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We take a look at the three things to overcome the FOMO that occurs when you see the one with the most information wins.

Hi Everybody, welcome, my name is Grant. So you're growing your business and you're trying to scale. In fact, you even have the data, but you and your executive team might be missing out on profitable insights. Welcome to insight FOMO right. So hey, there was a book written back in 1997 by Stacy M. Clements. I love the title. It was the one with the most information wins. Interesting, right? And in 2012 Tim O'Reilly was an interview think he was talking about his were conference back then. Anyway, he made this comment.

The comment was the guy with the most data wins in at the time I think he had referred to a you referred to back then the NBA, you know, the one with the most superstars wins, right? Well, today, the you know, the one with the most data wins in or is it the one with the most insights when right? You might have a lot of data, but you're not able to harvest the power of information out of it. Tim said something interesting, though, back in that interview, what is that? Almost, almost 10 years ago, he said, data is the source of monopoly power. That's interesting, you know, that sort of viewpoint course it means that it leans into the benefits of large companies, right? those organizations that have been developing massive databases over the years.

So what does that mean to an SMB, right to a small to medium business that's trying to compete? Well, certainly others think about this, and there's a movement out there called Open Data movement. In fact, if you check out data.gov, you'll see a whole set of free data sets. I don't remember the latest number, but it's 1000s of free data sets that various groups produce and certainly, you know, you know, give away Well, and while some of them have value, I find lots of times it's difficult to contextualize it. For your specific organization. I think the aspirations are great, though.

For for people, if you're able to leverage it, then then more parity, of course, I have found that there are three steps to help turn you around or tour turn your company around, as it comes to making data one of your greatest assets, right, let's say you've got insight FOMO, the FOMO is or the fear of missing out is that others are already leveraging and are competing, and maybe even be winning or beating you at this. So what are three things you can do to get in that game? Number one, it's start organizing your information today, right?

Your data today sounds simple. But in a previous episode, I talked about some of the things that go into that, take a look at the episode called control your data, or it will control you and that talks about the things to do for this first step number one, number two, well leverage an analytics and AI platform that and the goal here is it's to use a platform that provides I'm going to get nerdy here, the summation of many AI models, because one of the problems when you don't have such a significant set of data, as you might have, let's say, of course, if you're a Google right or a Facebook or who who have you, then it means that you need to do a lot more analysis on this to find those good predictive behaviors on it. Alright, so it's critical that that you don't get caught up in the world of amusing the most accurate data model, you'll actually you lose money in your analytics if you take that approach. Alright, so number two, again, just reiterate, you got to use an analytics and AI platform that leverages and summarizes multiple AI models that helps your predictive behavior even on smaller data sets. And Firstly,

As the SMB business owner, you're gonna want to do that. Alright, number three, so you're gonna want to leverage the experiences of analytics and AI teams that are already down the road. So AI is producing the foundation to leapfrog your competition. And in other episodes, I've talked about why what the level of effort is to take on the skills to become a data scientist, right? Or machine learning expert, right? 30, different skills and so forth. It's a lot of work. So don't do that, right. Okay, as the small to medium business owner, leverage the skill sets of others. So let's talk about what's at stake here. If you don't do those three things, and go after that, here's the FOMO part, Okay, I'm gonna lean into some FOMO.

What's at stake here is we're trading off these benefits, we're trading off the ability to reduce operational costs. If you do this analytics and AI stuff, right? You actually will reduce operating costs, you're also going to trade off deeper customer engagement. So the ability to get better, customer segmentation is a critical part of this, you're also going to trade off innovation doesn't mean you can't innovate without analytics, and AI, people been doing it for years. But the rate or the pace of it, and the insightfulness of that innovation in terms of being a better predictability in terms of where you should innovate. That's where you're going to trade off. You'll also trade off some top line growth.

McKinsey reported, I think this last year, they reported that 20% of the large organizations are increasing their sales Now, using AI. So top line growth is up for grabs. All right. And then last but not least, certainly least in my list here for right now. Competitive posturing, right. This is a leapfrog approach in technology today. And so yes, you will trade off that meaning you will not get as much competitive posturing, if you In fact, don't do the three things that I'd mentioned a moment ago, all right. But to reiterate, some of the tools and techniques right to do this are very expensive. And as a result, it makes it difficult to get the ROI that you need. So this is not about being customer centric.

This is about being customer conscious. And that's a critical part of this. So if you're going to do this, make sure that you go after the three techniques that I've mentioned, otherwise, the costs will become prohibitive, and it'll be hard to get the ROI. And again, this is not, hey, we're going to be more customer centric, you can do that without applying these analytics and AI. But if you're going to be more customer centric, or excuse me more customer conscious, that means you're going to iterate on a more frequent basis, you'll be able to look at ways to pivot more as your customers are pivoting, right? You're the ability to anticipate emerging patterns in customer behavior. And a pivot while they're pivoting is key to this, right. So part of this is mindset got to be thinking that way, if that's an important part of your business.

Now, this means that you're not only going to be applying analytics to your business, it means AI is informing those analytics. Now, many analytics, as you know, can be backward looking, right? It's a view of what got you to where you are. And that is a helpful view. But when you apply AI to those data analytics with the right platform, again, you're not only going to get the predictive insights, but you're also going to get the insights that are best for your business operations. It doesn't help as a small business owner, if you get some insights that cost so much or require so much change, that your business operations can't even afford to do that. So it's not about just having the most accurate model. It's about having the right model with the right level of predictive behavior that allows you to make incremental steps to move forward.

So FOMO grows, of course, as we discover that the businesses around us are preparing the organizations to leverage these capabilities. And let the FOMO begin because that is happening. Now. At the end of the day, you know, the one with the most insights wins, as long as they act on them and stay continuously curious, that is the critical piece. So he can do insights, even if you do all the things I talked about here, but you don't actually act on it and put it into operations, then it becomes a moot issue. All right, everybody. Thanks for joining in for next time, fix your FOMO and start preparing for predictive insights to grow your business.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at controlling your data. And what happens if you don't!

Welcome everybody to another episode of ClickAI Radio. So just about everywhere you go, there is some sort of data being collected on you or about you, right? Whether you're online, walking down the street, wherever it is now, or even driving your car, right? Even, you know, your your mobile device, obviously tracking where you are. And that does sound a bit creepy, right? It brings with it a whole new set of issues. But let's talk about this principle in your business. What kind of information are you gathering in your business? So, you know, for example, do you know what information you are gathering? And do you know where that information is in your business? And do you have duplicated information in your business? And if so, which copy of that data is the correct one? Well, there are some benefits, of course, to organizing your business information. One of the reasons that we do this is to help us understand predictive activities of your customers of your costs, and even of your operational efficiencies. Now, some companies capture a lot of data about you certainly we all know things like Google or Facebook or Apple, how they use the data is really the critical topic. So you'll recall a few years ago, the Attorney General of Massachusetts announced an investigation into Facebook and the data firm, Cambridge Analytica, this was I think, back in 2018.

Now, in this case, the allegation was the Facebook data was misused in a political campaign. Now, I'm not here to dispute that. But what I am here to say is that getting control of your data, hence managing it, and using it in ethical ways, to course better serve your customers and improve your business, this is the right thing to do. And that's the area that I want to focus on here with you. So what are some of the activities that you should consider to get control of your data? Well, here's a list of seven items. This comes from lead space, which I mostly agree with. So let's take a look at these seven items. One of those is very first, outline, number one, outline your business goals. All right, so you might choose to increase your sales or, or you might, you know, hey, I want to improve my decision making process in my company. Or you might say, you know, you know, what is the common theme that's taking place across across all of my customers, right, in other words, you might want to improve your customer segmentation or try to find their buying habits and patterns, or perhaps your business goal is, you know, you might want to improve your lead management or, or maybe improve the efficiency of your internal operations. I work with a lot of companies. And one of the common themes that I see across these companies is poor decision making.

Now what I mean by that is, it's not that they themselves are making bad decisions, it's that there's not consistent decision making processes and therefore sometimes the decisions are great and other times, maybe not as good. So having consistent insights to guide and influence your decision making process, that's certainly a worthy goal. Alright, so that's number one. So if to get control of the data, my business, think about outlining your business goals, choosing that first number to prioritize data protection and security. Man, we all know about the breaches that we hear and see where hackers get in and take data from organizations. You know, when you talk about data protection and security, no one really wants it or we know wants to talk about it, and until it happens to you, right? So it's not typically that exciting for most but of course, if you don't do it, it's challenging to recover from these data and security breaches. So when you're applying things like AI, it's critical to protect data, as well as information, privacy information about the people themselves. And so this is a really critical area around digital ethics also. Alright, number three, focus on data quality. Now this is a constant challenge, right? controlling the ingestion or the input of your data and its quality, all the way through to consumption is a challenge.

