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AI’s Role in the Future of Asset Management and Field Work | IBM’s Joe Berti



In this episode of the IoT For All Podcast, Joe Berti, Vice President of AI Applications at IBM, joins us to discuss the role of artificial intelligence in fieldwork and equipment management. Joe shares his experience in the asset management space, giving background on how equipment has changed and how that change is forcing modernization for the technicians who maintain it. Joe also speaks to the skill gap that will occur as senior technicians exit the workforce and a new generation of field workers start to build their skills. He discusses how artificial intelligence will play a role in helping them get up to speed, identify problems faster, and enable them to make more effective decisions, faster – not only as new technicians are trained, but as equipment continues to evolve and change.

In his current role as vice president of AI Applications at IBM, Joe Berti focuses on working with clients to accelerate their digital transformation using intelligent insights. His team drives the overall product strategy for a portfolio of business applications that includes Maximo, TRIRIGA, Sterling, Engineering and Weather Business Solutions offerings. Joe was previously CEO of Oniqua, a company that was sold to IBM in 2018. Joe received a Bachelor of Science in finance and management information systems at Ohio State University.

Interested in connecting with Joe? Reach out to him on Linkedin!

About IBM: IBM Maximo optimizes asset utilization, increases uptime, drives efficiency, and reduces operating costs with intelligent asset management. These capabilities are now being brought to technicians in the field through Maximo Mobile, a smartphone app connected to the Maximo Application Suite. Maximo Mobile uses AI and remote human assistance, digital twins, basic mobile functions, and disconnected mode available without cell service. With Maximo Mobile, when an asset breaks down, the technician can access a more powerful solution in hand, including the ability to remotely collaborate with experts and access Watson to help them diagnose problems and identify the likeliest fixes.

Key Questions and Topics from this Episode:

(01:01) Intro to Joe Berti

(01:39) Introduction to IBM and its role in the IoT space

(03:10) What is IoT’s role in maintenance? How is it changing the role of technicians in charge of equipment?

(05:20) How is AI addressing the skill gaps involved in the maintenance of all of this equipment?

(10:21) How is AI influencing the future of asset management?

(12:23) How is all of this newly available data enabling the change from focusing on reactive maintenance to predictive maintenance for equipment?

(13:58) How does connectivity fit into this change of focus?

(16:18) How will cloud computing and edge computing affect the future of asset management?

(18:56) How do you approach legacy systems? What does that conversation look like with customers and how do you plan to adapt to existing infrastructure?

(19:58) How is the next generation of mobile technology in the field helping to keep field workers safe?

(21:58) What is a digital twin and what industries are really embracing the use of digital twins?

(23:54) What are you most excited for in the future of IoT for the rest of this year and beyond?


– [Narrator] You are listening to the IoT For All Media Network.

– [Ryan] Hello everyone, and welcome to another episode of the IoT For All podcast, on the IoT For All Media Network. I’m your host, Ryan Chacon, one of the co-creators of IoT For All. Now, before we jump into this episode, please, don’t forget to subscribe on your favorite podcast platform or join our newsletter at, to catch all the newest episodes as soon as they come out. So, without further ado, please enjoy this episode of the IoT For All podcast. Welcome Joe to the IoT For All show. How’s your week going so far?

– [Joe] Good, how’s your week going?

– [Ryan] Not too bad, really hot here in the DC area, but hopefully it’s better weather where you are.

– [Joe] Nope. It’s hot in Texas.

– [Ryan] Okay! It’s great to have you, um, I’d love to just start off by having you just give you a quick introduction to our audience, background information, anything you think is relevant and gives them more insight into who they’re listening to.

– [Joe] Okay. So my, my background, I’m an entrepreneur. I’ve, you know, launched over 20 plus software products. I’ve,

– [Ryan] Uhum

– [Joe] I was running a company a few years ago and then sold it to IBM. And then since then, since it’s out of IBM, our IoT business, as well as our weather business, blockchain supply chain, Watson media, and a bunch of other things as well.

– [Ryan] Uhum.

– [Ryan] Fantastic. I’d love it If you could talk a little bit more about an IBM and the role you all play in IoT, AI, that kind of arena, kind of what you do, what you offer the market, how your approach is different than maybe what else is out there.

– [Joe] Okay. We started investing in IoT about six, seven years ago, and IBM is heavily invested in industries that I call asset intensive

– [Ryan] Okay.

– [Joe] and it’s, it’s global, so, we have customers and utilities, oil, and gas, transportation,

– [Ryan] Uhum

– [Joe] all that manufacturing, all that, IoT, have the intensive

– [Ryan] Sure

– [Joe] industries you would expect. That was the original reason for investments. We also have a large install base of product called Maximo. So, Maximo is used by thousands of companies all over the globe to do maintenance. And so obviously, maintaining assets and IoT, the convergen, convergence of those two is a natural fit. We also have a portfolio called TRIRIGA, which maintains buildings as well. So as you know, IoT’s being embedded inside of buildings and everything else for that matter. So those are, those are two large portfolios among others. We have the supply chain business as well. IoT’s more emerging in that segment, but think of tracking shipping containers and goods all over the globe, especially after COVID,

– [Joe] people want to know

– [Ryan] Right

– [Joe] what’s going on with their supply chain. So you’ll see more and more investments in IoT in that segment as well.

