Enterra Solutions CEO Stephen DeAngelis on AI in Legacy Software

foreign speaks we're talking about the use of generative Ai and Enterprise grade business applications clearly Enterprise companies are adopting gen AI at a rapid Pace but there are some perils and some possibilities they're overlooking to discuss that I'm joined by a major industry expert with me is Steven DeAngelis founder and CEO at anterra Solutions Steve very good to have you with us today nice nice to be with you as well James you know I really cannot believe the amount of excitement about generative AI in the last several months like really over the last year or so it really has been a mini hysteria and of course we understand why there's an enormous amount of power and possibility of regenerative AI I think companies are not really even beginning to truly truly capture it I know you're close to the topic I'd like to get your thoughts as you as you survey the marketplace about how companies are using gen Ai and Enterprise business applications what trends do you see moving the market this year well I think that you know you had you had mentioned that there's been a little bit of an explosion or or a hyper awareness of generative Ai and I and I think you have to think of it as additive to all of the last several years of of work that's been done in the market about artificial intelligence and data science so there's been a little bit of a foundational knowledge laid over the last several years that now generative Ai and especially the way chat gbt operates it it brings to life artificial intelligence for companies in a way that machine learning algorithms by themselves didn't do previously just a short Interruption I think it's so true I think people it's sort of been lost that artificial intelligence is not new in the Enterprise it's been going on for several years I think it's been a certain amount of frustration among companies they put money into it and in some cases it's been an expensive science experiment but uh no really I think the consumer World woke up to you know artificial intelligence last year but the Enterprise world has been doing AI for several years well you know they've been doing AI but it's been in the domain of PhD mathematicians and statisticians right who are using things like we should um statistical mathematical models or machine learning algorithms to perform functions more efficiently within a business but they haven't been able to break out to use the technology in a cross-enterprise way and when and when people were able to see chat gbt over the last several years and the the sort of um the conversational or the front end communication nature of it it started to bring it to life for people who who weren't PhD mathematicians or data data scientists and it right it became more popularly uh available now we'll have I will tell you that AI is a massive Force for transformation in the Enterprise and when we look at generative AI our clients are are you to answer your question earlier our clients are using generative AI today and there's they're they're seeking applications for a technology so they can try it out right and they are but they haven't been looking at Enterprise scale application usually they've been consumer to consumer or business to Consumer applications usually in marketing related activities right um we at Interra have a slightly different perspective where we use it as part of an ensemble of Technologies to help transform the way that business is fundamentally operate and to me that's really the exciting um breakthrough that's happened is that generative AI has given the permission to many more business people in the organization to use artificial intelligence to now help transform those organizations and we have seen leading companies in the world who have adopted lean in Six Sigma previously competitive Advantage Total Quality Management years and years ago the same companies that use those advanced management techniques are now adopting generative AI along with artificial intelligence and advanced analytic capabilities to transform the way they work so they could get a generation of competitive advantage in the marketplace and it seems like there's there's a lot of interest I certainly a certain amount of trepidation about using generative Ai and then it seems like when it comes to Enterprise grade applications a lot of those really Hefty applications are Legacy applications or at least partially you know Legacy applications and so what about the idea of building artificial intelligence or gen AI into one of these Legacy applications is that actually going on or is it only only the newer Enterprise applications so I'm glad that is a perfect segment actually good I gave you a perfect segue Steve come on and it was not the um the nature of that was or is that the for the last 40 years leading companies like sap Oracle and Salesforce and others have what's called transactional systems of record those were systems that that were meant to record transactions not make a mistake at scale but we're never engineered to dynamically think about changeable data in the marketplace and help companies navigate in a real-time environment their their business today we have uncovered a TENS of billions of dollar market size Niche for a thing that we call a system of intelligence think about a system of intelligence that sits on top of the data and process layers that reside within the transactional systems of record now you have a system of intelligence which creates the ability to dynamically sense think act and learn about the environment that businesses are operating in so you can navigate that you can navigate a rather changeable landscape dynamically and bring the