I was working with one group and the clerks at the retail stores, they were putting pricing information in a wide range of locations, and it made it very difficult for the business people to run their business. Now today, there are some awesome tools that help to clean and scrub your data. But nevertheless, data quality absolutely critical. So that's number three. Number four, reduce duplicate data. Now, as you know, lots of times we'll have multiple systems in our business, and they overlap in terms of their capabilities and what it is they do and that produces multiple copies of similar data might be customer information or address or what have you. And of course, you know, when you think about that you're struggling with Well, which one of these is the source of truth, right? Which one do I want to turn to and say, This is the master, if you will. So while you might have multiple systems from multiple vendors for your business, it helps to think about your data as a flow through the business like a river, right? So it comes in from data ingestion, to Hey, I'm going to curate it and clean it to him, I'm going to reduce, I'm going to actually get rid of this duplication. And finally, get it to a point where you can do analytics and AI and things like that, to help improve your business decision making now, if you have the ability to reduce the actual number of duplicate or similar systems, that helps tremendously with this as well, certainly can take a lot of money off the table for the business also in terms of expenses.

All right. Number five, ensure your data is readily accessible to your team. Well, yeah, what can you say, you know, having the right access controls to the data preventing others that shouldn't get into the data is just common sense. Number six, create a data recovery strategy. It reminds me earlier in my career, I've worked with one client who had a significant system crash. And so they went to their backups. And well, yesterday's backup actually had failed, they didn't realize it. So they went back to the day before and the neighbor and then they went a week back and then two weeks back, and then a month back, they went two months back and finally found a backup that was valid that they could use. And so they lost two months of data had to do a lot of obviously re re entry manually back in those days, right? So of course, it's not just about backing up your data. It's about the recovery. So this is called a data recovery strategy, not just data backup, but data recovery, right? You want to constantly go back and re verify, can I get back to this information? Is it in a valid state so that I can continue to serve my customers? All right. And then last one, last but not least, number seven, use a quality analytics and AI platform for insights.

And of course, you know, I got to toss that one in there, right? I'm a fan of good analytics and AI to guide decision making. It's augmented intelligence is the way that we look at it the way I look at it. But at the end of the day, you want your data to unlock the insights and the guidance to propel your business. Alright, so let's take a step back. So these seven steps identify certainly some of the things to do and, and it can feel like a lot of work. And while it can be but think about the other side, if you don't do this, what's the impact, right? While it can impact your business growth, it can impact the quality of the decisions that you're making, it might even impact the security of your business and your data can be compromised or stolen or take your business all the way down. So recently, I was talking with an executive at a major bank, and she made an interesting statement. She said, data is our most important asset. And then she stopped and paused and corrected herself. And she said, No, it's our weapon. All right, that's the power of data. So when you're controlling your data, you're really taking control of your business, which is really one of the greatest assets or might I use the term weapons for your business to help it compete and grow. Alright everybody, thanks for joining. And until next time, get control your data and turn it into your best business asset.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at the anatomy of growing your business one step at a time with AI.

Hi Everybody this is Grant, thank you for joining another episode of ClickAI Radio. So when we think about transforming our business, right, any sort of transformation of your business, obviously requires a vision of where you want to go. And certainly a commitment to make that change, of course requires more than that. But those are two fundamental pieces, obviously. Now managing the change, of course, is one of the more difficult tasks for a business owner. Now of all the things that I could change in my business, which one should I pursue? There's always tons to do in our business.

And the other question is, how do I even prepare my team to handle the change? And then how do I make the chain stick so that the organization continues with it over some time? Oh, and then one other question, now that we've made the change, what should be our next change? Right? What are we going to change next? If you've ever watched the Star Trek next generation series, I'm clearly dating myself here. If you ever watched that, there was a character on there called data. And data was brilliant. data was an Android. and data knew a lot of facts. He was fully functional in a lot of ways. However, one of data's downfalls was his awareness of how much information to give a person.

There'd be times when the captain Captain Picard would ask a question, and data would ramble on with long answers. data would go on and on and on, obviously, being unaware of the human's ability to either remember the information, let alone to be able to assimilate it, and then to act on it. But AI is a bit like data in Star Trek, right? It can, and often does give a lot of information, meaning many, many insights, it can go on and on and on. And not all of the insights are of the same value or quality. Now, while this might seem useful, of course, it's obviously overwhelming.

Not long ago, I was applying some AI for a beauty product supplier, and the amount of insights which came from their data was enormous. And the question was, where to start, right? That's a lot. It's too much. any effort, as you know, to improve your business using AI? should start with your business questions first. So it's one thing to just start looking at your business information. But really, you got to start with the question, Where do we hurt? Where are the pain points? What's the vision where we trying to grow? And of course, we still want to leverage AI to discover those things, that it's hard for our brains to see.

But this is a critical step, it's a necessary step where we guide the AI in terms of the business questions that we have first, and it should serve as input to where the AI should focus. Now, even when you do that, there are generally many insights which are discovered. So the real question is, therefore what? Let's say that you've done that work, you've got your business questions, you got some AI insights. Now what what are you going to do with all those insights? So it was on this journey, we discovered what we call a smart step. So when you're making an adjustment to your business, right to apply the AI insights, you're looking for the smart step. And a smart step has these characteristics to it, right? It's anatomy includes these things.

The impact, the insight needs to have high impact, of course to your business, it's got to have high relevance to your business problem, as well as high probability, right? Not all AI insights are the same level of probability. You want at least those three but in reality, there's one other key point that you want, you need to select a change that your business

In this and your team is willing to make, I've seen multiple organizations who go through this process, they get the insights, it's relevant to the business. But you know, this is the way we've always done things, people start thinking, right? Why would we make the change, right? And so this becomes a big hurdle for a lot of organizations. So you apply AI insights using smart steps. And one of the reasons why is not only to deal with all of the mass of information so that we, you know, we don't go through this Star Trek data scenario, right.

But it's also to avoid change fatigue, organization can get overwhelmed with too many changes to make. And as a result, it's hard to measure the effectiveness of a smart step if you do too many of them. So in reality, over time, you mold and groom your business in a series of successive smart steps. So the flow looks something like this, you're on your business, which of course, produces business information. And then you analyze your business information, leveraging AI, then you pick one or two smart steps from the analysis, make the changes to your business, that's your sounds easy, doesn't it. And then, of course, you run your business with those changes in you give them time to stick, and then you repeat that process.

Now, we have found that this style of iterative and incremental changes to your business, where you leverage the analysis from AI like this, that is sustainable for a business, otherwise, it's too much now, maybe a way to think about it is as a set of stairs that are ahead of you. But in this case, you decide as the business owner how wide each step is, and therefore, by making that decision, you're also deciding the angle of the staircase, by literally changing the course just the width of the step.

So for example, you might apply some AI insights as a smart step in your business and execute for six months, that would be the width of the step, right, you're gonna take six months to see the results of this. And then while you're doing that, of course, you are capturing more business information. And now it's time to at the end of that six months, say, we're gonna let the analysis from the AI give us some additional insights. And then we'll pick one or two smart steps, and we'll repeat and apply those, of course, you might decide to make the adjustments on a quarterly basis, it's really up to you. It's your business's ability to handle the change, as well as your ability to respond to disruptive behavior.

And so sometimes the change is intentional, right? It's part of our vision where we're going. In other cases, the width of the step, right meaning how long we wait, before we make the next change is placed upon us right there. It is put upon us by other factors, of course, that are outside of our control, might be the economy might be competitors, might be a pandemic, could be legislation, new taxes, whatever it is, so on and so forth. The key is, is to take those inputs that come upon you, and then change the width of your step, right?

Don't wait as long and then make some more incremental steps. Do them a little more quickly, and then you might spread it out over time. So I'd encourage you to consider using smart steps to grow your business. It's where you take AI or the insights from AI in bite sized chunks and you decide the size of those chunks. All right, everybody. Thanks for joining and until next time, apply your AI and smart steps.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In This Episode we look at the question: When do I start trusting AI?