– [Ryan] Fantastic. So one thing you mentioned was talking about the maintenance side, and I’d love it if you could talk about how the emergence of so many IoT connected devices that we have now, has kind of changed the role of those technicians out there that are repairing and maintaining the equipment that is powered by them. And just kind of, kind of what you’re seeing on that side of things.

– [Joe] Yeah. Well, part of the equipment’s changed, you know, when I was growing up, I, with my father, I used to work on cars, right? Today I wouldn’t even touch a car. I opened the hood of the car, and I’m like, what the heck is going on inside

– [Ryan] Right

– [Joe] There’s there’s wires everywhere and sensors embedded on everything. So the equipment has changed. So that’s changed the life of a technician. It’s getting worse and worse, like, at some point in time, we’re expecting to like, farmers to be using robots going up and down their fields, just, just even picking weeds, right? Instead of using fertilizer as an example. So you now have a farmer

– [Ryan] Sure

– [Joe] having to maintain a robot. So the, the, the equipment’s changing is one thing it’s being modernized as well. There’s, there’s a lot

– [Ryan] Okay

– [Joe] of aging assets out there

– [Ryan] Right

– [Joe] and they need to be replaced, and the new ones have sensors and, the cost of data, and the cost of chips has gone way down.

– [Ryan] Right.

– [Joe] So they’re all being shipped with the embedded computers, chips, and connection to be able to be maintained better and monitored better.

– [Ryan] Yeah. I’ve heard a lot about kind of what’s going on with chips lately. It sounds like there’s, at least the people I’ve spoken with that there are some, a shortage on the chip side. Is that, is that the kind of what you’re, you’re seeing as well, or how are you kind of approaching that?

– [Joe] It’s in certain segments, I thi,

– [Ryan] Yeah.

– [Joe] I believe that’s a temporary problem. So it’s a,

– [Ryan] Okay.

– [Joe] what they call “transitional issue”. The, that being said, more investments will be made in semi-conductors. We do expect more of it to move in country and, you know, depending on what country that is. So you’ll see more US-based manufacturing facilities

– [Ryan] Right

– [Joe] manufacturing chips, instead of overseas, but that being said

– [Ryan] Right

– [Joe] At longer term, there won’t be a shortage of chips, it’s a temporary problem.

– [Ryan] Gotcha. So kind of going back to our question a second ago regarding the maintenance of equipment. So I wanted to see if you could bring in the AI component as well, and talk about how AI is helping kind of bridge any skills gaps that are being created by this new technology,

– [Joe] Yeah

– [Ryan] help people that are, you know, being trained to maintain, repair these complex assets now in the world.

– [Joe] You know, the, what’s a little bit of background on it is, you’ve got this dichotomy of someone just coming into the workforce. Who’s, we’re pretty much, because growing up online and playing video games, so think of a drone operator like that teenager, they actually have a useful skill that can actually operate robots and drones and autonomous vehicles, etcetera. And then you’ve got a technician who’s been out there 20, 30 years, and there’s the ones who grew up working on lawnmowers and cars. And so

– [Ryan] Yeah

– [Joe] they’re more likely to just roll up their sleeves and start playing with the equipment. The person coming at uh, school, is most likely gonna go look for a YouTube video on how to fix it. So think about it from that perspective. Now you, you’ve got a, a workforce that is retiring and the knowledge is being lost. And so being able to capture that and infuse that, but also being able to use AI to then, the search through user manuals, videos, past work, order history, to actually resolve and fix things. The idea for it actually came from like shows like Star Track, Trek If you ever watched Star Trek,

– [Ryan] Okay. you see that they’re interacting with the computer. They’re like touching things in the air. The, the, the actual ship is telling them, oh, there’s a problem in this room engine, on this, you may want to go look at that, right? And there’s a, you know, beyond that, there’s the guy down in the engine room, screaming for some reason, trying to fix it, right? But, that’s

– [Ryan] Right.

– [Joe] actually the 20, 30 year old technician, right?

– [Ryan] Right.

– [Joe] A 20 to 30 years experienced technician who actually knows how to fix the equipment without actually watching a video. So, the AI is really driving the transitional workforce that we’re seeing and the need to, capture that knowledge and up-skill set of, a set of people who are new to the workforce.

– [Ryan] Are there any specific mobile tools that these field workers and technicians and so forth are using to help kind of keep up?

– [Joe] We launched the new mobiles, so I’m glad you ask. It actually has embedded AI assistance. So we’re using a Watson AI

– [Ryan] Okay. Okay.

– [Joe] It also has a remote collaboration. So you can actually, you know, think of calling a remote center where there’s experienced technicians, then you can get both human based of help and AI based help as well. There’s still a lot of work though, even though there’s AI, we, we even actually, by the way, we have AI based part identification. You can use a camera on your tablet and phone

– [Ryan] Oh, gotcha, gotcha

– [Joe] and say, oh, this is this part, this particular part, because having the right parts close down

– [Ryan] Right

– [Joe] the work as well, so you have to have the right parts and data to identify them. You know, so there’s quite a bit going, that being said, everything on the planet needs to be digitized

– [Ryan] Sure

– [Joe] in order for this to work, like you need the digital footprint. AI is only as good as the data that you feed into it, so,

– [Ryan] Of course

– [Joe] we launched what’s called the digital twin exchange to help facilitate digitizing everything on the planet. But there’s a lot of work to do. Think of, think of iTunes with no songs. That’s what, um you know,

– [Ryan] Yeah, right, right

– [Joe] that’s where we’re at with digitizing everything. But we’ll see a massive acceleration and digitization of, of common assets and in a form that’s more useful for AI and algorithms going forward.

