AI, Security & Compliance, + Managing Distributed Teams: Conversations from TAG Summit 2024

join Nile Ali smile and Rob oel for conversations with engineering leaders on the floor of tag Summit 2024 continue watching for conversations on the state of artificial intelligence security and compliance technology managing remote teams in a postco world and more with Daniel chopon jund dooo Kevin ketz and Danny fule Han part of the uh The Challenge and with customers is to make sure that we have a com a good understanding of what we can do with data set the correct expectation with what AI can do or cannot do and make sure that um the customer understand the capability of the team and how we can generate business value for the business uh based on the data that they have or potentially will have junao senior data science manager at Honeywell discusses the Challen es of working with clients at all stages of AI adoption why the best approach for each customer is highly tailored and how to align business value with organizational goals welcome to another episode of the engineering leadership podcast my name is robell I'm an architect at this. laabs I'm joined by I'm n smile I'm with magin X I'm the head of engineering and today we are joined by Jay-Z from Honeywell uh do you want to introduce yourself and what you do at course yep uh my name is jinda I'm the senior data science manager manager as part of the Ford inside team uh belongs to the Honeywell connected Enterprise all right well real quick before we started you were kind of telling us about the journey on how you arrived at this position do you mind letting people know like what you are by sort of by training and maybe explain a little bit about how you arrive at this position of course so uh I came from a mechanical engineering background as well as later uh went to grad school and got my degree in industrial engineering and operational research uh from University of Illinois Chicago nice and uh started my career doing prognostic and Health Management uh for the industrial sector and later as the uh device gets more and more connected with more and more data influx into the system we need to have a better way to analyze the data as well as to create a z value from the data for the business unit we're part of wonderful well listen obviously the types of things that you're working on are very high in demand everybody has their attention on it so what are some of the I guess the key challenges that you're sort of dealing with on a day-to-day basis as far as like getting the information that you need and maybe like scaling out the operations because it sounds like there's probably no shortage of people that want you and your team's support so you know what are some of the things that you're needing to do in order to kind of meet that need yeah I think a part of the uh the challenge and with customers is to make sure that we have a com good understanding of what we can do with data set the correct expectation with what AI can do or cannot do and make sure that um the customer understand the capability of the team and how we can generate business value for the business uh based on the data that they have or potentially will have how many of these companies that and partners that you're working with or just teams that you're working with are really ready to adopt these tools because I mean I think a lot of places maybe their data capabilities aren't really quite up to Snuff yet that's correct I see yeah so explain a little bit about how you kind of work with people to get them just even ready to be be able to support these tools the part of the things we do is to work Walk The Journey with a customer basically initially typically start with understanding what the data system they have where they are and then how we can connect them together to unlock additional insights hence leads to additional business value creation and create that line of visibility first and then see what AI or machine learning or all these analytics tools we have in our pocket can do for the data in a Consolidated way and that and leads to a business outcome that's clearly defined to the customer and how do you know you know with those business outcomes I know you know working with with data and AI sometimes it can be unpredictable depending on the type of data that you have quality of that data so how are you communicating you know what outcomes could be achieved with what's available of course and that's a very good question for us just because pywell is in a lot of these businesses in the industrial sector in the building technology sustainability technology Aerospace Industries and we have the domain understanding of how the business is operated and what kind of data is available and can be available and because of that and organically merge that with the artificial intelligence Technologies it is very easy for us to create the kind of solutions that can be easily adopted by the customer because of all the things we do outside of that around that as well including the inter interpretability of the prediction models through cause analysis causal analysis so not only the customer understand hey this is my prediction but also understand why we're making that prediction how we explain that and how they can what they can do to change the potential uh um adverse outcomes that if we don't even ENT about it it strikes me that a lot of people if they heard your title or were asked to guess what it is that you do would just assume that your role is 100% technical it's about putting data into the most complicated models and the newest tools and the newest Technologies but what strikes me what you're saying is that there's a very human element a very kind of interactive element a very social element to what you do how much of what you do is that kind of business and strategy and relationship and helping people understand what they're trying to accomplish and what they can accomplish versus you know just saying oh well we have all this data if only we had a better model that could handle it or is it a mix of both I think it's a mixed bag of both uh for us again coming back to the way we straty our analytics Solutions is not only we have the analytics capability and we are being the AI Center of Excellence for