What AI Readiness Really Means - with Tim Estes of Digital Reasoning
This is Daniel Fidel on, you're listening to a in business podcast. It's Thursday that means it's are making the business case episode making the business case we talk about Ai Deployment and the realistic challenges in the enterprise and opportunities and measuring Roi of Ai. This episode, we're going to talk about what a I. Actually means and we've got a guest with some excellent perspective on just that Tim Estes as the Executive Director and co CEO of digital reasoning. Digital reasoning has raised an awful lot of money to apply artificial intelligence to various industry sectors including financial services, federal and more tim speaks to us about what artificial intelligence readiness means from his vantage point. So digital reason applies mostly in the natural language processing. Space, but many of the will transfer no matter what you're aiming to do within your business. What is it that companies have to have kind of ground rules as sort of a baseline reality of their data of their enterprise expertise of their in house talent to truly be served ready to adopt in deploy artificial intelligence with nearly twenty years doing exactly Tim's got a perspective that I think is worth. Tuning into if you're just getting started with a in your own journey of a high readiness or you're helping your clients do death, then be sure to download our beginning with a free pdf guide you can find that at EMC RJ dot com slash be e. g. one that's bg like beginning the Edgy One. You can download the free pdf brief, which is going to be basically grounding concepts on. Early deployment that's GONNA help you get more out of your podcast listening and also help you help either your clients or yourself with the early phases of AI deployment the areas where critically a lot of companies get wrong. So hopefully, that resource, you'll find valuable and I'm certain you'll find Tim's interview today valuable without further ado. Let's hop into it says Tim Estes with digital reasoning on the in business podcast. So, Tim will kick things off and get your perspective on what Ai Reading this means. When an enterprise says walked, we want to become a I ready. We want to start using a I what kind of components have to go into that? Yeah, well, I think the first thing is you had to have infrastructure that sounds so basic especially with the cloud bud, the larger enterprises a requires a good functioning process to allocate infrastructure with their on premise or cloud. And then data governance of data can be used for training and validation around any process it's going to be tested. So it's all too often that you know one group in enterprise wants to try something. The aren't really the owners of the data that is required. To validate what they want to try. And they are not the suppliers of the infrastructure. So you might run into a substantial gap. The could take you know a sixty day or thirty day pilot. Or PSE and make it a nine month process because you're waiting on them to sort out data governance and infrastructure availability. So those are two pieces you know something about the education side of it. In terms of you know this this dictation you want to build and educate yourself to understand the difference between certain techniques, but it's always overalled because in the end of the day I I'm a little bit more pragmatic I think there's certain techniques which are better for. Some things and others, but obviously, the most sexy technique that talked about the time or different variations of deep learning. Yeah and we could go into braces but the phillies he'll is in most cases, the customer doesn't have the data sets available to train a really good deep learning class fire and so or an engine of some kind. So I I think that what you find actually is it's not just that they have data general had dated is prepared a certain way. Often to teach a machine that the machine can perform the task and that's really the that's the area. So these maybe the this question you elite in some other things but you know basics, infrastructure data governance I can pull they need to run the test fast and as a as a vendor or someone the outside I mean I would becoming in asking these questions now because I lived through being wishful thinking and. This is really exciting CTO and they want to do this and they have a business stakeholder that wants to do this kind of application. They think we're the answer I've been through that whole dance, and then you find out that of the whole dance that dance might take months four and then you wait nine months for data and infrastructure to be available in the large bank. Yeah. Well, not surprising at all within a large bank you're lucky it's not a eighteen months or something. So you bring up infrastructure you're bringing up data does this mean in the process of speaking to whoever your initial? Champion as your your initial kind of point of contact who you think is GonNa either signed the Checker help sign the check the you really have to be clear that sort of what infrastructure you need to access of what kinds of data you'll need to access of the state of that data with that person and or with whoever they need to rope in serve as part of the process of working to a pilot. So like doing that diagnostic I, guess as you go as you progress forward. Yeah that's right. So I mean I'm naturally gonNA give it more from the vendor sides, of course. But if I flipped the hats and I'm I'm actually in the buyers persona what I wouldn't WanNa do is the last thing I want to do is to put a lot of energy into something that could create real value get excited marketed internally, and then find out that getting infrastructure having data governance process in place where we can get the data necessary to test the system is not really well figured out or is figuring out but the restrictions that make this not work. So I, think that there's a good upfront investment in that but there's a difference between that and sort of what I might call the the Data Lake Panacea. We're everyone wants to have this. Highly Organized Library of