– [Ryan] Yeah, they, I think it’s a good kind of way, high end, how IoT and AI are working together. A lot of times people think of AI or IoT separately, but the importance of how IoT is able to collect and bring that data in, so the AI systems can do their job well. Cause as you’re saying, AI is only as powerful as the data that’s able to be pulled in.

– [Joe] Yeah, well, you know, it’s an early, early IoT projects, like one, one well-known project is the Apache helicopter. They put,

– [Ryan] Uhum

– [Joe] they spent billions of dollars putting sensors on everything and they collected so much data. They, they could barely store it. Right? And then what they’re doing is trying to catch helicopters that were crashing and understand why they were crashing, which is why they’re spending so much money on that particular project. But what they learned is they were putting them, the sensors in the wrong place.

– [Ryan] Uhum

– [Joe] The sensors were failing themselves, etcetera, but what’s happening now is it’s being done the other way, is actually saying, okay, what are we trying to monitor? What problem are we trying to solve? And then, what sensors do you need? And where would you put them in? what sensors even work? to be, as they’re embedded inside of equipment? So there’s, we’re much further along than we were, you know, 10 or 15 years ago when that project was first conducted.

– [Ryan] Right. And I want to take it back to when we were, we were talking about when we first started this conversation around kind of the, the maintenance side of assets on the IoT side, but can you tie in how AI is kind of influencing the future of, of asset management and what kind of AI is actually being used to kind of sport that?

– [Joe] There’s, AI’s being used at a lot of different levels. So one is, one common is using machine learning to predict failures, right? And in certain types of equipment, AI is being used for assistance. So AI based assistance, that technician assistance that I mentioned, AI is being used for like visual recognition. So like for example, taking pictures of equipment and visually saying, oh, there’s something wrong with it because the AI model knows that it’s supposed to look like this and it looks like that. Right? And, and computer vision is being used for sound. So, and you know, the, the really experienced technician can actually walk by some equipment and they could say, ah, there’s, that thing has a bad bearing. And you know, I’ve been with a technician. That’s, that’s something like that. I’m like, I can’t hear anything. I, like, I have no idea what you’re talking about. So, but you can use microphones and sensors to detect, you know, certain types of motor failures or bearings or other vibration,

– [Ryan] Uhum,

– [Joe] Etcetera.

– [Ryan] Right.

– [Joe] So it’s been used to, determine the use of what’s called anomaly detection. And so what that is, is saying, is it really doing something different that it’s never done before? And if it is, you know, should somebody actually look at it? And so, so anomaly models are pretty high. It’s not the only type of model used, but anomaly detection is pretty commonly used to, to say, is this thing doing something different? Like if you’re, if your air conditioner starts vibrating at home

– [Ryan] Right.

– [Joe] and it’s never a vibrator before, there’s probably something wrong with the fan, right? There’s something going wrong. So an AI model would catch that and then hopefully you repair it before the whole thing breaks and sh, you know, shatters and

– [Ryan] Right.

– [Joe] damages the entire unit.

– [Ryan] Well, that’s a good point on the predictive maintenance side. How are you seeing all this data that we’re now having available, that’s coming off equipment that we’re pulling in and analyzing work and kind of shifting the approach from repairing and maintaining once something breaks to being able to predict when something is going to go wrong and then prevent it from happening, what are you kind of seeing as that kind of change is happening in the market?

– [Joe] There’s, one, there’s people are doing predictive forecasting. That’s kind of a one trend.

– [Ryan] Uhum.

– [Joe] People are setting up remote monitoring center. So imagine a technician instead of doing an inspection weekly, I’m walking by a unit and having that one technician monitor hundreds of units all at once, so that data’s being used to set up remote monitoring centers. So that’s one way to do that. Kind of what a common one most people would understand is your alarm system in your house, right? Somebody’s, a systems monitoring that remotely. If you paid for a service and then it’s, they’re gonna first call you, then call the police if there’s an issue, you could do the same thing with equipment. You can have, you can kind of scale monitoring across hundreds, if not thousands of assets. So that’s

– [Ryan] Uhum.

– [Joe] currently underway, especially with COVID, COVID has really driven an acceleration of that

– [Ryan] Right

– [Joe] because you had social distancing, distancing issues, and some plants can just continuously run with very little people. And,

– [Ryan] Right.

– [Joe] but you still need to monitor everything and make sure everything’s up and running. Like, for example, a nuclear facility, you can’t just stop monitoring it. You have to pay attention to what’s going on day-to-day

– [Ryan] Of course.

– [Joe] hour by hour.

– [Ryan] So, and as technologies kind of evolve and especially on the connectivity side and, you know, 5G starts to get implemented and in a more wider sense, and it gets adopted. How are you seeing kind of the evolution of connectivity technologies, especially 5G impacting this, this shift over to more predicting and preventing as opposed repairing and maintaining?

– [Joe] Yeah, there’s really two things with that. One is, the, the IoT data is being put in the hands of the technicians, kind of the dirty little secret is the technicians actually didn’t have access to the IoT data, um, but

– [Ryan] Um.