Honeywell but also we have to have the understanding of the business and speak the language of the folks on the ground to know what they're doing to be able to have a solution that can generate adoption because when there's no adoption there's no business value creation so that's a very important aspect of our data scientists to understand the business and the problem you're trying to solve and people we work with has been in this area for 20 30 years and that experience as well as knowledge needs to be incorporated into all the solutions that we build and deliver so what are those you know when you're working with people what are those big challenges that you face because you know AI and machine learning can be almost like a black box not everybody understands so what are those challenges and how do you overcome some of those challenges of course and we see we work with customers that um has a wide spectrum understanding of artificial intelligence technology they can range from hey uh with AI technology you can unlock everything and um I can do that with minimum control of data quality and stuff like that and on the other side of spectrum there's like hey we've been doing this business for 20 30 years with first principle uh as well as our understanding of equipment Hardware design we don't need uh artificial intelligence Technologies to get in our way so um I think it's just a like beginning of the conversation to set a proper expectation on hey this is what you have right now and with our technology this is where we can get you to and then to work with the customers as part of this journey to make them understand that this is our capabilities limitation as well as you know what you're doing right now you're missing this much of business opportunities if you don't leverage AI technology it feels like every day that we wake up there's a new model that's coming out a new capability that's being announced a new academic paper that's being released so as somebody who's kind of an expert in this field how do you keep up with everything like what does that look like for you what what portion of your time do you devote to just trying to make sure that you're giving your customers and your partners the most up-to-date information on what might be possible with their data of course and for us is we need to stay up to speed ourselves as well and there's needs to be a portion of time carved out from our current team to particularly dedicated to learning as create that AI competency within the team and across Honeywell as well so we're working with Partners to upskill our workforces uh in a very active way to make sure that they're connected and we have typical cadences within the team like um weekly knowledge shares and we bring people in to talk about the latest and we'll send people to uh you know industrial conferences to make sure that their up speed with was out there and how that that what you learn through these upscaling exercises programs can apply to our current Solutions and projects as well as you know products I love that I mean a lot of compan a lot of Industries continue in education is a part of the DNA oh it feels like sometimes in development and in some technical organ that sort of Fallen by the wayside but it's good to hear that that's still alive and well with your team oh yeah very much alive with yeah and I think that's a that's an area where a lot of managers right now are looking for ways of upskilling their teams you know so maybe you've got some advice on you know if you're you know if you're you know a software engineering manager and maybe not you're not in data science right you know what are those things that maybe some of those engineering managers could focus on for their teams of course and we have a lot of these learning platforms like Cera or Alex or udemy out there so make sure you work in conjunction with your learning and the talent management team make sure you have a clear strategy but of course you know being a manager part of that is to cultivate the team to make sure that they effectively using their time to upskill rather than hey here's a three-hour course from Cera and uh go take it uh with that maybe portion of that is only available for what you're trying to do on your day-to-day life interes so you need to have a strategy as well as the tools to make sure that the team can spend as little time as possible to get maximized outcome from the learning and it sounds like it's very focused learning for the problem at hand right so that you can go and apply it and then you get the value from it and then you continue to do that instead of trying to take like some large course you know or something like that which may take time but it's too generic you know of course and this is particular for example for for a team so we as manager need to understand what are the the skill sets the breath and deaths of the team where we are across different critical vertical for learning for data science like pong for example uh and Cloud Technologies and with manager taking on that responsibility and understand where the Gap is we have to have a team that the skill set actually complement each other as well to make sure that we have full coverage of all the skills uh that needed to be successful in this nice wonderful well listen as we wrap up can let people know if they were interested in what you had to say where they can find you online are you on LinkedIn or Twitter anything like that of course please uh am me on Linkin as needed we are always looking for good talents and for Honeywell I think um if you're interested in in to apply your data science skill across a variety of business problem from industrial to Aerospace to building to sustainability and uh please pick me up link in right well that's going to be it for us today on this episode of the engineering leadership podcast see you next time sometimes it's hard to bridge the gap between business objectives in Tech implementation and it can get messy this do is trusted by top names like meta Google and T-Mobile and they love helping Business Leaders fulfill their strategic digital initiatives check them out at this do. one more time that's th do T.C goal was to create uh do an energy Model A Basic Energy model building to predict the energy