you said, it could be a small team, but you have to have that ability to identify, you know, what the data is, where to find it, how to use it, how to extract it, and then implement it correctly for your users in order to actually get the full benefit, not just hire some back end person that just, hey, here's our 5 tools.
Speaker 1:Use this, and you'll make 1,000,000 of dollars kind of thing.
Speaker 2:It is a good question. I mean, if you're listening to this and you're thinking, well, I'd like to proceed with my business, what should you be looking for? And, you know, I think it's a valid question. It probably is, you know, somewhat of a balanced team. I think what you were saying, Joe, is you don't need, you know, a full set of staff, you know, to come in and do this.
Speaker 2:But as you're alluding to, Makoto, you know, you probably want someone who can understand the business that you're in to a degree. You know, maybe someone that has, you know, worked in similar industries and, you know, has a small team that can, you know, go from, you know, identifying the benefits of your business right down to, you know, being in the weeds and saying, right. How are we going to connect to the systems you're using? And I think that's important, and that's that's a caveat to a lot of that software that you're talking about as well, Joe, where you maybe are locked in to a specific offering opposed to you're coming from a more custom angle and saying, well, this is what your business does. This is a sort of solution that would work for you, and this is how you put it together opposed to out of the box that may not really get you where you need to be.
Speaker 2:So
Speaker 1:Yeah. I almost see it as when we go into a client, we understand the best design principles. We understand how to have product strategy involved from a business side, a development side, and a consumer or user side, and putting that all together correctly, injecting the right processes so your business is efficient at building those things. But we don't have the expertise of that given industry, and that's what we rely on the clients for. They have the expertise in the industry.
Speaker 1:So leveraging the 2 and having that separation definitely has a lot of benefit. And same here, maybe we don't know exactly the data that is used, but we work with a SME or somebody that does understand or what are the things that we try to identify, you know, whether it's a workshop or whatever with leadership. What are the important points? What does it look like an efficient process or workflow? And how does that benefit the client overall?
Speaker 1:And tie that all together into, like, creating a road map into what AI should do for you versus let's use anything. And then your second point with, if you're roped into, like, Microsoft Suite or you're already roped into Salesforce, then now you're forced to use those things. We have to design dashboards that utilize those tools because they've already spent a lot of money. They're ingrained with those systems, so there are constraints. But, yeah, you then now you have to just use those particular tools.
Speaker 3:Yeah. It's also just about having a beat on the AI industry in itself. It's moving so fast currently. Mhmm. If we were to go, you know, okay.
Speaker 3:Let's hire someone who's specialized in Salesforce AI, then that's great. You know, our AI checkbox is ticked. Right? That's not the case at all. And if you do that, I think you're actually gonna be behind because those monolithic sort of software packages deploy AI at their own pace, and you just get it when they provide it, whereas the AI space is moving so fast.
Speaker 3:Unless you're kind of flexible right now and kind of quick to sort of be able to pivot and actually jump on new AI technology as it comes out. If you're not doing that, your competitors are. Right? You know, if they're that flexible, you're gonna be behind. They'll be doing offerings and providing features and functionality way ahead of you because you're sort of still, you know, trying to play catch up with whatever implementation that you're invested into or your particular AI resource that you acquired and, you know, again, your OEM specialist who's sitting there, so maybe focusing on a very niche area of his knowledge.
Speaker 3:Great. That's gonna help answer that one question. But AI is answering thousands of questions and just having people understand the industry so well like we do, really tapped into it, understand, you know, what's coming out every week, what are the best tools for that use case, and to be able to pivot to sort of any question that you may have and any kind of answer that you're trying to get to instead of, you know, just being limited to what your current capabilities are with maybe a particular software or a particular sort of specialist who's just very initially and narrowly focused.
Speaker 2:Yeah. Well put, Joe. And I think, Makoto, to tie in what you were saying as well is it really is kind of balancing you know, it's not just about AI. AI is this a very important part of the solution, but it is, you know, how well can your business be understood by the team that you're engaging with and, you know, connect the dots to give you a technological solution that'll actually work.
Speaker 3:Cool.
Speaker 2:So I'm not sure, Joe, whether whether there was more that you were you were adding on. I had another point, but I don't know whether you've got any other sections.
Speaker 3:Yeah. No. Yeah. That's it for me. Go for it.
Speaker 2:Okay. Something that I think we didn't touch on, which is really important is the internal knowledge that an organization has and how however many employees who have built up unique sets of experience, knowledge, relationships, and all of that knowledge is contained within each employee. Now if you're starting to use technology to capture some of the the more important facts of knowledge from each of those different employees. You can start building a centralized knowledge source, and the more you do that, the more you'll enable better sharing of concept or highlighting of concept or culture within those employees, using AI to obviously onboard new employees quicker. You don't have such a significant loss if someone resigns or leaves the company for some reason, and that's often overlooked because there is this investment with no documentation.
Speaker 2:So by working on a system where you're able to capture each employee's knowledge and in an environment where they can share knowledge together, you're going to uplift, you know, all employees to a similar sort of standard by giving them access to, you know, a veteran who's who's documented a lot or, you know, someone senior employee has been documenting a lot of their experience and their insights that can immediately be transferred through to, you know, younger employees that don't necessarily, you know, have the time maybe from more senior to actually engage with those other employees, but it's shared through that medium of AI and through that internal knowledge. And I think that is another really valuable sort of avenue for organizations to look at and say, are we documenting our knowledge as best we can? Are we making this a living resource versus a resource that is very much contained in each and every employee?