– [Joe] operations has it. The people operate, operating and making sure that the manufacturing facility, for example, is running day to day, they have access to what’s called the SCADA systems or the IoT data. The technician really never had access to that. So we actually took that data and we put it in the hands of the mobile or the technician. So that’s one big change. And that that’s actually pretty, that’s actually very groundbreaking cause that hasn’t happened until now. So now the technician can see the temperature, the vibration, the information over the past. So that’s fundamentally changing how they maintain equipment. So, and, so that, you know, there’s some fundamental changes happening 5G in particular, what’s happening is, I can’t even pronounce it at all. The carriers have this long-term for indoor 5G. I’m just going to call it indoor 5G.

– [Ryan] Sure.

– [Joe] It’s, it’s ultra band. Wifi is something, blah, blah, blah.

– [Ryan] Right, right, right

– [Joe] And so they need a better name for it like wifi, but it doesn’t exist yet as though some, someone will come up with it. But what they’re doing is they’re blanketing the indoor with 5G and, because it’s, it works so well at high fidelity in, in a very close space, you can take an entire manufacturing facility and kind of get, get basically wireless and 5G everywhere in the facility and what that allows is you to put like cameras and other things, other sensors, video acoustics,

– [Ryan] Right.

– [Joe] or sound sensors

– [Ryan] Right, right

– [Joe] all over the place. And they weren’t really able to do that before, without putting wires all over the place.

– [Ryan] Uhum.

– [Joe] So it’s, it’s enabling a new class of kind of robots, robotic or

– [Ryan] Right.

– [Joe] AI based visual inspection, as an example.

– [Ryan] Speaking of that, kind of that same shift that we’ve been talking about here, how do you see not just the connectivity piece, but now also cloud computing and, kind of edge computing affecting, not just the shift we were talking about, but also kind of shaping asset management as it’s going forward in the future?

– [Joe] A lot of the data from the, that’s being collected is now being pushed to the cloud. So you can do more longer term predictive analytics. Like for example, if you’re predicting that equipment it’s gonna fail, you’re typically predicting weeks, days, weeks, months out into the future.

– [Ryan] Right.

– [Joe] So you can do all that in the cloud and you can aggregate the data. Now, if I’m trying to monitor, and it may, if it matters, if this thing fails and I need to know the minute it fails, you’re gonna do that on the edge. And so there’s still a place for things running on the edge, alerting someone at the floor or texting them or, or letting a remote monitoring center know there’s an issue, but, the cloud is being used more and more because they can actually deal with the computational side of things. It can also deal with scaling, like I mentioned, that, that remote monitoring center

– [Ryan] Okay.

– [Joe] that remote monitoring center is most likely gonna run in the cloud and it’s going to be taking data feeds like every minute or every few minutes, and then it’s gonna do what it does better. So you put, put what makes sense in the cloud, put what makes sense on the edge. And I think some vendors and people are still figuring that out, but it’s tends to normalize over time. There’s, there’s, you know, when, when something becomes a new concept, everybody runs towards it. Like “let’s all run to the edge!” and

– [Ryan] Yeah, right

– [Joe] and it may not have sense. It may not make sense to that we’re under the edge, right. But on the edge of what belongs on the edge, right in the cloud,

– [Ryan] Right.

– [Joe] what belongs in the cloud. And so that’s getting normalized as kind of, as we speak.

– [Ryan] Yeah, just giving options on kind of better is building a solution that’s has, you know, everything kind of in line with where it needs to be and how it needs to operate to be as efficient and at the right costs for a lot of these solutions. That, that makes a lot of sense.

– [Joe] Correct. Yeah. There’s a lot of hardware sitting on the edge

– [Ryan] Of course

– [Joe] in inside facilities. Is like, I was talking to one customer, they have like windows 2012, still running with PCs, operating their manufacturing facility, and nobody knows how to use it anymore. And if they move it, they’re worried about just even rebooting it, can actually cause it, to not start up again. The drive may be rebooted. It may just not, it just may not kick again, If the drive

– [Ryan] Right.

– [Joe] quit spinning, and it’s gonna die. So, there’s a lot at the edge that’s old and, needs to be

– [Ryan] Right.

– [Joe] either containerized or modernized. So that’s, that’s currently underway as well.

– [Ryan] Is a, how, how do you all approach kind of that, that problem when you’re working with a potential customer and you see that, you know, their legacy systems and what they have at the edge is, you know, very out of date, how does that conversation usually go? And you know, what do you usually kind of look to do in order to kind of help future-proof it in a sense?

– [Joe] Yeah, we’re doing two things. One is we just connect into it as it is.

– [Ryan] Okay

– [Joe] You know, is, They don’t want to touch it, but they, they typically have feeds or data feeds off of it moving into like a local historian or a local data warehouse. So we typically connect there and we can immediately start doing AI and then we run AI models and it, it makes it all the way to the technician on their mobile.

– [Ryan] Right.

– [Joe] Right. So we’ve, we have, what’s kind of what we call clo, a closed loop process. The other thing we’re doing is we’re containerizing stuff on the edge with OpenShift.

– [Ryan] Gotcha.