usage for one year and so we knew we had a basic set of inputs a very specific uh output that we wanted to generate which was the energy use intensity that's the one number that Architects are concerned with as it as it relates to energy and so we we built the tool with that in mind with sort of how can we simplify uh the the input process because all these tools that are out there today you know you have to input 100 uh individual items Daniel chopon CTO and co-founder of Cove tool discusses how the company transitioned from exclusively doing consulting work to providing a software that helps enable sustainable design in architecture manufacturing and Engineering hello everybody and welcome to another episode of our engineering leadership podcast series my name is robell I'm an architect at this. laabs today I'm joined by NY Alis smile with imagin X consultings and we have Daniel from Cove Tool uh Daniel welcome can you to start out for people that may not be familiar kind of introduce yourself and what Cove tool is absolutely so I'm the CTO and co-founder at Cove tool we a uh software that appeals to Architects Engineers um we started as consultants we build a software to help with Consulting and then we came full circle and now we offer consulting services to arctex Engineers we're very much sustainability focused so the energy and the uh sustainability aspects of of Green Design um and really getting in the door early with Architects Engineers uh to kind of let them know how their buildings are performing you know it's funny too because like when we're hiring people there's often this divide when you're talking to Engineers or anybody for that matter oh I want to work for a product company oh I want to work for consultancy so for you to have gone around this track a couple times what has been that experience for for you and for your team been for those mindset shifts between being service oriented and software oriented or is it just that you have a team that's kind of both how does how does that work for you absolutely I think for us we're set up well to handle that because we have a separation between software and product so the product team uh consists of Architects and Engineers as well so people from the industry uh and so they've really helped us to kind of get the Consulting off the ground in terms of you know hey here's the processes that we need to follow to produce some reports now can that be automated right and so the software team continues to build software you know it just may not all be user facing immediately but eventually the goal is all these uh Consulting tools will be kind of built back into the tool um and so you know we've gotten into AI to sort of help automate much of what the the Consulting arm is doing um so really it's been a pretty easy transition um the software Engineers they they typically you know they aren't Architects or Engineers they they don't have the industry knowledge and so pretty much they're they feel pretty adaptable to to new changes um but it it's definitely been a a mindset shift in terms of you know why are we building this you who are we building it for uh it's not just you know uh you know the user experience is still important very important but can we build things that make our jobs easier and our internal processes uh even better I know you were talking earlier that one of the challenges that you all face is that you know you work in an industry where people have a lot of knowledge deep knowledge of their tool set and uh and maybe not as much of a willingness to kind of explore new technologies can you kind of explain a little bit about how what that is and how you guys have had to deal with that absolutely so know Architects they're very busy people right and so you know they're typically obsessed with delivering the project U on time in budget and so you know to introduce a new software kind of could break their processes require retraining um and so typically the the the lower the barrier to entry in terms of the user experience or in our case you know this Consulting of hey we'll do it for you and eventually you know as you see the value of what we're delivering the reports the the St studies that we're doing you know perhaps you'll see that there is value in training your people on it and so but yeah the barrier to entry is definitely there there's also budget that that plays a big part as well uh software budget is usually a very small percentage of the overall budget and so you know there might be a 5% budget allocated to this Autodesk Revit takes up a very large portion of that yeah and so we're working with a very small slice of the pie uh so the Consulting lets us do is to build to the project uh and so we can sort of reduce that barrier entry and then provide training and and more so on an ongoing basis to to get them in the door you know one thing that's pretty interesting uh about your journey is that a lot of Professional Services companies they do want to end up building some sort of you know accelerator or something to help them with their uh with their service offerings right exactly so but but there's always that um that uh limitation because you are already providing you're already providing Professional Services and that's really how you're generating Revenue right and you know when do you spend the time to invest in some accelerator or tool like how was that Journey for yall like how did you decide to go from you know being a professional services company and then you're like Hey we're doing the Consulting work that's bringing in the revenue now we're shifting to investing in technology like what what like caused that shift well I think to go back to the beginning of the journey may help explain this a little bit so you we were doing consulting uh you know very much a hand crank uh process with Excel spreadsheets and you know there was a few scripts and things that we had but you know nothing to that you could just just say as a software and so it just made us more efficient when we decided let's build this SAS tool to use internally at first uh it made us more efficient and increased our gross margins we could deliver the same output in a much much smaller amount of time you know traditional Consulting project might take seven to 10 days