Speaker 3:Yeah. And just to add to that, another great benefit of using AI in that use case is, you know, traditionally, if you want to capture internal knowledge, you know, you'd have some sort of internal site that someone would have to write an article around or documents or or, you know, a very lengthy process to capture knowledge that they've had. And then it would take someone to come and read that document. 1st of all, find that document, you know, wherever it is stored and read through it and try and understand it. And if that wasn't well written, if it was written with the expectation that the audience really knew a bunch of, you know, the surrounding material around it, even understand what it's talking about, you know, that becomes extremely challenging in itself.
Speaker 3:And what AI is doing is that you can extract that knowledge in a way more simple way. You don't have to write out a huge document with, you know, perfect grammar and perfect, like, you know, structure and paragraphs and headings. Like, you know, all you want is just the knowledge that they have. It can be just a huge one giant paragraph, you know, from your perspective or emails that they've sent before or any kind of documents that they've written in the past. You know, it can all just be thrown in together into your data warehouse, and I can extract all of that itself and understand how it all connects to each other and understand the bigger picture of what all this data means.
Speaker 3:And so when someone asks a question, it's not just, okay, cool. Well, we've got, you know, it's data that's out there. I can understand who they are, where they are in the process of understanding a knowledge. Are they an expert user? Have they been around for 20 years?
Speaker 3:Are they someone new? And then understanding what their intent of that question is, they may not understand the knowledge well enough to even know how to phrase the question, but they I can guide them to where they're actually trying to go. Okay. You're asking something about our sales system, and you don't really understand the internal terminology that we have around our metrics. Okay.
Speaker 3:But it can see that's where the person's trying to go, and it can guide them in the right path. And that facet of AI, it's not just about extracting knowledge, but about, you know, guiding someone to actually get to their answer is just a massive sort of game changer in terms of that knowledge sharing process.
Speaker 2:And also for multinational companies, a lot of people may not think about this, but you could have, you know, branches in, let's say, China and, you know, in Spain or Portugal, in South America. So you have a whole lot of different languages where you're documenting, and that is not a barrier either. You know, you can have all these different languages translated and understood by, you know, an AI model and being able to pick out the trends and the similarities and really report on, you know, what what is, you know, common across those as well. So And also identifying gaps too, which we have seen before.
Speaker 3:You know, you can sort of start understanding just based on usage and based on, you know, the sort of questions people are asking. And if they're not getting the right answer, you know, it can actually help identify gaps in your knowledge and where you should focus on, which is just, you know, a huge value add to our process.
Speaker 2:And as AI technology advances, I'm pretty sure companies are gonna be, you know, asking the question, well, why didn't we start documenting our internal knowledge a long time ago? Because if we had it, we would be in a situation to act upon it and make, you know, our staff so much more efficient and our offering so much more finely attuned to to what the users or customers need. So
Speaker 3:And yeah. And I think and I think, you know, that's I wouldn't hold it against a company who hasn't done that. Again, just the process of capturing knowledge. Like, most people throughout the day don't have time to, like, spend an hour or 2 every day sort of capturing everything that they know and what they experience that day and the sort of, you know, micro decisions that they've made and where, you know, they've they've built up over years years of experience. How can you capture that easily?
Speaker 3:And just, again, just AI being able to extract that so easily from a person. It doesn't have to be in a huge perfect format. It can just be small snippets of notes as they take throughout the day. That's all it needs, and they can run with it, which is yeah. Just that's the the big game changer on that side of it.
Speaker 3:Yeah.
Speaker 2:And I think to, you know, to look at those opportunities because we've we've talked about things like meeting, you know, note capturing. So, you know, where there's AI that is, you know, taking notes, even utilizing something like that in your internal knowledge base is amazing because often their meetings are opportunities for people to share their experience and their insights, and that is all being documented. So if you had, you know, a year's worth of meetings with high level employees, you've got a amazing amount of insight and knowledge that's being shared through those that is documented without doing anything, with automatically feeding those in to be indexed. So Yeah. Yeah.
Speaker 2:I think I think it is just limitless possibilities. And Yeah. And I guess, yeah, looking at at knowledge, like, saying that your data is gold, and whether it's your data or your internal knowledge is really the value and the potential that's right on your feet.
Speaker 1:Definitely. I think this is a good time prior to wrap up unless you guys have anything else to add to it. But I think, you know, we wanna encourage our listeners to really think about, you know, you can start small and you can either audit your existing data yourself, identify those gaps, and then obviously partnering with somebody that can that can help you implement some AI solutions. And this really falls into some of our kind of workshop sprints that we do for our clients where we help with leadership to to do these kind of workshops in a usually a quick sprint to identify areas of where the greatest impact can be, pun intended, for your, you know, where we can implement these kind of efficient processes, where we can improve on your data, what you have, what we can use and integrate AI into your products, and that eventually will, you know, hopefully equate to more revenue because your customers and clients are more happier with what the output is. So I don't know.
Speaker 1:With that, I think that's a good point to wrap up, and wanna thank everybody for tuning in again for our 1st podcast of the year. And look for us, till next time. Like and subscribe as usual, and, we'll talk to you soon. Thanks. See you later.
Speaker 3:Thanks a lot.