– [Joe] They’ve got, you probably heard about OpenShift with IBM if you’ve been,

– [Ryan] Uhum, yeah. halfway awake, right? So that’s being used to containerize everything. And so some of that older stuff is getting containerized and some of it’s being rewritten or modernized as well

– [Ryan] Makes sense. makes sense. Okay. Now, one thing we’ve been talking about with recent, some recent guests is around, workers, and worker safety. So this kind of ties into our conversation with technicians and those field workers. And I’m curious how, not necessarily just what we’ve talked about so far, but just any other technology connected to IoT, connected to AI, how this next generation of mobile technology in the field is helping improve worker safety, just kind of across the board and what you all are kind of involved in.

– [Joe] Yeah. Because everything, every technician has a mobile or a tablet or a computer, you can actually

– [Ryan] Right. attach sensors both to the technician and around the facilities, so, for example,

– [Ryan] Okay.

– [Joe] if there’s hazardous areas where a person’s not supposed to go, you can have a, what’s called a beacon, in that particular area. But, you know, there’s sensors being embedded on safety vests, on helmets, just panic buttons inside of a mobile application. All those things apply. You know, certain like for example, there’s been, I’m not going to name any company per se, but there’s, you know, disasters. Doesn’t have like chlorine plants where at any given time they need to find all the employees, like where are they physically located in the facility, companies that have safety sensors on all those technicians are gonna be able to find those employees and most likely save more lives as the fire department and other people come in to help, you know, the other is just making sure employees are following best practices around safety as well,

– [Ryan] Sure.

– [Joe] but it’s, it’s not, the attempt is not to be a big brother. So I, IBM has actually taken a strong stance on what’s called ethical AI. Like for example, we’re not, we’re not using visual recognition, so we’re not gonna say, “Hey, Joe used to be close to that equipment” And we’re using visual recognition, you know, to map someone’s face. So we’re not in that business. And we’ve taken a clear stance on that. So there, there’s a place where we’re drawing a line and what we call ethical AI.

– [Ryan] Right. Well, that’s fantastic. One last thing I wanted to ask you before we wrap up here, is, we were kind of loosely talking about it earlier, but I’ve seen a lot of material coming out across the board, but also from IBM directly around digital twins. And it’s not a topic that we get to cover very much, so I’d love it if you could just kind of explain to our audience in as layman terms as possible, what, digital twin actually means? And then also kind of branch into what impact are digital twins having, and which industries are really embracing them the most?

– [Joe] It’s, the digital twin is really needed to have that “Star Trek” experience, right? You have to have a digital model of an asset. You need to know what the parts are, how it’s fails, the failure codes,

– [Ryan] Right.

– [Joe] what, what sensors are embedded on it. So it’s really needed to drive that AI experience. I, you know, I, mentioned earlier, it’s like, but what we have now is iTunes without the songs. And so you’ve got this great music player, which is AI, and you really need the songs to make it all, hum or work, like expected.

– [Ryan] Right.

– [Joe] So that’s what the digital twin is needed for. And no one company is big enough, even IBM to write the check, to digitize everything on the planet. So, there’s a massive effort to digitize both old assets and existing assets.

– [Ryan] Uhum.

– [Joe] And what would, what IBM has done is we’ve really provided the iTunes for digital twin. That’s our digital twin exchange that allows our entire ecosystem to upload digital twins. So we actually now have thousands of digital twins out there, and you could actually sign up and create a digital twin, and either open-source it, or

– [Ryan] Okay.

– [Joe] almost like a, you know, where you can actually create a digital twin

– [Ryan] Sure.

– [Joe] and put it out there and actually monetize it. So our ecosystem

– [Ryan] Sure.

– [Joe] owns the IP and they’ll own the digital twins, and they’ll get paid for that. We’re just facilitating the transfer of the twins,

– [Ryan] Okay.

– [Joe] to people who need it.

– [Ryan] Fantastic. Well, this has been a, a great conversation talked about a lot of stuff we regularly do not cover, but we’ve needed to cover for a while. So, I do appreciate you taking the time and kind of shedding some light on, on a lot of these topics. I do want to ask a, one kind of final question for, just from your perspective, IBM’s perspective, whichever side, like, where do you kind of see, or what are you most excited about kind of going into the second half of this year, into next year? You know, getting out of hopefully more of the pandemic and just moving IoT and AI forward, what are you most excited or looking forward to?

– [Joe] Well, what I’m, I am most excited about is we launched ours, our Maximo application suite. So we took IoT

– [Ryan] Okay.

– [Joe] operations, reliability, which was our APM products and maintenance, and combined it together into one solution. So we’re, we’re the only company on the planet that has all three of those things available and, and a mobile technician who can actually access IoT, data, digital twins, etcetera, it’s a, not, not to, not to push an advertisement on you, but that’s, that’s what I’m most excited about. We, I’ve been working on it

– [Ryan] Well, good

– [Joe] for three years. So I have to have some level of excitement.

– [Ryan] Oh, you have to, after this, after your

– [Joe] Indistinct three years.

– [Joe] Yeah. It’s shipped and it looks beautiful, and,

– [Ryan] that’s awesome.

– [Joe] We’ll be at Maximo world August 2nd. And they have some pretty exciting things with robots and other things that are

– [Ryan] Cool.

– [Joe] coming down the pipe as well.