data with the Dewey. Decimal system in their enterprise. And that's not gonNA. Prize is unfortunately function. So many it efforts in an enterprise are responsive the business as a higher priority to eating across business lines. That you'll almost never find as you will a pristine data infrastructure. So you really WanNa make sure the process to pull data, put it in the compute environment do that safely and would security sign all's that should be enough to get moving, and so I think if you try to go four steps beyond that. You have much bigger challenge and essentially trying to boil the ocean and I think a lot of people went down that road with all the WHO do vendors to be blood. You know the idea that just got to spend all this money on that and then from that. You end up having all this application. These applications become so easy and here we are five years later it must. We're seeing what applications besides restoring my you know might my loan scores or some other batch structure process? You could probably done some other way. Yeah well, yeah. People talk about the data swamp as opposed to the data lake that was sold or what have you cloudera still made a lot of money but but yeah, I think that's that's often the the gripe. So what you're saying is maybe be more modest with initial goals if we're a buyer for assessing our own readiness, do we at least? Have the stuff organized enough to harmonized organized whatever enough to train some kind of model on it, and can we get enough of a handful of even run a pilot and those are hard nose than maybe we're just not ready for this particular business function altogether, and we need to either focus somewhere else or or focus on getting our infrastructure up to speed. Exactly, and let me tell you the cautionary tale, which is there are people that don't do that work in enterprises. They don't do that assessment of front they get the vendor very far into the process. They may even think that it's going to be easy to get the they need and what happens is this they get toward the end they done all this work and the vendor says, well, okay let's do this. Here's the data I need and they said, well, we check on, we really get that data. We have this other toyed over here. Can you do this and the toy data in Hungary startup or small or oh yeah we'll do that. But then what you actually prove isn't what you meant to prove. and. You find out that they'll be objections and come back. Well, that's not really on our data. I don't know if it's really going to work, and so you're not really that much further along than when you started. That happens more than you would expect that people don't do that upfront work in it sets up essentially or worse economy works in that other data but data. So not exemplary of what the real business process data is that it goes to production. You almost are back to square one and you disappoint because it's like this is the kind of data that we saw will. Yeah we didn't have access to the data. So I'm just pointing US I I. Think this is a real threats much bigger issue than people think of through readiness even though in theory it sounds simple. It's not because the politics of who owns data. Either risk version of disclosing it to more people than have to or the you know the politics of this data has value and I wanNA control who gets value out of the data. Yeah that's unfortunate. But you know reality that in my opinion Tim, every vendor learns that with a bat to the face. I. Don't really I don't really know of any vendor that just sort of bypassed you know stepping on the rake there because it feels like if you spin out of Amazon, you just think data's accessible and easy. If you spin out of university, you just think that your science is good enough and you'll just make it happen but everybody that. Moves into a space gets hit in the face with that and then and then has to really dial back their sales process needs to be really white glove upfront handle all this kind of gunk. Do you think that in I don't know four years from now tim that we will have less of that upfront gunk to get a POC to work to get access to the data we need or is it a way longer ballgame than that? What are your thoughts? I mean it could be I tend to think it is a longer ballgame. I think he's got to use that I mean at the end of the day the I wanNA covers always gonNA invest in. You is the time and them investing time as long as think they're getting something out of it is not always a bad thing. Now it cost you money time just call them but generally the the buyers lot bigger than you are. So that's not a thing you can run indefinitely so insured, I, mean I. There is a long window here where now just thinking about a little bit back to the face I was wondering if my nose is actually genetic or not. No No? No represent reference no no no. I'm just having fun with him because I think that it's a really great lesson learned that it's almost like no matter how someone tells you during a learn because you just want to believe it's easier and it's not the area I think people are now coming around to the piff handed pivot a little bit in this it's Okay let's say the infrastructure let's say they can get the data staged and you can run whatever you're gonNA run on the process you want to validate. Most the time what you come in with has to be taught or adapted to the customer's data. I e the out of the box it just works Hell No. In a non-consumer area just in the enterprise space, it almost never happens. So the next barrier becomes, do they have the data organized in any way to teach your machine or not? That is the thing that has probably broken more a I projects in the bigger. Project the harder in the bigger that problem I just described is. So, if you go in on their very public projects, it's been tens of millions of dollars and the customer will say we get the outcome we fall and generalists because the expectation for set high and they were set high by radically underestimating the availability of the data to educate the machine. To solve that you have to really coach the customer through that. Oriented real answer. We actually don't think