Hi Everybody, welcome to another episode of click AI radio. My name is Grant. Alright, so been looking into this problem around trust. And what is it that gets in the way of us leveraging AI? Well, part of the problem comes from some reports. Right. So we'll look at a couple here. One of those was a report from Pegasus and the other was from pega systems, I should say, Now there was a Harvard Business Review report. And of course they they capture what people what we think about in terms of challenges around AI plenty of horror stories out there for sure. Can we actually trust this stuff? We all know about, you know, the Chinese board game go and how AI beat beat the Chinese board game player. kg. I hope I'm saying his name right? Back in 2017.

ertainly machines can do a lot. And that's, of course, what we expect what we need them to do when we look at the Harvard Business Review. And what it is that they pointed out some of the challenges around this in in the Journal of marketing, they said based on data from over 3000 people who took 10 experiments, they provide some evidence, all right. And so here's here's the evidence, right, so there's the word of machine effect. And that stems from a widespread belief that AI systems are more competent than humans in dispensing advice. That's kind of an interesting viewpoint, especially when you really think about what it takes to build an AI model. It requires multiple layers of a network, typically, to help create decisions.

Now, that's typically too much for us to think about as business owners. And generally, you don't want to have to get to that level of detail. One of the problems around AI, those are expectations of it, given all the hype that goes on out there, one of the best things I found that helps us to work through this is the mental model of viewing it as augmentation intelligence, rather than as artificial intelligence. I think sometimes that misnomer gets us into problem. And we expect it to do more than it than it really should. In fact, one of the best practices around this that I found is, as you're working through making decisions, and you're getting insights from an AI model, you need to compare it against reality.

Certainly there are contextual things that the model designer may not have known. And of course, the more that you can get context into the brain of the model designer, the better it is for your business. But that doesn't always happen. So you need to apply the human cognitive sense and the sense of reality that the humans can pick up that the AI is not able to. So what what's critical to this as it becomes more of that collaboration, if we view AI as I'm going to spend time giving feedback into the model to inform it of ill advised insights, or things that just didn't work out. We need the models to be able to learn and grow and make adjustments over time.

So coming back to this, to this HBr review, Harvard Business Review, one of the things that they pointed out was convincing a customer to give AI the benefit of the doubt. All right, so Okay, I think that might get a little bit too Ryan's talking about a moment ago, which is we need to look at AI as augmentation to our insights. And one of the key areas, of course, to think about is those ways in which AI can see things that's hard for us to see. That's really where we should should leverage the capabilities of AI itself. So I was working with a business owner recently who made the comment. Well, I'm not sure that my people will actually trust The insights from this, which I find interesting, and then there's this other group that I interact with and work with, they provide an AI platform that I strongly recommend. It's one that we've built and based our stuff on.

And, and they made this interesting case study. Recently, they said that, that one of their clients had taken an AI model, and had deployed it and put it into production and hadn't actually told anybody. And what happened is, is over time, people started to notice this score, and the score was integrated into their system. So they were making these these selling decisions. And they started to draw a correlation between when a score was higher, the sales opportunities, they found out, were turning out to be better. Whereas when the score was lower the sales opportunities, were not as good. Rather than tell their employees, you must use this, they just put that into the system and let people start to observe it.

And then they started to derive their own conclusions from it. And so rather than this top down approach of you must use this, they took this organic approach that said, let me let me put this in front of them, and then let them experience the results of it. and discover if if what the insights are giving reflect the reality and in fact, augment your reality in a way that it builds your confidence in that system. And so it's a critical way to approach AI. It's not to look at it in a dogmatic fashion, but rather look at and I love that case, study, look at it in a way that says, Let's, let's leverage and build on what we're already doing. And then let's verify is this augmented intelligence actually benefiting and restoring and building our growth and trust in AI itself? Simple example. But I think it's a very telling example, for sure.

All right, trust is built over time. We know that certainly with humans, and I believe with AI as well. So though the approach here, get some AI going in your business, do it low key low cost, don't push or force it, and let the people in your team start to experience the benefits as well as the downsides of it. Learn together, refine it, over time, that trust gets built, and then he starts to see the results that will actually increase your business outcomes. All right, everybody. Thanks for joining and until next time, build your trust in AI.

Thank you for joining Grant on click AI radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at why AI is ruining your business.

All right. Thanks for joining. So how is AI ruining your business? Well, certainly over the years, I've seen several ways the AI ruins your business today, I want to focus on one of them. So first, let's take a look at a report. It came from MIT Sloan Management Review. This was last year in 2020. And they were quoting a study by BCG. That's Boston Consulting Group. And here's what the result was the result of BCG study was that a mere 10% of organizations achieve financial benefits with AI. Holy smokes. That's that's quite the conclusion. So why do only 10% of businesses achieve financial benefits with AI?

Well, the ROI to AI gets wrapped up in at least two key areas. So the first is the technical foundation that's necessary to conduct AI. And second, the business problems on which AI is applied. So let's let's take a look at the first one. So what does it take to create a technical foundation? And how do many companies do this today? Well, first of all, one of the things that happens is they start hiring data scientists. And of course, you need data scientists in order to do that kind of work. So that's the first thing. The second is they begin doing AI model creation. Now, this is typically a multi month effort for these organizations. All right, moving on to number three, well, while they're building out their technical Foundation, they start deploying the AI model to something like Amazon or Azure, or Google or something like that, right.

And it turns out, that at this point, about 50% of the models actually don't even get deployed. All right, because so far, they haven't been able to find a good, a good application for that. So at that point, you're down to 50%. And then number four, now you start on working for user adoption, hey, let's get the people to actually start using it. And by this time, your model may be out of date, right? So needless to say, by the time you do all of this, right, you've invested hundreds of 1000s of dollars, if not actually millions of dollars. So again, where's the ROI? Well, that's one of the reasons why only 10% achieved some financial results, because you're putting all of that investment upfront on your business, right? That's very expensive way to do that. That's crazy. Don't do that, there's got to be a better way.

And there is, and I'll come back to that here in just a moment. But before we do that, let's look at the other reason why AI has ROI problems. So it has to do with the business problems on which AI is being applied. Now, as you know, as it turns out, the majority of organizations start running all of their machine learning algorithms, without clarity on the business problem on which they're focused. So what that means is, they're looking for highly accurate AI models that in fact, aren't actually going to work well for your business anyway. And that is, well, that's bad business. So the net result is, ai gets a bad rap, because sometimes as business people, we get too excited about eye candy, right?

And so as you know, there's got to be a better way and of course there is so here's a couple things to think about. Right? Instead of making those mistakes, and ruining your business with AI to address the first problem, right? So the first problem was, gosh, darn, this is expensive and holy smokes. You know, I'm buying expensive AI resources. As a small to medium business owner, you're not going to do that. So don't get caught up in the world of expensive resources or building out your AI team, or expensive AI tools. So if you do go that route, then getting the ROI from AI is quite difficult. As you know, one of the best business rules is to use leverage. So leverage the skills of people have already done it with the platforms that have been tried and true. Alright, so that's overcoming the first problem to address the second problem, which was, hey,

there's no focus on the business problem, right? It turns out that if you do this first, right, if you focus on the business problem first, it's actually the best thing to do. sounds cliche, sound sounds trite. Always start with the business problem. This actually influences the AI model development. So as you identify not only what's the business problem, but you also need to identify what parts of my business Am I willing to change?

Because it turns out the cultural piece to this is just as important. If not, if not more important, you might have a business problem. But if you can't figure out how you're going to get the people to make the adjustments to take the insights and make the changes, might as well not do it, right. So Furthermore, identify that your business is willing to make the change a Have I got the right business problem, be are willing to make the change. All right now go apply the AI resources, but don't go build out that dog on team, right. So if AI gave you some insights, and you were willing to make the business adjustments needed, now you're in a good spot to actually get some ROI from your business. So it's critical to identify the business problem first, and to do it in an area that you're willing to make a change. All right, then let that serve as input to your AI model development.

Now, I promise you, if you apply these two rules, then you'll be in the 10% of businesses that are getting ROI from AI. Now it turns out that, in another recent study, this one came out of McKinsey, that 20% of the large companies are now getting financial results from their sales activities with AI, so the number actually goes up. So think about that. So you'll actually go from the 10 to the 20%, right? So if you not only just applied in general across anything, but apply it to your sales related scenarios, focus on the business problem.

First, focus in areas where you're willing to change, don't go build all the resources out and the skills don't build out the team leverage teams that have already built this capability. If you do that. You will not only get ROI, but you'll actually leapfrog your competition. All right, and who doesn't want to be a frog right? All right, everybody. Thanks for joining and until next time, apply the two ROI rules for AI in your business.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we take a look at the question. Is there a way to use AI to find my online content? This question was recently posed to me by business owner.