– [Ryan] That’s awesome. Yeah, no, I have no problem you talking about that, cause like you can hear the excitement in your voice been working over three years. The fact that it’s, you know, it shipped is, is amazing. So that’s no, no problem dropping that to our audience, I think will be great for them to check out. A, a last thing I wanted to ask you is if our audience has more questions or follow up or anything, they want to learn a little bit more about what you have going on, what’s the best way to do that?

– [Joe] That’s easy. You just go to

– [Ryan] Fantastic. All right Joe, this has been a great conversation, thanks so much for taking time out of your day. I’m excited to get this episode out for audience to listen and we’ll make sure we send, we put all the appropriate links to all the things you’ve been talking about, so our audience can get all the information they need, to learn more about IBM and what you all are doing on the IoT and AI side.

– [Joe] Awesome. Thank you for having me, I appreciate it.

– [Ryan] Hi everyone, thanks again for joining us this week on the IoT For All podcast. I hope you enjoyed this episode, and if you did, please leave us a rating or review and be sure to subscribe to our podcast on whichever platform you’re listening to us on. Also, if you have a guest you’d like to see on the show, please drop us a note at and we’ll do everything we can to get them as a featured guest. Other than that, thanks again for listening, and we’ll see you next time.

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Why Choosing the Right CBD Product Is Important



When people first decide to use CBD products in order to enjoy the benefits of CBD, they are often confused over which product to purchase. There are many different products you can choose from these days, with many people buying CBD gummies, drops, capsules, and other products online. For those who are new to CBD, it is always important to do some research and find the right CBD products, and this is vital for a range of reasons.

Of course, you do need to look at a few key factors in order to help you to choose the right CBD products, as there are so many different options to choose from. You can do things such as look at online reviews from other people, research the manufacturer and retailer, and consider the suitability of the product for your specific needs and lifestyle. In this article, we will look at some of the reasons why you need to ensure you make the right choices.

The Importance of Doing This

There are many reasons why it is so important that you find the right CBD product for your needs as someone who is new to these products. Some of the reasons behind this are:

You Need to Ensure Quality and Safety

One of the reasons it is so important to look for the right CBD products is so that you can ensure quality and safety. As with any other type of product, you can get great quality CBD products from reputable sources, and you can find substandard ones from questionable sources. It is vital that you do not make the mistake of buying the latter, as this could lead to you ending up with a product that is ineffective and even unsafe. By choosing the right product and provider, you can benefit from quality, safety, and effectiveness.

It Is Important to Ensure Suitability

Another of the reasons you need to ensure you find the right CBD products is to ensure suitability, as you need to find ones that are perfectly suited to your needs. To do this, you should look at your preferences and your lifestyle so that you can then match these to the ideal products. For instance, if you use a vape device, you could look at using CBD liquids whereas if you like sweet treats, you could consider CBD edibles.

You Must Look at Affordability

One of the other reasons you need to choose the right CBD products is to ensure you find something that is affordable and fits in with your budget. The cost of CBD products can vary widely, so you need to do some research and compare different costs in order to find ones that you can afford. Also, make sure you know how much you can afford to spend before you start researching the options, as this means you will not waste time looking at products that are out of your price range.

These are some of the reasons you need to ensure you find the right CBD products.

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Artificial Intelligence

Morgan Stanley’s robot Libor lawyers saved 50,000 hours of work



Untangling trillions of dollars worth of loans and other financial contracts from Libor is a complex, expensive and time-consuming job.
So, finance giants are turning to artificial intelligence to simplify and speed up a task mandated by regulators — and spare human lawyers some serious drudgery.

Morgan Stanley figures it’s saved legal staffers 50,000 hours of work and $10 million in attorney fees by using robot Libor lawyers instead of only the human kind. Goldman Sachs Group Inc. says computer algorithms sped things up “drastically.” These banks aren’t alone in adopting AI, and the revolution likely won’t stop with the Libor transition — but the number of contracts involved in this shift provides an ideal testing ground for the machines.

The task would be grueling for paralegals, whose torture involves parsing dense clauses to sort out which govern in a post-Libor world. Does this paragraph decide how to replace the rate, or do these? They’d sweat floating-rate options, applicable periodic rates and substitute basis to sort out the new interest payment, and grapple with whether the legalese applies just to bonds or to loans and swaps as well.

Then repeat all that grunt work over millions of pages.

‘Army of Lawyers’

“We had a client that had 15 million queries and they were able to get all that answered within a quarter,” said Lewis Liu, chief executive officer at Eigen Technologies Ltd., which helped Goldman Sachs and ING Groep NV deploy Libor-analyzing software. “The alternative would have been literally an army of lawyers and paralegals over a year, or maybe two.”

This is all happening because a decade ago major banks were caught rigging Libor (full name: the London interbank offered rate). As a consequence, the benchmark is being switched off throughout the global financial system. Newly issued loans and other products cannot be tied to the rate after Dec. 31, and it will be retired for dollar-based legacy products after June 2023.

So here come the bots. But even with AI, examining old legal documents to figure out how they change when Libor is swapped out for another interest-rate benchmark is costly. Major global banks are each spending at least $100 million this year on the job, according to Ernst & Young. And humans still need to check their work and make final decisions; once banks discover which contracts need to be renegotiated, they must sit down and haggle with their counterparty.

“A person has to look at the documents and come up with a strategy,” said Anne Beaumont, a partner at law firm Friedman Kaplan Seiler & Adelman LLP, who views AI as an enhancement rather than a threat. “It probably makes a lot of paralegals and lawyers happy that they don’t have to waste time.”