concept of autonomy to to the Enterprise so over the last three years James we've had almost The Perfect Storm or petri dish for experimenting with a system of intelligence because we had problems with the pandemic we had geopol we had geopolitical problems we had in inflation we had rushes where during Ukraine affecting the global food and agricultural value chain and the Cascades to that so what happened was companies needed to be nimble at a degree that they haven't been able to be nimble previously and the artificial intelligence that we were able to use allowed companies to be able to analyze data generate a recommendation make a decision at the speed of the marketplace where you couldn't have humans manually cranking analysis by hand these Technologies allowed the leading companies to navigate effectively those dramatic Marketplace changes that we were experiencing over the last several years well all right good I want to make sure I understand the idea your idea of system of intelligence it's as I understand it sounds like it's almost an abstraction layer over an Enterprise application one of these good old apps we've been used you know 15 20 even 30 years in some cases hard to really rebuild that so you're saying there's a system of intelligence it's an AI layer above that that good old good old older application and it's actually interacting with it between say the human or the system and the application itself it's almost like robotic process automation at the Enterprise level as opposed to RPA at the at the workforce level um except it's not RPA RPA right it is it is more of a intelligent agent based function based on what I'm sorry it's an intelligent agent based function so we're robotic process automation says if this then we do that right this says if this happens under these conditions what is the optimal outcome that we can execute given the corporate strategy that you have robotic process automation can never even approach it right no no right so this is truly creating an intelligence layer that is the that abstract floor is liquid has a common brain that sits on top of the transactional systems or record generates insights and sends instructions to the transactional system to say system of record do this and do that and then it then gets the results learned from it and then uses that learning to inform the next decision it makes okay good all right I see that I see definitely the advantage of that I'm imagining you what the critics would say uh and so I'll give you a chance to answer what I think the imaginary critics might say they might say well we like that we don't necessarily want just an abstraction layer over the application we want the the AI built into the application itself we want that good old-fashioned database working faster and better because the ai ai is built into that rather than abstraction layer above it do you hear anyone saying that yeah no these are the abstraction layer James this system's integrated to the transactional system record aha okay all right so it's really not above it necessarily it is it is it is with it it is encompassing it yes I mean it's a if you think about in a technology architecture it's above it but it is systems integrated too so for example when we would do a trade promotional recommendation for Consumer package Goods firm the recommendation is auto is autonomously generated by the intelligence layer and then the instruction gets sent through an integrated connection point to the transactional system for execution right it goes so there's a two-way street connectivity and through that system so for example we in sap are innovating massive capabilities like that to drive transformational change throughout the organization by integrating the system of intelligence to the system of record or the digital core of sap which runs you know most of the Fortune 500 companies right it is a very very um powerful powerful combination and it is the next step in the evolution of Enterprise Computing is to Leverage The Legacy investment you don't want to replace the Legacy investment that firms the focus quite frankly sap and Oracle they've done great jobs right in the transactional systems or record they're very decades long durable but this now lengthens lengthens the the the time and durability of those systems of record and allows companies to leverage their legacy Investments okay so you really you're breathing new life into old dogs so to speak uh I totally get that that's I see the demand for that that's pretty exciting well then let me go to a different angle then let's say there there are companies that out there who want to optimize their use of generative AI in the Enterprise maybe eventually simply to use your system but I mean other than that which could be a good answer how can companies optimize their user generative AI in in their Enterprise infrastructure well part of the challenges James is that generative AI is a emerging technology where you're seeing the like the tip of the iceberg right yeah absolutely literally like the tip of the iceberg that's happening and quite frankly I think that generative AI is almost going to be to a large extent the provenance of large Tech firms because they're gonna they're gonna generate the massive large language models and they're going to have to over time validate the large language model because right now generate the the problem with generative AI in scalable Enterprise applications is that you have the garbage in garbage out problem right on steroids because right you can't validate that the information that the Gen AI is gathering from the Internet is efficacious so as a result of that and then when you try to do it internally you've been probably following