Hey, everybody, welcome to another episode of ClickAI Radio. It is good to be talking with you again. All right. So as I mentioned in the intro, recently, a business owner approached me and asked this question, he said, Is there a way to use AI to find my online content? Because I think it's been taken, I think people are taking my material. And is there a way to discover if they're a poacher or not? So what if that happened to you? Right? What kind of content do you have? That is online? And that potentially people could take in fact, how much of online content is plagiarized? Well, we know that there's quite a bit out there, but we know it empirically. But what what does it actually look like? I was looking at, at one site, they were talking about some some metrics, this was back in 2015. So this is a little old. It said researchers found that roughly the same percentage of papers included plagiarized text, were about half of the pre internet papers. So from their research, they came back and they said, You know what, there's actually more plagiarism in papers before the internet versus after that, I don't know that. I agree with that.

That's not what I'm seeing. Right. So I kept looking around, I found another group for this was, of course, in the schooling, and education sector, came back and said most college presidents about 55% say that plagiarism in students papers has increased over the past 10 years. That was just a few years ago that that particular research base, they also went on to say that among those who have seen an increase in plagiarism, 89% say computers and the internet have played a major role that came from a Pew Research poll. All right. So that that line a little bit more with what I think I'm saying, I searched out another one, too, that also confirmed some similar things. All right. So when you talk to some people most sort of nod their head, right when you say, you Oh, yeah, there's plagiarism that takes off. But the the, you know, that takes place, but is there anything that could be done to protect yourself?

So there are a number of solutions, of course out there today. And then the question is, well, what's the medium of the content? For instance, Grammarly, most of us know that site, that that solves the problem of, Oh, I'm developing a paper or a document or an article, and it will do the work to help you understand potential plays, or plagiarism in there. And there's a whole host of other sites out there for sure. The question I have for you, though, is, what if you want to find your content out there on Google or on YouTube? And let's say that your content was in the format. Not necessarily just the document, but it was perhaps something on your website, write some some particular findings that you had created or posted? In your content, even some of your course material, perhaps he created or even some digital material, such as, you know, on YouTube, things like that. How do you even scale to find something like that?

There are many Google domains, not just google.com, but Google dot , right? There's tons of Google domains around the planet. And so your content has this ability, digital content to grow legs, metaphorically. So how do you even scale to go find something like that? Certainly, you can do it once or twice, but then you run into pretty quickly that it's obviously very labor intensive. So as I mentioned, recently, this business owner had asked this question, and so what we did was we ran some AI to identify plagiarism candidates.

And the result for this particular business owner was that there were over 150 sites which has some level of reference either to him or to his material. Now, the next step, typically that we see in these scenarios is to identify if the reference is a friend or a foe. Now and the AI indicates right, it says while it thinks it's plagiarism or Well, not sure if it's plagiarism, but definitely, definitely referring to and and some of your content. The key is, is that most AI and the way we should treat AI, at least my opinion is more like augmented intelligence, right? And so you still need to be involved and say, Well, okay, the AI estimated that right?

Or no, you know, this actually looks like it's not a problem or situation. Now, in this particular case, about 40% of the sites turned out to be suspicious and required some level of pursuit to say, Hey, you know, you've taken my stuff, right. And, and, of course, the thing about this is this, this takes place on an ongoing basis. It's not like you run this once, and then you're done. It's that you run it, and then you pursue, and then oh, gosh, it grows more legs. And so you know, you're in it again, you continually have to chase these sorts of situations. So it's it's an ongoing sort of capability now, leveraging AI automation to monitor your content usage, especially this kind of scenario, right? work continues to, to expand and grow. And to find potential poachers, it's a great way to protect your business, right?

It's kind of like a business content insurance is maybe a way to think about that. Now, interestingly, we found in some cases, the sites that are discovered, turned out to be friendly, and are actually referencing you. And this has been an interesting outcome, it translates into an unexpected good outcome in that scenario to where it actually allows you to build your network and discover new allies and partners that you may not have known about. So applying AI in this scenario is interesting, finds potential bad guys and also finds potential good guys, so it sort of draws a line in the sand. Now, while plagiarism, of course, may continue to grow in the online world, right, there are definitely some cool tools out there today to help you combat that. All right, everybody.

Thanks for joining and until next time, get some AI protection for your content.

Thank you for joining grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, I have a guest who discusses nutritional AI that you can eat.

Grant
Everybody, this is Grant Larsen. Welcome to another episode of ClickAI radio. Wow. Today I have in the house with me. Mr. Daniel DeMillard, he is the CTO of FoodSpace, and I am just honored for the opportunity to meet him and to get to hear his story and what he and his organization has been doing with AI. Alright, let's jump into this. Okay, so, Daniel, first of all, welcome.

Daniel
Thanks for having me, Grant. Great to be here.

Grant
It is, it is really great to have you here and to have another another patriots in AI, right things that you guys are doing, as I got to know you. And before we started this recording, I was fascinated with the kind of work that you guys are doing and where you focus your use and application of AI. But before we get too deep into that, would you step back in time and tell us? Who's Daniel, where do you come from? And what is it that's brought you to this point to be CTO applying AI in such a cool way?

Daniel
Yep, so I actually studied economics and finance in school and came across an online class by Andrew Yang on Coursera. And at that point, I just absolutely fell in love with machine learning and artificial intelligence. And I was like, wow, this is absolutely what I'm wanting. Yeah, with my life. So, you know, started studying a lot. Got a job eventually at IBM Watson, and worked at a small company doing what type classification, then I was doing some consulting on the side, where I actually got connected with iosshare, Nike, the CEO of foods, but at the time, it was a lunchbox. And they were developing a consumer facing app that, you know, they were trying to pair people with recipes. And you could set up a diet profile for yourself, and instantly order things online through instacart, based on recipes that you find. And I initially got engaged with them, building a wine pairing recommendation application, where, given a certain recipe will automatically recommend a wine pairing that would go well, really well I

Grant
...need that certain kind of food, you're like, Hey, this is the right, right,

Daniel
...we're gonna wanna medium red wine with that right or a very sweet dessert wine.

Grant
Until it this where lunchbox started, they were focusing on solving that problem.

Daniel
They were focused on solving the one stop shop for keeping all of your recipes together, ordering food very easily. And then also being able to manage your diets, and allergens, and just make making sure all of that was really seamless. And they also had a great mission of trying to mitigate food waste, so that they could recommend given all of the stuff that you have in your fridge that would normally sit there and forget about they could recommend a recipe and for you to make of the things that you've already selected. And so

Grant
all right, very good. All right. So so then you bumped into them, and you started to work with them. Tell us a little bit about the transition over to food space. How did the vision change?

Daniel
Yeah, it was actually pretty serendipitous, and rather abrupt. So back in 2019, io was a part of grocery shop, which is a big conference in the CPG space and food industry. And he's trying to pitch this idea of lunchbox to brands and retailers to get them to sign up for it. And they all have basically kept telling them the same thing of like, hey, it's a great idea. It's super interesting. I would love to but what you're asking for with all of these like dietary profiles, and The information necessary to build these types of recommendation algorithms. It's we just don't have it, we don't have ingredients and digitized nutrition labels. And, you know, we have the very minimum. So given that that information is like, Okay, well, we've got to take a step back. So he calls me up. And he says, you know, hey, Daniel, would it be possible given a bunch of computer or a bunch of e commerce images, to extract the product information from there and actually read it from those images. And I had done a little bit of stuff with computer vision by that point, using, like pre trained, convolutional neural net models on image net, and then using transfer learning to identify key regions. And so I stopped and thought about it. And I was like, yeah, you know, I think the problem is tractable enough. And the technology's in such place, now that we can absolutely solve this problem. And so yeah, two years later, we now have a model that can classify things that matter of seconds. And we're gonna extract that product information and seconds with 99.7% accuracy 99.7. So if I bring I can assume to you, and you can see the label on it, you can figure out what this is, you can identify it. Exactly. So we'll extract things like brand name, product, name, net weight, the ingredients, the nutrition label, any certifications, such as whether or not it's recycled, kosher, non GMO, vegan, all of that awesome stuff, including marketing claims, like low sodium or, you know, contains less sugar, those those types of things, well, pull out all of the relevant information from the product label. And we'll read it in the same way that a person would read it. A lot of other products that do something similar or are entirely based on the universal Product Code code, the UPC barcode on the back, where they're basically just looking things up from a database, a database might have inaccurate information, it might be out of date, so might have been accurate at one point, it might have been transmis, transcribe with whoever transcribed it in the first place, we're gonna read that label and not image, the same way that a human would label it so that it's, we're going to the ground truth.