we can do it in hours right and so it helped make us more efficient we started selling that as a product you know and then as we start realize that this Consulting thing still may be a thing because of the training the barriers to entry uh we realize that you know we can actually make our processes even more efficient by whatever we're we're building now whatever we're delivering these reports if we can automate that it's not only going to help us but it's going to of hit our ultimate goal of you know reducing carbon in the world uh and so that's you know like as much as we want to you know profit off of Consulting at the end of the day our goal is to get into the hands of as many Architects as possible um and so the more that we build in uh and build back into the software you know not only are we delivering the services with a very tight uh turnaround you know three to five days in some cases or even less you know depending on the the the uh length of the study uh not only can we do that more efficiently But ultimately you know the architect can still you know bring this on to their to their processes and things like that so well we've seen the reports and those are very detailed to turn around in such a short amount of time so yeah yeah templating is important you know uh we do use AI we're using it even more to sort of Drive the the input and requirements Gathering which is usually the biggest the longest task um and so we're using AI to sort of assist with that that portion and then you of course create the output uh as well it's kind of a mix of Automation and AI at this point how did you I mean to just kind of build off of that you know because I think one thing that's interesting is this idea of how much did you have to commit how much at at risk were you when you're building this tool did you build this up sort of one utility at a time like oh I need something that'll help me put this spreadsheet into a more organized thing and then eventually realize you have a suite of tools or did you guys sit down and go no we're going to game plan out we're going to do requirements document for this whole thing we got these people we're going to go for a year we're going to build like explain how how you conceptualized and and sort of set up that project you know for for how you thought it'd be effective versus the risk profile you were willing to take absolutely yeah so when we first started you know our goal was to create uh do an energy Model A Basic Energy model building to predict the energy usage for one year okay so that was like this is what we're going to build you know at a minimum we need to have a simulation we need to have you know understand where the building's located the energy codes that apply to it uh some of the default inputs for Windows walls installation that sort of thing um so we knew we had a basic set of inputs a very specific uh output that we wanted to generate which was the energy use intensity that's the one number that Architects are concerned with as it as it relates to energy and so we we built the tool with that in mind with sort of how can we simplify uh the the input process because all these tools that are out there today you know you have to input 100 individual items so we wanted to provide a lot of defaults um think into it Turbo Tax right sure you know it assumes a lot of things and you can of course drill into the details if you want to um so that's kind of how we build it is like can my mother use this tool you know can can she get in here and uh run an energy simulation from start to finish and so that was the original product from there we just kind of looked at the various steps uh in the architectural process from you know uh conceptual development design development and onwards um we sort of focused on the early stages of of design uh where okay you know they aren't necessarily you know building every you know filling in every little dimension of the building but you know we wanted to know like what are the general Square footages what's the you know uh where's the location and then that kind of led to some of the the other tools like the 3 analysis or you know where we do these like daylight studies that you see here um and just basically like what does the architect need to know upfront to to know if this is a viable project as it relates to sustainability um and so it just kind of naturally expanded over time so do you all see yourself now as a technology company or you see yourself more as a professional services and consultant I would still describe us as a technology company but uh you know I think we're we're starting to shift a little bit more into technology driven Consulting so to speak um and so I think it's important that um you know we have a lot of brand Capital brand recognition around the actual software tool right and so we want to continue to maintain that while also you know letting people know hey we do provide these Services we do uh uh and we can deliver them more efficiently because and at a lower cost because of the software so I think it's always important to still emphasize that as we're as we're uh pushing this Consulting thing yeah and the reason I'm asking because it sounds like you have almost like two different like tribes within your company right you've got the group that's working on the Professional Services but you have a team of software Engineers that are building the technology right so how are you keeping your technology you know technology teams engaged with what the company's doing you know when maybe that's not really the focus of what they're working on exactly so the the beauty of it is that the the product team which do handle the Professional Services they're also the ones building the requirements for the software that's Trad always Ro and so as they enounter new scenarios through the Consulting process you know they realize okay our tool may have these uh shortcomings let's go ahead and build this into the software so that not only can we use it for the Consulting but it'll just make the product better for for our existing users as well so it's kind of a a a continuous feedback cycle that that works really well um and so one of the the areas that we've been able to sort of cross over is we're building our our new Cove tool