that's the image you coach the customer on it. We don't think the customers are going to move fast enough to organize your data. So we actually had admit technology around that. So we we found that the biggest bottleneck for us was, could we have our machine learning models be taught from customer data really fast by having the I find the data to teach the I. So, we actually went one level further, which was, could we actually use? To build a curriculum and teach the I a customer data customer data so. I I don't actually know a really good answer to that. But I am confident there's a good process answer I. think There's a technology answering that problem but I think that's what's going to bottle up a lot of these projects is this problem of not having the training set available to educate the machine and then having to educate the client on on how teach machine we had a product is that into literally something that was almost like a game for our area and I think probably that's going to be what happens I think just sulphur technology in the end actually so Yes if you If you don't WanNa, be a service business and ucla out of the enterprise There are some news flashes that you will learn sooner rather than later hopefully but maybe more bats the face for some of the folks tuned in regardless with that being said, we've talked about infrastructure and this really pivotal concern. The you've talked about around wise initial projects fail and how the bigger and more ambitious oftentimes the more. Tragically they flop couple of things that we haven't touched on yet I'd like to get your thoughts on is around sort of talent and culture. So you talk about like well Geez, we couldn't. Get our hands on the data. We hear of a lot of pilots failing in just doing having so many conversations because enterprise leaders expect that it's plugging play or because enterprise leaders don't realize what kinds of use cases are realistic or not realistic. Let's call that like executive understanding. There's also people don't have sufficient house talent I've talked to the enders who actually help their customers hire in house data scientists to help them work with the vendor I mean like in that didn't even really surprised me that much because of how intense this stuff is. So there's internal talent as well. What are those other components? Infra? Okay. What are the other things that make it work what what else goes into readiness? Well. I think that the talent question really comes down to what kind of buyer a re talking about I believe there are either. Or aspirational enterprises that one have deep competency in ai to the level that they could build their own applications and and make it work. That doesn't mean they're going to build other applications by any means. But it means that you're actually overcoming almost an internal competitor to most of your value propositions. And whether competitor is real or not meaning, do they ever launch project and fund ten to one hundred developers to replicate what you build? It. May never happen. But what happens is you're fighting business case inside. which is a week to do this, and it would be one half the cost. So I think that what I see more often is you have that extreme, which is the you know we can build versus buy it. And Open source in the large tech companies open sourcing such substantial advancements in technology. Have made that less challenging than it used to be meaning. It's it's actually far more tree that you could build a lot but having said that it's almost like, why would you wanna Fab your own chips and building motherboards? It's more hobbyist thing to do I think we still have large institutions that are doing it and so you really want is. If, you have a lane where you can make a very distinctive value proposition. What you end up doing is you end up bringing on those people as champions because you're enabling them to show value. You're saying why spin the energy in this area? Do you really want to build a system like this and based? We have your internal products. Or instead you know, do you want to focus on what comes next? And by focusing what comes next year allowing them to get ahead of their peers or match their peers so far left if you will spectrum is internally I shop that's their next spectrum is the ones that think it's there but it's not and. That's really challenging because. Bad News to him. Yeah that that's that's where you have a handful of experts but not really believed implementer deliver, and so the expectations are said Hi and then a lot of the load comes on to you for delivery because the talent isn't deep enough or broad, enough enough volume to actually make themselves sufficient. And then you go to the next level over, which is you know they they have curiosity that may have one or two experts that are there for vetting only but they know they gotta buy they're looking for a solution and they recognize though that it's not fully turnkey and those tend to be some of your other customers to because I think the that they respect for this area had to go through and cost out what it would do to actually build a team to do this and make it function, deliver multiple products, and finally heal the far. Right. You have the you know what? I really want to check a box but I liked God this word somewhere like. Man Happens all the time and so so I think that you have like those extremes you've got kind of the almost like Saudi matter experts that are I'd Hansen the middle and then you've got check box checkers on the right that essentially one a little bit of of Ai Decoration because the idea they didn't take something when I was available makes them look like behind and on the far left you have the weekend bill all of it probably better than you and we pay our people more. Than you do and so you have that full spectrum I'm not trying to be negative. Need us now the talent the talent basically is my point is the talent issue is not one of having it or not he it's if you have the talent and you can have other false if you don't have the talent, you can have a of pitfalls and so that the truth is it's like it's all about humility