Grant
So PC based, you're actually extracting the actual text, you're figuring out what this is, and then the semantics of it. What does that mean? Oh, it means this ingredient, this so much in the, you know, in terms of the amount of the ingredient, and so forth.

Daniel
Exactly. And then we will also derive new information from that information that we've extracted, such as whether or not it's going to be had certain allergens. If it contains peanuts, we're going to let you know if it has a peanut allergy. We're also going to determine whether or not it conforms to different diets. So I'm a vegetarian. And I'm constantly reading labels for obscure things like whether or not my cheese contains rennet in it, which is a animal derived enzyme. So we will read all of that for you, and then derive whether or not it's going to correspond with with your diet.

Grant
So can you talk about some of the use cases around this? So are you targeting b2b scenarios? Are you doing b2c? Is that something that I as an end consumer comes in interacts with it, let's say through through my cell phone? or How are you? How are people going to consume this this cool platform?

Daniel
Yeah, absolutely. So right now, we are primarily focused on optimizing the e commerce experience. So if you're on Walmart, or Albertsons or target, and you are using your favorite grocery delivery app, or you're going in to do pickup, all of that purchasing decisions are happening upfront on the e commerce website. And at the very least, we want to make sure that that information is present and accurate so that you can make the decisions yourself whether or not at the very least, you can see that ingredients label and search to see if that rennet in gradient is there, or if you're trying to, you know bulk up make sure that it has enough protein or has low sugar, low fat, whatever your dietary needs are. We want to make sure that information is there. But we really want to enable a more optimized ecommerce experiences where you know in your little left side of the toolbar, you can select vegetarian or pescatarian or low sodium diet or a South Beach diet, or I'm allergic to shellfish, and automatically only be shown products that correspond to your dietary needs. So we really think that optimizing that e commerce experience and the search is where we can have the largest impact right away.

Grant
So So some of the health profile of the person intersects with this, is it coming off? Like, I don't know, like, like the fitness app? Or is it coming off of other sort of apps? And then are capturing that health information? How do you integrate with that?

Daniel
Yeah, absolutely. So right now, we are basically providing the data to the retailer so that they can make those optimizations. But certainly being able to integrate with, you know, My Fitness Pal, or Weight Watchers would help optimize these experiences. And we are in discussions with those types of companies as well to improve their databases. So that you aren't, you don't have to manually type in all of that information on your fitness app, you can basically just look it up in the database, and it's accurate and recent. One problem that we've seen is that 30% or so of data is of grocery products are updated every year. So anyone I think use one of these apps has the experience of typing in their information, finding out finding the correct product, but it's a little bit outdated, the calories are a little off the nutrient profiles a little bit off, we're gonna make sure that it's updated. And in the right place.

Grant
That's interesting. So you talked about accuracy, the model accuracy for AI? And I think you should say 97%. Right. 99.7.

Daniel
Yeah, we are absolutely religious about that is, wow, you know, that is the problem that we're trying to solve. Right now, if you look up any product, on, say, a large, very large e commerce website, like walmart.com, there is a somewhere between 40 and 70% chance that there is at least one mistake on that website, regarding just the ingredients and nutrition information. So if you're trying to base you know, your health profile on that, it's it's an inaccurate, so we are just absolutely religious about getting every single piece of information. Correct, at least as so far as it corresponds to the product images.

Grant
So is this is this just for humans? Or is this also food for animals and pets? And how does this work?

Daniel
Yep, so we've definitely just, we started with humans, we are expanding to pet food and being able to build attributes around that two things like wet versus dry pet food, whether it's for a large size dog, or a small size dog. And all of those attributes we're hoping will also assist in that product search and discoverability so that you're not being shown a dog food, that's, you know, too too big for your small small dogs. Right.

Grant
Right. Right. Okay. All right, that makes sense. And then in terms of what we're talking about, who it's relevant to terms, your current market, so it's for humans, obviously, animals in the future. But as we think about the humans, this English base, is it other languages, Spanish or Mandarin or others? Where are you in terms of multilingual?

Daniel
Yep. So, you know, food is, I think, sacred to everyone everywhere. And as we move from this, in store grocery experience, where you're, you have the product in front of you, you can pick it up, and you can read the label to an e commerce experience, where somebody might just be dropping that off to your doorstep, and you don't see the product until it's there. We really think it's important that we have as larger reach as possible. So we definitely are working on expanding our algorithms to apply to different regulatory regulatory environments. You know, Europe has, I think, 12 allergens, whereas the united states currently has nine, and they just added sesame, to their allergens. They also have different nutrition labels and different information that they require to be on those. And then in addition to that, the different languages that are actually present there, and all of that obviously presents different technological issues, custom models for each of those markets, but really what we've spent a lot of time Building and working on is creating models that can quickly adapt to these new domains and building a really robust training pipeline. So that basically all we have to do is collect more data, instill a little bit of domain expertise, where we have to learn a little bit about that new market or that language. But after that, we can adapt our models very quickly to that new.

Grant
You know, I just have to ask, given that I love the AI piece of this, as well as just the benefit that you're bringing to human family. I mean, that's, that's huge. When I think about the AI portion of this, I think, how, how was building that model? I mean, how you have a lot of cans in your food storage now. I mean, how much? How many boxes of Cheerios did you buy? I mean, that's amazing. How did you get through all that? That's just that, right? There is a big challenge, right? To get through enough instances?

Daniel
Yeah, um, I Oh, and Dan, my business partners, they spent a lot of time getting kicked out of grocery stores, because they kept picking out prod products and taking pictures with their phones. And so they were kicked out of a few grocery stores, I think they learned to, you know, explain what, what they were doing their first after a little bit, but certainly a lot of time, taking pictures of your entire pantry. Going around the grocery store, just pick it up as many random things as possible. That's creative.

Grant
Yeah, that's, that's really great. If you have any particular challenges in terms of the kinds of food and other words, some things don't have labels, right. So certainly asparagus typically right or decent, things like that. So how do you deal with that?

Daniel
Yeah, absolutely. So currently, we only support branded foods. So it does need to have that product label. But it's interesting that you should mention certain types of foods, we were doing a analysis an audit of our accuracy. And we were noticing that a certain product category, yogurt, in particular, was creating a lot of issues for us and was very low accuracy. And it turned out that the curvature of the yogurt container, and then the fact that it kind of tapers down, creating a lot of issues for OCR model, where the text is kind of getting bunched up at the edges of that, you know, yogurt container. So we actually had to like build a specific model just to handle those types of containers. So certainly, you know, a lot of our time and effort has been focused on the corner cases in those weird scenarios where that are particularly difficult. The like, very simple run of the mill cereal box, where it's a nice rectangular box and the nutrition labels very prominent. And it's a very usual format that's easier to solve that most of our time has been focused on these weird one offs, like these tobert, tapered yogurt containers.

Grant
So so let me think about because I love the, again, this problem that you're solving and how it benefits people and their dietary needs. When I think about how people can consume this, what's the way that they will be able to interact with this standard? And what's the state of what space is doing today? Is it? Is it out there ready to be used? Or Where are you guys?

Daniel
Yeah, we're currently working with brands to get their data to the retailers and some retailers are a little bit further along than others and optimizing the, you know, experience for you where you can set up those dietary profiles for yourself and only be shown the products that correspond to your values, or do you only want organic food or you have a gluten intolerance, only being charged on those foods that correspond with those values or dietary needs to just getting the product information out there to the retailers in the first place. We're also working with some initial engagements with smart appliance manufacturers, things like smart fridges and smart micro microwaves, where you can simply scan the product, either using the barcode or just the front of the product and instantly have your oven or your microwave set the time timer or the temperature for you to cook that product for you. Additionally, being able to do things like recipe planning based on the products that you have in your fridge, being able to order products from I'm a retailer directly using the feature on your fridge that is based on your dietary profiles and just you never needed to get on your computer. And you could just order, you know, your gluten free pizza directly from your smart fridge that is linked to a product database with information that we're providing, we really think that more and more people are going more and more of our purchasing Our food is going to happen in this virtual digitized space, whether that's through your computer, your smart fridge, and the more that information is available, the more that we can build a more customized experience, and really make shopping easier as well, so that you aren't ever being shown products that don't correspond to your dietary dietary needs or your values. You know, even being able to set timers and things for microwave, it might sound trivial, but it really should make the entire cooking, cooking experience that easier for you.