AI tool which is basically like a way for to to get people in the door in terms of like things like zoning studies uh shoe box models it's very like high level like hey is my building going to work in this location and then you know as they get that very useful information that's AI driven we can then direct them to we have these expert services that are also available if you want to get more in depth um into you know one of one of these Consulting studies and so we we're sort of creating this as the crossover between the traditional input output you know wizard driven workflow uh and the Consulting side awesome well as we wrap things up can you let people know if they're interested where they can find you online I don't know if you're on LinkedIn or anything and where they can find out more about uh Cove tool yeah so uh LinkedIn uh look up Daniel chapson that's chop PS n uh Cove tool our website is cove. tools uh we have a careers page uh everything's pretty well organized there uh so if you are looking you know we're we're uh very interested in in a lot of the talent here in Atlanta awesome very cool well thank you everybody that's going to be it for this episode of the engineering leadership podcast and uh we hope to see you next time we have a list of about 47 different things that we want to do with AI Technologies and it ranges from uh completing evaluation of evidence to doing framework mappings to helping our Auditors do a better job of auditing programs like an auditor assist type program Kevin kets CTO at risk 360 talks AI security and compliance and the key metrics Auditors look for when reviewing a model hello everybody Welcome to the series on engineering leadership my name is Rob oel I'm an architect at this. Labs with me today is Nile Alice mile I'm with imagin X and Kevin ketz and I'm with r 360 yeah so we're just getting a chance to meet Kevin about RIS 360 for people that don't know uh what you're about what your company's about could you kind of introduce us a little bit about what your platform and your tool does sure R 360 is a company that helps people build manage and maintain security and compliance programs so we can do everything from audit your program to manage your program Implement your program uh do your offensive security and we have a platform that underlies that all that's a GRC platform software as a service GRC platform very cool nice I know just prior to us starting you were saying that one of the things that you guys were looking at was a AI of course that's what everybody's talking about these days absolutely but like how does that factor into sort of risk management and the types of platforms and the work that you do like what what what are you kind of hoping for with Technologies like that integrated into your platform yeah so that's a big question we we're working with some folks out of Georgia Tech some of the students there looking at several different options we have a list of about 47 different things that we want to do with AI Technologies and it ranges from uh completing evaluation of evidence to doing framework mappings to helping our Auditors do a better job of auditing programs like an audit or assist type program so we we have a ton of ideas and we're just trying to narrow it down to what is going to make the most sense and be the most accurate these days so you know something that's interesting is I think a lot of companies right now are doing just that they're experimenting with AI could you tell us about you know what you're doing with Georgia Tech and what that experimentation looks like right now with Georgia Tech they have a tnm program where they have a group of students that are that work with industry clients like us like R 360 to do a a Capstone project where we actually come in and Pitch to the students and they identify who they want to work with and then there's a team of four or five of them that come in and do the project for the company and so what we Engage The Georg Tech Team to do this time is to do just that Let's Help Us identify the right AI large language model let's train that model on our specific data and then let's get repeatable answers accurate and repeatable answers to questions that we ask it that apply to those topic areas that I was referring to earlier interesting so that's what's interesting about that to me is that a lot of companies are out here trying to figure out how to do it themselves maybe they're not really thinking about how to make these types of Partnerships explain kind of the logic or how you found into that because that seems like a really smart idea to go and make Partnerships with people to help you make that transition you know without necessarily having to go and figure it all out yourself yeah so our um uh cheap Vibes person Jessica Lucas our HR Director uh developed a relationship with Georgia Tech and it was primarily to get into that Community Support that community and then hopefully over time start hiring out of that community so we developed the relationship with the tnm program and what we decided to do with that relationship was tap into that incredible resource pool and look at futuristic things that we want to do have them help us solve our problems right and one of the things that we think we do well is give them hard problems that are pretty ambiguous and that teaches them real world situations right there are no clear answers it's not a checklist kind of thing so when they get into this they get really excited they do a great job of coming up with Incredible creative answers that we wouldn't have thought of it on our own so like you said that partnership's very valuable to us and then they lay a groundwork and then we take that and build on that and then over time integrate it into our application into our platform and you mentioned a few use cases but what would be interesting is to hear are you looking to increase productivity within your company or are you looking to offer new serves to your clients like where's your focus like what are the outcomes you're looking for so that's a great question and I serve two different constituencies right I serve the external client and then I serve R 360 who's arguably my largest customer we built the platform to support our Auditors our offensive Security Guys