The experience is reshaping broader attitudes toward large-scale administrative tasks, pushing other cumbersome jobs to AI. JPMorgan Chase & Co. has asked its Libor robots to expand their remit and grapple with other hard tasks in the company’s corporate and investment bank, a spokesman said.

Of course, a broader industry shift to more AI could mean fewer jobs for humans in certain areas.

Feeling the Pain

Libor is keeping the bots plenty busy, though. Morgan Stanley’s software digested 2.5 million references to Libor, according to Rob Avery, a managing director at the bank. The algorithm — based on neural-network models and known as Sherlock — rifles through contracts, digging out clauses that identify how a collateralized loan obligation or a mortgage-backed security will transition to replacement rates.

Graph by Bloomberg Mercury

It categorizes them so Morgan Stanley can determine how their value will change depending on the replacement rate. That helps the bank decide whether to keep or sell the asset. The software operates “in a fraction of human processing time to assess the impact of potential rate-change scenarios,” Avery said in an interview.

Goldman Sachs, meanwhile, has seen AI “accelerating the project timescales drastically,” Managing Director Donna Mansfield said in a testimonial published by Eigen.

ING used AI to decide whether more than 1.4 million pages of loan agreements needed revision, said Rick Hoekman, a leader in the bank’s wholesale banking lending team. “It was a big success” that eliminated a lot of manual work, he said. The company’s data scientists may eventually use the software to approve the credit of clients.

That’s not to say that everyone is piling in. NatWest Markets Plc was approached a couple years ago by consultancies offering AI, but turned them down. “We sensed it would involve a huge project to get it to work and would consume lots of time when we just wanted to crack on,” said Phil Lloyd, head of customer sales delivery. “We felt it might help but it wouldn’t be a nirvana.”

Plenty of other banks and asset managers have struggled with such software and are instead hiring offshore lawyers and paralegals to do the work after seeing the large amount of training and technology required.

But there’s likely no stopping AI from spreading throughout banking.

Bank of New York Mellon Corp. is working with Google Cloud to help market participants predict billions of dollars of U.S. Treasury trades that fail to settle each day, and with software company Evisort Inc. to manage contract negotiations.

“When your 12-year-old and my 12-year-old are our age, they’re not going to do finance the way we do — you can see their impatience with technology,” said Jason Granet, chief investment officer at BNY Mellon and the former head of the Libor transition at Goldman Sachs. “You’re not going to beat them, so you’ve got to join them.”

— By William Shaw with assistance from Greg Ritchie and Fergal O’Brien

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7 Ways Machine Learning Can Enhance Your Marketing



In the digital era, no marketers can survive without mastering data, analytics, and automation; the reason is a massive surge in data generation. Suppose you look at the stats about data generation. In that case, it’s more than 2.5 quintillions of data generated every day, which equals 2.5, followed by stupefying 18 zeros according to social media today.

“And by 2025, the amount of data generated each day will surge to 463 exabytes of data globally, according to the world economic forum.” 

And the fun part is the words that humans have spoken fit into only five exabytes of data. Now imagine the importance of mastering data, analytics, and automation and why it is crucial today? You probably have got your answers by now.

But to stand out in the market and beat your competitors, you need to understand the ongoing and upcoming trends. How can you analyze them seamlessly? Through machine learning and advanced automation.

And in this blog, we’re going to learn how machine learning can enhance marketing in the highly competitive world. Remember, you’re not alone in the race, but you need to think and act a step in advance to beat your competitors.

If you get what I mean, let’s dive in and explore them in detail.

7 Coolest Ways Machine Learning Can Enhance Your Marketing

Marketing success depends upon many significant factors, from proper customer research to building the brand strategy, engaging with the customers, and delighting them; it takes a lot of effort and automation.

And to solve these massive problems, ease the marketer’s work and responsibilities through accurate data analysis, machine learning has enormous roles to play. And here is the complete breakdown of how machine learning influences marketing.

Understanding Customers in 360-degree

Every day, your customers share information about themselves, but the best thing you can do is spend most of your time where your customers love to spend. When you start paying attention, you start knowing them better and better.

You get to know your customer’s last purchase, their problems, and how you and your products can help them. When you understand their pain points and are able to fulfil their needs and predict what they are likely to purchase the next time, understand the psychology behind it – you get the 360-degree view of customers.

Real-Time Analytics Gives You On-going and Up-coming Trends

Today, in the digital era, the world is changing so fast that it’s tough to comprehend data, and that’s one reason why business decisions keep changing from time to time. Because the whole thing is when you’re up to the final decision in the making, more and more data gets bombarded.

A few free tools from Google are Google Keywords, Google Analytics, and Google Search Console. When you use them, you get the exact data you need to understand the ongoing and upcoming trends and how your competitors do the same for any location and product.

According to Gartner, real-time analytics is a discipline that requires logic and mathematics to make better decisions quickly. And again, according to Gartner’s research, by 2022, most companies will incorporate real-time analytics to push their firm to the ultimate level and stay ahead of their competitors — just to improve decision making.

Smart Engine Recommendations is the Smartest Move Ever

Businesses run on data, and that’s so true, but where does the data come from? From users, right? Yes, whenever you visit a website or purchase a product, the website cookies track everything, and from there, the analyst can know what other things you would be interested in and like to buy.