Grant
You know, I certainly could benefit from walking up to the fridge and say, what are the possibilities of what I can create from what's in there, my wife will do that she's got that AI model already in her head, but I don't have that model, same set of food and go, there's nothing in there. And then she can craft you know, miracles out of it.

Daniel
So yeah, I'm the exact same way. And, you know, you could you can set user profiles for everyone in your family and say, Hey, you know, I'm a pescetarian. And my daughter's gluten intolerant and my son really only it's organic food, and being able to mix and match all of those constraints, we can figure out what recipe and you know, what to eat for dinner, right?

Grant
And so it sounds like, like, like, we've done that, that South Beach diet multiple times. Sounds like you know, you can literally walk up to your, to your fridge at some point and say, Hey, what is it that I can make that is in compliance with the South Beach diet?

Daniel
Exactly. And then things like, you know, macros counting, like calorie counting, and counting how much protein that you're consuming, would be a lot easier using if all of this information is digitized, and you're interacting with it in a smart fridge type environment where it can track what you're picking up and making. So I think entering information into one of those calorie counting apps is often a pain and I think, a limitation for a lot of people. So anything that can mitigate some of that barrier to getting healthier and keeping track of what we're putting in our bodies, to me is very much welcome.

Grant
So we've talked about the art of the possibilities around this right? What is it that this can bring the people that dramatically influences and impacts their health? What do you see in terms of the downsides? What hurdles or challenges? What could get in the way of either people adopting this or getting value from it? What what concerns or challenges do you see there?

Daniel
Yeah, so some of the things that we've seen in the industry about the difficulty to use this type of data is, every retailer kind of has a different format for how they ask for data. Some retailers want the units and the nutrition and the value to be separate. So if you have seven grams for protein, sometimes they want us separate key for seven and a separate one for brands. They might call things different. Some people might think call things, UPC, other ones call it barcode. Other ones call it product ID. So that's some of the work in transit translating the data mapping or the data model to each of those retailers can be a major bottleneck for a brand say wants to get their data to Walmart to Albertsons to target. And they basically had to look at these like massive Excel spreadsheets, but like 70 columns or 150 columns, and manually copy that data over and it's a huge pain. And that that is one of the major reasons why only the largest of brands have the resources to get their data digitized in the first place. So what we do is, you know, we're going to first extract that information for you automatically from your images. You don't have to hire a team of people to do that extraction in the first place, where we've also built these mappings for the top 10 retailers where we can automatically syndicate and get the data in the format that they want to see. Whether that's directly through an API, and just automatically updating your information through an API, fortunately, some of the grocery industry isn't quite as forward thinking. So a lot of updates are just made through Excel spreadsheets. But we'll create that Excel spreadsheet for you. So that it's basically just a matter of sending that over an email. And I think that should mitigate a vast majority of the bottlenecks currently faced in the industry. Because some of the, I could just imagine being a brand manager and be like, Alright, well, here's my data mapping. But then there's these close lists for Walmart, where, you know, I'm supposed to put in a certain beef cut type for this product. And doing that, for every single one of my 150 500 products, that is going to be a huge ass.

Grant
Yeah, it has said that. It's one of the things that drew me to this. And when you and I were first talking about this recently, which was, I feel like the work that you're doing is not only scales to the larger brands, but also it's pulling out all this information that makes it available, even the small to medium business space as well. And so feels very scalable, therefore approachable to benefit a lot of people, lots of different scenarios.

Daniel
Yeah, absolutely. And we try to make things as easy as possible to get integrated with our system. So, you know, our simplest use case, if you already have data and a list of URLs for your product, you're going to send us over a CSV with your URL links and the product IDs associated with those. And we'll download those images for you and process them through the system. And now you can download it and whatever data format you want, you know, CSV or JSON, or an Excel or in target specific taxonomy format, or Walmart's or Albertsons. Or you can upload it through a, you know, drag and drop upload portal where you can just drop, drag a folder of your product images into that upload portal, interact with an API, or even give us access to your put them up on an FTP server and point us to it and we will download the images there. So it's really trying to make things as simple as possible. So that whatever your tech stack is, and whatever the size of your organization is, we can help you get up and running as quickly as possible.

Grant
Lots of integration strategies for if that's powerful. That's awesome. Alright, so let me ask you, if the for the people that are listening to this, where are you going to direct them to what's what's, where are you going to invite and where do they go find out more about this?

Daniel
Yep, so a FoodSpaceTech.com is the place to find all of the information.

Grant
Okay, FoodSpaceTech.com; Awesome. That's great.

Daniel
Okay, we actually just released a brand new website. So it looks great. And you can look at it now.

Grant
It looks awesome. I've asked you a ton of questions. What questions Haven't I asked you? What would you like to share that I haven't prompted you?

Daniel
Yeah. So I think that can be skepticism and the world of AI. And, you know, whether or not we can do what we say that we can do. And we are, again, just absolutely religious about product accuracy. And I think it's good for anyone who knows a lot about AI to know that AI can only take you so far and the machine learning is only going to get you so far. So we've spent a very large amount of our time building a very sophisticated human in the loop process, were really trying to figure out where the ML system is doing well and can be trusted, versus when a human needs to come in and take the reins and make a more educated more critical thinking decision about things with things like building known rules between the nutrition label. So calories is a very direct calculation from total carbohydrates and protein and total fat. So we can basically just cut check to see if that calculations done well. We can cross check our nutrition information against our ingredients where we've actually built models where we can predict certain nutrition elements based on the ingredients. You know, we know that a cookie were the first ingredient might be butter or sugar is going to have more fat content than something where the first ingredient is carrots. So if anything falls outside of those ranges, we can alert it and say, Hey, something's gone off the rails here, we should make sure if human takes a look at it. For certain container types, we know we are struggle a little bit more things like that yogurt container. So instead of relying on the ML models that work most of the time, but not all the time, we can just flag that certain product type for review by a human just to get another check on it. But we really think that the just to solve a problem, at least in the near term, using AI involves humans in that in the loop and being able to really distinguish that the easy cases, the happy path that I like to call from, hey, we've seen a new domain, you know, maybe it's a it, both English and Spanish is written on the back. So our models are getting a little bit confused. Let's flag that for review.

Grant
Yep. Yeah, I really appreciate the qualification around AI. I tend to prefer to think of it as augmented intelligence than artificial intelligence, I feel Yeah, I feel like that's the state of where it really is. There's so many things out there, like, Oh, you know, ai robots and Terminator that give a real misperception. But, but today, this stuff around deep fake, right, is really starting to become, you know, a bit of a challenge, right, in terms of creating even less trust around this. So it's a real misuse, if you will, of that. So in this particular case, this is obviously real, honorable use of AI itself. But the whole if we can keep people's perceptions to this is to augment your thinking process, right? Your cognitive behavior. So even though it's coming to you and saying, This is what you could or should eat, or this is what makes sense, you know, from a nutritional value, you still own the responsibility yourself, right to end up saying, Yeah, this is something I'm gonna do, I'm not passing that off, you know, to the AI model and say, do all the thinking for me, right?

Daniel
Oh, absolutely. And I could not agree more, you know, we are just providing the information to you. But it still requires that critical thinking and decision about your own values and your own goals to make the final decision about what you're going to put into your body. Right. We're just trying to make that easier. Make that whole decision process simpler. Yeah. Powerful.

Grant
They're very cool. Okay. All right. Any last comments, before we wrap up here, Daniel? No, it was a great to be here at Brampton. Thank you very much for having me. Yeah. Thanks for taking the time and for sharing this cool platform that you've put together everyone, go take a look at food space tech.com. Thank you for joining and until next time, go get some nutrition.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

What I didn't know that I didn't know cause my very first startup company to fail.

Thanks for joining, this is Grant. So we started this company, I was right out of college. And as you probably recall, I turn to my wife and said, Hey, I'm not going to take the secure job, I'm going to go do this, this startup company. So we headed to Chicago, and we started working on this company, the focus of the company was to deliver some technology solutions to the healthcare world. That's that's honorable, right? Well, as we all know, in the world of all possible knowledge, there's the sliver of things that you know, and then of course, there's there's the section of, you know, the things that you know, that you don't know, and then there's this big wedge of the things that you don't even know that you don't know. Well, what we did know was that my main partner, he had years of healthcare experience and tons of connections.