the um recent analyzes on how what what the cost level is for to develop it internally themselves so I think if you liken generative AI as a massive and AI itself as a massive foundational platform there will be different players along the Continuum doing different things so large Tech like Google and Microsoft and Amazon and others will will will generate the fundamental large language models in the chat gbts capabilities but those are going to take tens of billions of dollars to build and curate over the next decade much like Amazon spend tens of billions of dollars to redo the retail um retailing right online right right this is the same kind of mass investment but then you're going to see firms like ours and others using generative AI in conjunction with other artificial intelligence right and machine learning and data science applications to perform optimization in decision making in very discrete but yet transformative functions along the value chain of firms that will help make that will help transform the way companies operate going going forward so I think you're going to see like a dual level thing where you've got large asset plays that are the provenance of large Tech and then you're going to see transformative business applications that are driven by firms like mine who use gen AI in combination in an ensemble with other artificial intelligence and data science techniques to build transformative applications for parts of the for the ways that businesses operate I totally see that and I I think you touched on one of the key issues which is the idea of the large language model it's going to be so it's going to be expensive to create a really proprietary llm and it's going to be a competitive advantage to have a really specific llm that can feed you the kind of data you really need and you know there's going to be large companies who can have an advantage of that I'm going to guess if that world of a large language model is going to be commodified and so it's going to be open to a lot of different people I I uh outside of a large Enterprise it might be citizen citizen uh journalist it could be you know small developers could be small startups to do even consumer applications I think that the world of the llm will be open to all agree disagree I I think it's open to all but I so any anything that is in our world Right is open to everybody but when you when you want to do search historically you've gone to Google or you've gone to some of the other major search engines and when you want to do when you want to buy ads or place ads you go through some of the major Tech platforms so I think there's going to be this bifurcation like I said some of the larger Investments could be done by Major firms of some of the Innovative yet still big applications could be done by emerging entrepreneurial firms right who have a great idea and they're going to leverage some of this but there's two pieces to the puzzle that gen AI James doesn't address itself one is the common sense knowledge we see in Terror we use things called ontologies and knowledge bases and within our platform we have the world's largest common sense knowledge base and that knowledge base um that has come from Decades of research rate that uh partner of mine has done has curated Common Sense knowledge so when you are when you want to reason let's say you want to take a large language model and put it in common sense context right understand things like you know can a can a um a grandchild pre-birth his grandparent right that sense knowledge base knows that ahead of time so when you put your reason about the information in an llm you need common sense to to combine with the knowledge you derive from the llms let's say you're casting a netting you're bringing information about molecular biology in an llm you want common sensical knowledge to apply against that such that when you then take the other piece that llms don't have which is a reasoning ability do you then create you use the thing called an inference reason or to reason against that combined knowledge and that's how we use um technology today you have this Ensemble of Technologies today along with very discrete mathematics to use large language models and generative AI in a practical way right and so I think you're going to find you know large llms done by large large companies or firms like you know like I said Microsoft and Google but then you're going to find almost like think about it as micro LMS that are industry or domain focused language models that address a particular part of the marketplace and then those will be used along with other Technologies to drive very practical business applications interesting okay makes perfect sense um I want to I want to get to the idea of of discussing the future and I also really want to give you a chance to say what anterra does I think you've pretty much covered anterra tell you what if you do sort of the nutshell the set of the almost 11 second elevator pitch of interior and then we'll move on to the Future because I I want to keep our whole presentation fairly time limited for so folks actually watch the whole thing so can you give us kind of the what in a nutshell how is it and Tara addressing the generative AI in each of its clients Interra is using generated AI as a part of an ensemble of technologies that we call autonomous decision science that help build a system of intelligence that we discussed earlier that helps transform the way that large companies operate by creating an intelligence layer that performs Enterprise class optimization decision making and learning and connects that intelligence layer by through systems integration into the Legacy transactional systems of organizations and through