of the enterprise and humility of the vendor to get to the truth, and if you don't have that then I think you end up with misalignment and it creates tension until you get to an answer either you have large enterprises try to. Build it and then find they're holding onto a masterpiece of technology and have to keep all this talent. Happy because when that talent NCFEI's it win, that talent leaves to go start a company. You can't make the system he built right this happens large solutions additions all the time. It's why you know generally you have commercial off the shelf software because they know that risk is catastrophic and then the next over is we're GONNA go pick something that's kind of demos well, and has ai, and we're going to elevate that even though we don't have any capacity to assess internally because what this stuff is not even well enterprise hard. What if it is doesn't More basic requirements, but it's just sexy new. So I think in the talent has to align to the mission of that you set the team won. But more importantly, it's the humility. Of whatever you really have. You could be you know J. P. Morgan have amazing people in talent or you can have nowhere near that budget in another industry that has like the one person that's got the to masters level in Ai that have been on teams it done in. L. P. twice. Like. You have that spread in the enterprise space, right? It would that spread just know where you're at. And then optimize appropriately and basically ask for humility, and then expected of the vendor to vendor over sells you or doesn't show the same then maybe not good partner. Got It. So little takeaway Tim, we'll nutshell this as we wrap up here it sounds like if I'm a leader in, we've got plenty of sea level folks tune into the show directors, etc who were looking at their own organization. There are asking the question are we ready? Where do we stand? You know we talked about data you talked about infrastructure you talk about what kind of projects to pick it sounds like another big maybe take home. Point in maybe reword this but another take on point is understand really frankly I guess number one where you are on the date in the infra. But also where are you with your talent and expertise as well to not sort of have to maybe feel bigger and stronger about what you have in house in you actually do but be able to say, Hey, look, this is what I think. We're good at what we're frankly not good at we have experienced where I think we're not and be able to guess readiness. It sounds like really involves a readiness to assess that very objectively if you are a leader. Itself Assessment I. Use a it's a cultural thing. Right? I guess I'm saying that like in many things if we were talking about in a different area than a even the still is true. A incorrect self-assessment leading to a misalignment is going to create issues in a I. It can create really substantial issues because. It's not very well understood yet even by the people that are the experts. In fact, what you find with some of the deepest experts the ones that have you cadillac some of the deep learning revolution. For instance, you'll find humility you'll find them talking about, yeah. We're about to hit a wall here really gonNA pattern recognition. We can do stuff and getting dictating years ago but you know when it comes to generalization and different kinds of signals choosing together and unsupervised learning we're just really the basics they're like that's what you'll hear like he actually been. been on the show five years ago. When you have those caliber or people who deserve the credit or You. Come at it from the perspective like that's all I need to know right that tells me that if they don't. Have a lot of swagger about it. Then who has right to? Yeah Nice I think that's a good point to end on. If if if COON INVENT GIO are swag about this, being a done deal in terms of making this stuff work in the lab, what makes anybody think that they should carry that into a business conversation where money's on the line? It's not. It's not like something where you turn into a skeptic money means it's just it takes humility and once again focus on the things like there's all these tasks that humans spend time. All right now that shouldn't have to because we're a today. You can make effective classification of a lot of things and you can triage and you can get a lot of multiple human time like that's that's an area that's highly under-exploited. The ARP guys are doing a tiny fraction of that and I mean there's a as a credit. They tapped into it at a lot of success. All these other kinds of signals language vision like we're just the beginning of that automation, and in the fusing of those different triage is into more complex ranking of what's important with a risk score that's areas which just need to be baked into these enterprises. Right and harnessing the feedback from people working on those tasks like always learning having a learning loop. Those are things that are right in the thick of the most competitive enterprises right now. So I don't WanNa sound like a ski at all. I'm just saying that I think people you know probably set. An expectation because they're trying to market themselves, which is human thing to do, and I think in the end people better served by you know humility I actually I really when I was younger in this I've been almost twenty years as a twenty years old boy al I've made all these mistakes. So I say this not from any variance like I've already done these mistakes and so I come at this and say, yeah, I remember when I thought we would get from here to here and it would take you know two years. Now I'm your tandem got. Not all of it yes. Problem big time in like you said, it doesn't mean that we can't solve meaningful promises with Ai. It just means we shouldn't take for granted where we stand or maybe over underestimate things but we need to look things frankly worthwhile lesson for literally anybody tune Tim I. Know That's all we have for time in the readiness episode year. Thank you very much for joining us on the PODCAST. Christian. 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