So we knew that. All right. And then what we knew that we didn't know, was we weren't familiar with the healthcare payer details, right? We were focused on health care providers, right and the solutions around there. But what we didn't know that we didn't know, that was the level of investment that these health care providers were putting into technical solutions. It far surpassed our ability to support, right, so just a couple of us guys coming in saying, Hey, we're gonna go solve this serious healthcare problem for you. Yeah. Now, the other thing that I didn't know, that I didn't know, was that my partner was unwilling to accept input, he was unwilling to change course, and align with the market better, right. And so that unwillingness to change, that's a common challenge for many SMBs. Hackett's common challenge in big companies to, but the ones that pivot both the large as well as the small companies, that pivot, right, that shows some willingness to adjust course, with some market input. That's a critical skill set for business survival and growth.

So of course, the key is to be open to change. And there's a few things to consider there. Number one, commitment, right, make a conscious commitment to be open number two, trust, trust that you're heading in the direction. Not that you want to say is right, but you're always heading in a direction of new knowledge, right? second, or third curiosity. So allow yourself course to experience the curiosity of not knowing, right, some that sometimes we get uncomfortable with that we want to stay safely in the things that we know. But curiosity is actually critical to the business pivoting and growing. And then fourth, is around freedom. And that's basically the freedom of experiencing something new and unexpected. So getting open to change is a critical part of any SMB. Now, you know, dive I have plenty stories of things that I didn't know that I didn't know, one of those was even years before the startup that I did. This was well before college. But before I went into college, I had an opportunity to run a business for a friend of mine, and he was going to be away from his business for a year so and so on paper, this looked great with like a great deal. He had set up about $100,000 in business contracts, all I needed to do was to fill on those and you know, keep the profit. Now, the team was small, and this was back in the 80s. So everyone had big hair Anyway, you know, to a young pre college age kid this this looks like a lot of money. So I'm thinking hey, how could I pass up such great opportunity?

Oh, and by the way, I could use that money later for paying for college. So in my mind, it was a win win win. So I told him, yes. Well, I got a few weeks into this and he hadn't left yet. And one day I got this pit in my stomach, right? Something just did not feel right. And this not at me for several days. So I eventually got some alone time, right? Just ponder and think about this right? What is going on. And during that time, I got some clarity. And I went back to my friend and I turned down the opportunity. Now I thought the story would end there. A few months later, he reached out to me, and he informed me that, that the other business partners that were involved had embezzled a bunch of money, and the company went down. And he was tied up in in legal matters. Now, certainly, I didn't know what I didn't know, right? I didn't know that these potential partners were planning to do something like this, right? We're right in the middle of when I was trying to make make this decision. Now, in this case, gut instinct, some inner voice right gave me some reason to say, to pause and reconsider, right, which I did, of course, and, and I'm grateful for that. Right. It didn't tell me of the impending scheme that was about to unfold. But it's so how we learn about things that we don't know that we don't know, that tends to vary, right? Sometimes, it's got instincts, right? Other times, it's some AI insight, we need all of that when we're running our SMBs.

Right, we need to understand and be open to the change of constantly learning and, and constantly not knowing and have that curiosity for that. That's a critical aspect of running our businesses, of course, well, there's some techniques around this right. One of those is if you want to practice with your brain, allocate some time on a regular basis to learn something new, right. And so that's literally it could be set aside 10 minutes a day, or whatever it might be. Maybe it's while you're driving in your car, you're listening to a podcast, or whatever it is put in some time to learn and to expand what you're what you know, and what you don't even know that you don't know. Because there's incredible cross pollination that takes place from other industries into yours. So recently, we were working with a group who had been in business for quite some time. And they made the statement, they made the following statement. I'm paraphrasing, but they made the statement about receiving some insights from AI. They said, Look, we know enough to keep the business going. And if we don't know what we don't know, how's that gonna hurt us? By the way, they paraphrase, I'm paraphrasing them. We've been in business 10 plus years. And so you know, we actually don't need this, we already know the business.

Alright. So in contrast, we were working with another group. their mindset was different from the beginning, it was more like, hey, what is our business information mean? And what is it that we don't know? And what changes should we make? They were, of course, informed of many things in their sales that were both effective and ineffective. And many of the insights were things that they didn't know. And they didn't even know that they didn't know. All right, well, they approach the insights, the AI insights much different than the other group. And as a result, they actually experienced a different outcome, a great outcome. So here's something to think about. One is find new ways to solve some of your problems by looking at other areas, right, by encouraging or bringing in additional ideas or insights. It turns out back in the feudal days of Japan, the samurai, they were not only warriors, of course, and I'm no expert on this, but I discovered that their mindset, and their approach was that death was not to be feared, right. They wanted to die honorably.

Well, that's interesting, right? That's amazing. But one of the cool things about them is that they were cultivated in many areas, like the arts, so they took classes and calligraphy and bonsai cultivation and tea ceremonies and origami and flowers and painting and so forth. And they, these, each of these areas influence their fighting skills, right to ensure that they, they could be this Ultimate Fighter, right that ultimately develop them and their abilities as as a samurai. That's amazing as they tried to live the Bushido code. I'm amazed with those kinds of stories, right, where they're willing to be open and look outside of areas. So mindset is a key, certainly not only with AI, but with most things. And so in our SMB businesses adopting a mindset, like the samurai, right to learn from other sources, that's real similar to the AI insights and applying it in your business fight.

Hey, everybody, join me on Thursday for a web class to discover what you don't know that you don't know about your SMB.

Go to ClickAI.com/BreakTheTrap

Looking forward to seeing you there.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback and Remember to download your FREE eBook visit ClickAIRadio.com now.

Hey, we tried it once it didn't work, so why would I even try AI?

Hey, this is Grant, thank you for joining in this episode. So okay, we tried it once it didn't work. Why would I even try AI? Well, it turns out the global artificial intelligence market, I think Market Insight report estimated it to be I think it top $40 billion in 2020. That's a cumulative or compound annual growth rate at 43%. That's pretty amazing.

So there are some amazing achievements around AI. However, you know, there's still a lot of bugs to work out with this stuff. Let's take a look at some. Okay, so let's take a look at one of these failures. That was gender phi, right? 98.4% probability that the word male match to the word professor, and if you put in the word stupid and come back and say 61.7% was female prediction, oops, what the hack right? what it what a mistake. Here's another failure, right, this facial recognition, I'm sure you've heard of this one. You know, you've got some 94 year old granny who has to be held up, you know, in front of the picture, you know, in her Chinese bank, to just to get her face, right, just that there's some usability challenges there, right?

Uber course walks away from driverless citing adolescent AI, obviously, Amazon, they axed AI for their recruitment, because they found out that we've had bias at all had a lot of bias in it. And that's, you know, eventually damage Amazon engineers realize that, that the AI was indicating that the male candidates were automatically better outs. Not good. Here's another Amazon failure. Right? There facial recognition software match 28 US Congress people with criminal mug shots. Yep. Yep, that's facial recognition thing is, is tough. All right. So in short, what does AI actually work for? Right? Where does it actually succeeding? Where's it doing? Well, well, McKinsey points out that AI is shining in the area of big companies that are now reporting 20 plus percent of their revenue coming from AI insights, that's pretty big. Two thirds of those big companies are using it to increase sales. And these companies plan to invest more into AI in response to COVID.

So that's a success story seems like a lot of these facial recognition things. Heck, I think even IBM was applying it, you know, in some of the cancer, cancer use cases right, with with their Watson technology, and the doctors rejected it, claiming it was giving some some really bad advice. So the areas where he tends to do better is when it comes to the area of sales and sales planning. Now, the question is, is how do you identify the best AI project? What does that look like? Well, in typical, you know, typically, what you're looking for is something that's going to give some level of conversion, or attrition impact. So if you can increase your sales, conversion, or reduce the attrition of your people, that's typically where it tends to do well. So your sales and account management teams, typically are benefited from this. So you got to build a business case around it.

In fact, there's an article by a guy by the name of Jeff Caitlyn. This is on Forbes, he pointed out a couple things here. He said, Look, typically you're looking to predict customer churn or you're looking to handle and review or handle online reviews better or improve your messaging. But the bottom line is, you're really going to build out a business case for AI much like you would other business cases. What does it take for me to put into this, what's the effort and then can I get something out of it and the place that the large companies tend to be getting some traction with it is in this sales improvement area. So three things here. Number one, AI has been bringing positive impact to sales and Supply Chain use cases number two, identify which sales or supply print you know, supply chain problem areas that you're going to focus on.