and our uh people that run manage Security Programs so they're my biggest customers so I am looking to in introduce efficiency and I am looking to introduce capabilities to the external customers the platform users and so in fact you ask how do I decide I have it yet and that Georgia Tech team is actually working on one thing for the external customer and one thing for the internal yeah very cool very cool so you're talking about you building this platform for internal use first that's such a great way to kind of or I guess you guys have to dog food your own Solutions yeah uh we like to say drink our own champagne okay drink our own champagne that's higher class commodity there we go but like that's so important for companies right because I mean you know it's really difficult to sometimes empathize with your users you wouldn't think that until you're really behind the scenes working on one of these things and you realize I really don't know my users I think I know them but I don't but if you can be your own user what a powerful thing that is so what does that allow you guys to do so we we talked about it in terms of built by Craftsman for Craftsman right so these bespoke tools that are built by people who have done it for a long time I've been in uh cyber security since it was called it back in the late 1990s right so I've been through it I've run a lot of programs we have Auditors that have audited thousands of different uh Frameworks and companies over the years and so when we meet with them we understand what they need and we H they have a direct pipeline to the engineering team my group and we build it to suit their needs and that translates directly to our customers so let's talk about the technology a little bit you mentioned that they're selecting some uh large language models for you you're going through a fine-tuning process could you tell us about maybe what models they've chosen what that process looks like yeah so we went through an analysis with them uh and they looked at different large language models that we could host ourselves and compared to uh chat GPT and one of that um newest release like yeah which one it is every day you wake up there's something new so hard to keep exactly so they looked at all those and they gave us a list of person cons and we chose to go with for this particular project we're using the open AI Chachi peti interacting via apis right and one of the biggest challenges that we're finding with it amazingly enough is it gets lazy yeah we're asking it to do some heavy workloads and it'll start to do it and then end with something like and a continuation of this right yeah so one of the craziest things that we found about the prompt engineering side is if you tell it you're going to get fired it'll do the work oh that's funny right uh so I asked I asked the team I'm like okay so what about the carrot option you're using the stick have you tried the carrot option and they found and uh they actually did uh tapped into their community and there's some research out there that shows that the if you're nice to it and ask please and offer the carrot it's less productive it'll take advantage of you yeah what what kind of carrots you ask you telling you're going to give it some money please do you know yeah right money no but if you ask please and you're plight and kind to it you know it's less effective you have to sort of give it an objective and tell it that there's going to be negative consequences you know uh for it to produce it's very interesting that's pretty fascinating so how change over time obviously so how are you using chat GPT with your own data uh so we have custom gpts that we've developed right that keep our data um somewhat secure right the training model right now we're not using customer data we're using off fiscated data right cleanse data to help train it um until we better understand those security models and the implications and how we can contain it we're going to mitigate our Risk by using genericized data uh but we're using custom GPT loading it with uh data that we needed to understand let it learn that and then uh interacting via apis and scripts right now very cool very cool well listen as we kind of wrapping things up I mean you're still right in the midst of this process so maybe it's a little hard to look back it's kind of looking straight down at where you're standing but um what kind of advice or pointers beyond what you've already explained would you offer maybe yourself if you were to go back I don't know what it is months or a year now or whatever it is at the beginning of this process for other people that are doing this is this Partnerships the key thing here is it anything else you've learned in this process that people should take with them yeah I think that's a interesting question and if I were to go back i' would say absolutely take advantages or take advantage of communities that are available to you right that have the ability to tap into others around them for expertise right so that's one thing don't expect too much out of AI in the short term yeah don't expect too much there's a lot of work to be done to operationalize it make it consistent and make it trusted uh also don't expect to put something directly out to your customers without having very good oversight and understanding of what's coming out of this system is it consistent repeatable accurate there you go right are the key things to be looking for and amazingly enough some of the stuff that we've tried it's a challenge to get those three metrics well that's good advice because it's coming from the risk and compliance guy so you know that that's good advice um can you let us know where people can find you connect with you online or maybe find out more about rist 360 yeah uh rist 360 rist 360.com www risk3 the numeral 60.com and I'm Kevin ketz I'm on LinkedIn and you look me up there and find me there all right well thanks everybody that's going to be it for this episode of the engineering leadership podcast great this program is presented by this. laabs the framework agnostic consulting firm helping Enterprises realize their technical goals through staff augmentation Consulting project management on demand subject experts training and other Professional Services find out more at