that we help transform the way that core operations along the value chain work like consumer insights Revenue growth supply and demand planning and we do that in a in a comprehensive systemic way without human being involved in the day-to-day calculation of those of those things we can act at the speed of the marketplace yeah by the way you mentioned sap is there is there an alliance with sap or is that you mentioned that in passing no we have an alliance with sap so and we have a alliance with Salesforce right and the notion there is we seek to connect the our our intelligence layer to the transactional systems of record such that the client has a seamless interaction between the intelligence layer and the system of the transactional system or record or the digital core of sap right okay great all right so let's let's talk about the the big question the the billion dollar question the future of generative Ai and the Enterprise I think there's some smart people all across America all across the globe thinking about that one putting a lot of a lot of thought and effort and investment into that one what do you see say you know two three four years out I wouldn't even ask you for like a long time frame but just maybe you know 36 months 48 months from now gen Ai and the Enterprise what sort of stuff do you see Steve I think and I could tell you sort of some of the lessons from the Practical frontiers of that today is we um we're building what I call an intelligent agents think of that as anthropomorphized knowledge buddies yep that sit within an organization so let's say you're a supply chain planner at a company you're helping plan the supply chain functions of a large corporation sure you will be able to within the next 12 in 18 months have a anthropomorphized knowledge buddy think about a um a a version of an R2D2 right I can I can see my own my own knowledge about it sure so so working next year that says hey James this happened last overnight our production plans were were affected by a b and c here's our recommendations for you to navigate that supply chain Challenge and here's the recommended course of action would you like me to execute that right so you so that is going to be that's going to Leverage The conversational AI part of gen AI right but it's also going to leverage the other technological assets I mentioned to you earlier so what you're going to see now is the cut you know we've been very accustomed to um autopilot when you hear planes flying without autopiling you've heard of autonomous vehicles right so yeah the concept of autonomy is going to be brought in pieces to the to the Enterprise where gen AI applications will augment not replace but will augment the humans playing roles along the Continuum of the value chains of very uh of of large mid-sized and small companies I just think it's a very cool cool future because now you're going to see these things as assistance as you know knowledge buddies that help you do your job faster more efficiently so you could spend more time thinking about the Strategic application of what you do and how to use your decision-making ability better um in your in your day-to-day operations and I think it's rather exciting yeah I definitely see the idea of an autonomous infrastructure developing that AI would would run quite a bit of it it would augment not necessarily replace some of the humans it might replace some of the humans we don't know for sure but the other interesting piece of that and this is complete you know Wild Card conjecture is that we know that artificial intelligence learns by itself to a certain extent an algorithm can iterate and iterate and iterate actually becomes smarter over time that's one of the Revolutionary senses of revolutionary ways in which AI is different from traditional technology I guess when we think about an autonomous system running a very complex Enterprise infrastructure the question would be as AI evolves over time and what should the management decisions is it going to make in its autonomous mode and I guess that's an open question I think if you think about decision making in the pyramid right and you have decisions on the bottom that are rather ordinary decisions that right we tend to render you know to a robotic process or you would render to junior level staff right here those decisions would get made autonomously out of the box yeah the next level the middle layer of the pyramid are augmented human intelligence where the system is generating a recommendation for the human to then approve and say yes I but you've yes intelligent agent knowledge buddy you've done a really good job analyzing that I'm going to execute or not execute that recommendation and then the top of the pyramid are decisions that are still in the provenance of the human but the AI has assembled the information package that the human needs to have in order to analyze it and make that human decision and then instruct the AI to go take an action so I don't think this is and this is not where we're going to have a clear bright line that says today humans do it and tomorrow autonomous systems do it but there will be a there'll be a transition where trust and reliability get built into that much like we have trust in autopilot on an airplane in much we have trust we're building trust now with self-driving vehicles Steve it's it's it's fascinating I I thank you for sharing expertise today it's going to be an amazing sector to watch in the years ahead uh unpredictable and then I'm looking forward to it uh thank you so much for sharing your Insight today and come back and talk on this again sometime thank you very much I appreciate it have a great day foreign

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