And then number three, of course, as an SMB don't go build this out yourself. It's too costly, it takes too long to turn it around. So hey, I invite you to join me Thursday in a web class. To bring some positive impact to your sales and supply chain scenarios. Go to ClickAI.com/BreakTheTrap

Look forward to seeing you there.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

In this episode, we'll take a look at how to run from your SMB pain.

Okay, welcome, everybody. So AI requires months and years of training before results are seen. Where do we heard this before? Right? Well, we saw some material not long ago, of course, and we've all experienced this on Amazon, right? Where they're spending well over $20 billion annually on you know, research and development, things like that. What they do is they collect data to give product recommendations for AI, right, and we've been on Amazon, we see a product. And of course, out of that then comes this other set of related products that might be interesting, right or might be related to what we bought. That takes a long time for them to gather that. And as an SMB, of course, we certainly can't take that long, or certainly even spend at that level to get those kinds of AI results. In fact, when you look at it, some people feel that it takes six months just to learn how to do data science. Now that's just to learn it, that's not to actually become an expert, and be proficient at it. That doesn't even include building an AI model. And then finally, eventually getting some business results. Another group, they say that it takes their teams 30 days just to get their AI models deployed and functional. As an SMB owner, you don't have time for that.

And another set of group said, it takes six months just to build their AI models and deploy them six months, can you imagine as an SMB owner taking six months to do that? Now, I found out looking at some other stats in over 60% of the RL artificial intelligence projects. They don't even get past the initial proof of concept and data preparation stages. So you know, pay, you're just trying to figure out is this even feasible? over 60% doting get through those phases, that's, that's huge. So look, take, take the pain out of your SMB and don't even do those steps, right? Put that into the hands as someone else Unless, of course, you got you got money to burn, right and you got time to waste. I guess if you do, that's the way to go. But as an SMB owner, it makes, you know, doesn't really make sense to build out all of those teams and to take months, eventually, to get something to help your business grow. I was looking at a case study recently, where a group came back and they had used some AI. And they said, Look, the time and the effort that it took for them to find $3 million in additional sales leads, it was only a matter of a few days. That's huge. So by the time they got their data, a little bit of cleansing, and then ran it through this advanced AI platform, hey, came down to just just in a few days, they found $3 million in additional sales leads. Now the point is, that obviously is a very short time with a huge turn right a huge, huge result for this medium sized business. Nevertheless, it is indicative of the other side of the spectrum, which is you're not spending the months, lots of months, your eye to get these teams set up to go try to find some sort of business value. So to reduce the time that it takes to get value from AI for your SMB. There's three steps that I'm going to go through here writing three steps to reduce the time that it takes.

Step number one, list the areas of your business that you're trying to move away from pain, it might be in the marketing part of your business might be around sales, or the supply chain. But the key here is you got to get specific with this because as we know as a human race, we tend to move away from pain faster than we move towards other emotions. Alright, so that's step one. list out the areas of your business that you're feeling the pain. Step two for each of those areas might be in the sales area. Identify with Current processes are, are the activities that you do around that right where the pain is actually occurring. All right, so that's, that's step two, step three, for each of those processes, identify which ones you're willing to make a change on. Alright? That actually becomes incredibly, incredibly important that third step. And the reason why is, I've seen time and time again, where some AI insight comes into an organization, it sits on a shelf, right, there's an unwillingness to actually make the change or to adopt. Right now, I want to walk through an example of these three steps. So let's go back to step number one, list the areas of your business where you're trying to move away from pain. So let's say that the pain in this example, let's say that the pain was, I don't know what changes to make in my business, because I don't know what my business numbers mean. Alright, so let's say that the business numbers in this case are some sales numbers. Right? That's step one. Alright.

So we've got we got some pain identified step two, for each of those now. So let's let's identify what the current business processes are, that we do. So let's say we have two processes. Process number one is with our sales team, we do a monthly sales planning and forecasting process. All right. So that would be one area around the sales that I need to know what those business numbers mean, that would affect that. And let's say process number two is with the business team. So that might be the CEO, the President, etc. That's a quarterly budget review, let's say the of those two processes. Alright, let's now go to Step three. And step three is critical, because this is where you now identify, which ones of those processes are you willing to change, when you might have some pressure, right, there might be someone in the organization that says, I'm not willing to change our quarterly budget review process, I'm not going to take some, some insights from that. So let's scratch out that one, let's just say not changeable, the CEO, the President, whoever, just saying not willing to do it. But let's say in that first process and sales team, they're interested in, hey, if you had some additional insights, we might make some changes then, in our monthly sales planning and forecasting process. Alright, so now we've got this setup. It's first we identify the pain, the pain is, we don't really know what our business numbers mean. And then the second step, hey, we not we know what some processes are, that relate to this pain once in the sales area. And then third, we picked the sales team, monthly planning forecast process, right, you got to have those three. Notice we haven't talked about data we haven't talked about, go get your data, go get it cleaned up, ready to go? No, as an SMB, you focus on the business part. And on where are we willing to make changes? Given that we get some insights? All right, that that's the critical piece. Now, let's say then that some AI gets applied, you don't go apply it, you have someone go apply the AI. And what comes back is some insights about sales effectiveness.

Okay. And I'm going to give you a couple in this example, insight. Number one, the AI comes back and says, you know, sales on Mondays and Tuesdays are less effective in the southwest of the United States for two products. Okay, so the AI looked at the numbers and figure that out. It's like, okay, that's an interesting insight. Insight number two, sales staff member a will call the staff member, a sales staff member a has a higher closing rate for product x in the central us for a specific demographic, right. So it might be for people of a certain age range or whatever. But let's say it's for a specific demographic. Okay, so the AI has found those two insights. Now, this is where the AI gets interesting. The AI then produces something called a smart step. Smart step is forward looking, right? It's predictive, it's okay. Now, therefore, what should we do moving forward, and the AI comes back and says, you know, to increase sales, there's an 80% plus probability to have staff member a sell product x to a target demographic, in the southwest us on Thursdays, Fridays, and Saturdays. Right? So the question is, let's say that we have that prediction coming back from AI to grow sales, do these things highly profitable, or, you know, high probability to go do that? But question, the real question is, what changes will the business be willing to make to adopt this smart step? Again, we said hey, we got to process our monthly planning forecast. Would we be willing to make some adjustments to apply that so Given that the sales team was willing to make those process adjustments and the, you know, the quarterly planning and budgeting process team wasn't.

So given the sales teams willing to do that, then what you do is you put in place the strategy to apply that AI smart step, right? In other words, we will make the adjustments, we're going to get staff member a to actually go off and do that work and focus on that particular area. It's critical to do that, because really to be successful with AI, yeah, you need the data. But as an SMB, don't, don't go focus on the data piece focused on what problem? Where's the pain? What am I willing to change, and then let me go put that change in place. So to sort of be successful with AI, three things. One, the AI insight, you need to have that obviously, otherwise, this is all moot. So you need the AI insight that augments your intelligence, right? This isn't about saying, the AI is taking over your brain, it's more it's bringing insights and uncovering things that we don't often know, we don't know. So, a we need that. So that's number one. And then two, we got to have the willingness and the ability to make adjustments to your business, right to apply the insight to some either existing process, or maybe it's creating a new one, but two, you got to have that. And then three, this is critical, you gotta track the impact of the changes. So how did things? You know, how did things come out? At the end? What was the results of that?

So the scenario I just walked through, actually comes from some customer examples. You know, I liked some of the big company timelines, where it's multi month, you know, six months or whatever, right? This can actually be done a lot more quickly for an SMB, and one of the advantages of an SMB have been a small to medium business owner. It's It's the ability to pivot on the insights more quickly than the large companies can do this. Now, if you think about it, can you imagine the rate and the pace of growth that can happen with an SMB that applies AI smart steps, again, and again, and again, again, you We can't go do all of it in one step as a small company, but you pick one or two of those smart steps, per each time you apply the AI, as the organization has the mindset to make those adjustments to track it and move on, you intentionally have the potential of putting a dent in the rate and the pace of change that big companies are doing, in fact, going even faster, quite frankly. So the takeaway is like, hey, as an SMB owner, I can access AI insights quickly without major time and cost event investments right and feel and experience the results of that. So come join me on Thursday for a web class on how to take advantages of AI as an SMB, go to https://ClickAI.com/BreakTheTrap

Looking forward to seeing you there.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.