And they push you to do similar things when you visit their website. Let’s suppose you purchased an iPhone at this Great Indian Festival; what Amazon will show you next, the phone charger, the case, and tempered glass, saying people who have purchased iPhones have also purchased these items.

How does Amazon do that? Amazon does that using KNN algorithms, using smart engine recommendations. That’s the most intelligent move over.

Predictive Engagement and Analytics (Just a Few Steps Away)

The first step of data analytics is to be able to understand the data, meaning when you know the data, you know customers and what they are looking for. From there, you might know what they might actually purchase.

And predictive analytics is all about that; it’s the likelihood of customers taking a particular action and companies using different software for the accurate prediction.

The best example is “The Big Billion Sale” campaign by Flipkart. If you have looked closely, you have seen the best deals, only seven left, and many different tactics to boost sales while the price fluctuates.

When you’re about to purchase, the order gets out of stock, and again it gets available. Or something you can relate to wherever the new flagship phone launches, there are limited sales every week and delivery to the first registered customers until the device is fully available.

Chatbots are the New and Ultimate Sales Persons

Nowadays, if you see every website, it has something called chatbots, and it is NLP enabled, meaning it’s a self-learning algorithm that learns by itself. With this, you don’t need to be active on a website 24 X 7.

Chatbots are your new and ultimate sales AI-Robots and can guide your visiting customers by understanding their search intent, helping you collect the leads, and later you can turn them into customers.

Personalization is the New Customer-Centric Emotion

When you look into it from different perspectives, you can always relate to customers being emotion-driven; when you present them in the right way and poke their pain points, they are most likely to take action.

But when you personalize them, addressing them with their name, they feel ‘This company is customer-centric and valued their customers a lot. And that’s what hooks them to your business.

The best way to do this is through email marketing, and we have so many tools for the same with self-learning algorithms that automate the whole process with personalization.

Voice Search is the New Generation of Search Optimization and Search Engine

In the digital era, and with many advanced features on mobile and web apps, our life has become more sophisticated. People were hardly interested in typing out their queries but voice-searched them.

That’s what the world’s largest eCommerce platform, Amazon, does brilliantly with Alexa implementation. It works on the principle of Natural Language Processing, where it captures the audience queries, looks for the best matches and related to them through the KNN algorithm, and showcases the most relevant items to the customers with matching keywords.

That way, Amazon makes the marketing and business model easy for the end-users and holds their customers for a long time.


When you read the whole thing, you learn how advanced and essential machine learning has become and how crucial it is to integrate into the business models.

These seven machine learning algorithms have already been game-changing. If you’re a business owner or stakeholder, you must plan to implement them in your business to see it scaling.

Also, Read How to Use Machine Learning for E-Commerce

The post 7 Ways Machine Learning Can Enhance Your Marketing appeared first on AiiotTalk – Artificial Intelligence | Robotics | Technology.

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Common Pay Per Click Mistakes and How to Avoid Them



There are plenty of articles online that talk about some recommended practices on how to build your marketing campaigns. There are also a variety of techniques for optimization and numerous concepts regarding how to structure effective online advertisements.

Since there are countless pieces of advice that are available on the internet, it is very likely for you to get lost with conflicting ideas and be confused as to what you will follow or not.

Things will be easier if you have an expert team to help you with your needs. Nevertheless, it is completely normal to commit mistakes as long as you will learn how to avoid them next time.

Not Utilizing Negative Keyword Lists Efficiently

One of your allies in the effective execution of PPC campaigns is the proper use of keywords. Aside from that, using negative keyword lists with efficiency is also a helpful way to ensure that your PPC campaigns are doing well.

“It will be a great practice if you will have a master list of negative keywords so you can apply it to all of your campaigns with particular terms or phrases that you do not want your advertisements to appear for.” 

Regularly checking the search query reports will help you avoid wasting money on search queries that you do not want your advertisements to be suggested.

Not Matching Keywords to Ad Copy

As a wise business owner, you have to exert more effort in making your advertising campaigns as relevant as possible. Since online consumers have a very short attention span, they do not have the luxury of time to deal with unnecessary and uninteresting websites.

One of the most common mistakes in PPC is when an advertiser is making one set of ads and utilizing them across multiple ad groups. It is good only for having a broad same theme but for personalization, it will make your campaigns weak.

Since you have a lot of other things to focus on for your business, it would be wise and easier to hire an ROI-driven PPC team who are experts in making relevant and successful advertising campaigns.

Focusing Too Much on an Average Position

Advertisers commit mistakes by focusing on an average position. This is because an average position of one (1) simply means that your advertisements are appearing ahead of any other paid ads in the search results.

It does not strictly mean that your ads are actually in the top spot. This is why the average position is not an indication of the location of your ads when they are suggested.

Key Takeaway

Now that you are knowledgeable regarding the common mistakes on PPC, take this information as your driving force to help yourself avoid committing these mistakes.

It is good that you know how to solve these problems when you have committed some mistakes but it is better that you know how to avoid these problems before you even commit some mistakes. You have to employ a proactive approach to ensure that you maximize the full potential of PPC as your marketing campaign.

Also Read, Impact of Artificial Intelligence and Machine Learning on SEO

The post Common Pay Per Click Mistakes and How to Avoid Them appeared first on AiiotTalk – Artificial Intelligence | Robotics | Technology.

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