this. labs.com you know what I've noticed even on the development side with AI is just that the more structured the information that you give to these tools the better they're able to combine and align with what you're trying to do and and assist you and so like you're saying these things may not replace us what they might do is really put a premium on people being able to organize and systemize problems in data in order to to graft nicely onto these tools if anything people are going to have to become even better at product management engineering problem solving requirements um you know those are always important but I think they might even be more important in the future Danny fan president of soft density that discusses how he was able to succeed in a postco world through decentralized operations hello welcome to another episode of our engineering leadership podcast my name is robell I'm an architect at this. laabs today I'm joined by n Al smile I'm what imagion x Consulting and we're talking to Danny from soft density Danny can you introduce yourself and your company and kind of what it is that you do we'll do sure my name is Danny fan I'm one of the founders of soft density we are a provider of global teams to uh companies in the US and we've been in business about 20 years great and it sounds like your company really operates at scale how many how big is your company right now we scale up to about 3,000 people Global currently with teams in Eastern Europe turkey North Africa Tunisia and Latin America is a big Focus for us right now that's incredible how do you how do you manage to build and maintain that kind of culture and kind of keep control of something like that as you scale teams that are I guess integrated all over the world that are working with many different clients but yet still kind of your team explain a little bit about how that experience and that process has been you know I I look at things sometimes before and after Co right were very centralized before where everybody came into a depth Center and worked in centrally right but since then everybody has moved home and some have come back but as you know nobody wants to work 8 to5 every day in an office some do but uh it's a very decentralized uh operation although we still have activities and and uh uh people still come to the office but it's still very U there's a lot of local leadership that helps keep it together and it kind of rolls up into for us everybody you know kind of speaking the same language and so that's how we keep it together wonderful so what kind of Industries do you serve VAR Automotive uh real State Healthcare uh you name manufacturing Transportation Logistics anybody that has a technical problem to solve right all of them are right so how have you seen sort of the landscape evolve I mean you were already taking this back you've been with the company for a long time so obviously you've seen problems and the things that your clients are dealing with evolve a lot what are some of the big trends that a lot of your clients are kind of dealing with that that your teams have been having to pick up and and help your customer s you know what uh the big buzz word everybody talks about is what today two letters right AI so sure we're actually getting immersed into it without even uh you know having been ready when it first kicked off right but now we're we're in full gear and all of our teams are embedding AI into their operations and I I don't have a lot of details because I'm not at that level but I hear from a lot of our teams that oh we're already doing projects in AI so I'm very pleased that a lot of our customers are actually down that road with the help of our Consultants as well wonderful and how does that change again with the global aspect I mean the problems I mean I guess the problems that people solve are somewhat Universal but you know getting this information and uh this training this education spread across your team that's a pretty Herculean effort I'd imagine so how have you guys went about trying to build these qualifications are you are you bringing in people to do those trainings are are people motivated to do this like how are you guys building these kind of capabilities good question we do both actually we do some ex external training but we have a lot of In-House Talent that's also picked up and okay gone deeper and and and just from their own background had this experience even AI is not a new topic right generally sure sure sure so we just it's a matter of adapting and even we put on some contests with some prizes for for internal folks to build projects in that space to get people excited about uh uh getting into this field that's ever growing yeah that's a great idea giving people incentive showing them what you where you want to grow um what are the kinds of things that your clients are asking for right now in the data and AI space well it it varies and I'm not going to lie to you I'm not in daily contact with a lot of our clients here from our teams so I I will defer to them on those topics because I I'm not on on that side of it day today okay wonderful well yeah so I mean I guess that's what you were saying about being able to leverage the knowledge of having such a wide array of Engineers I mean that's got to be such an amazing value ad so one of these kind of capabilities or advantages that compounds with the more people that you have is that suddenly whatever everybody is good at they can bring to the team how how do you guys Foster that as kind of like a culture of shared learning and getting people to kind of share the things that they're learning outside of work or that they that they're expert in that maybe their peers aren't a good question we have a lot of formalized education but we also do a lot of lunch and learns and get togethers at the local level so that's that's one of the ways we do it so with your team being so distributed are you primarily serving us clients or are you serving clients in the countries like you mentioned turkey you know are you serving clients in Europe or are you primarily serving clients in the US it's mostly us and uh West call it I call it Western Europe but UK mostly so that's where most of our clients are it seems we have the most thirst for technology I believe yeah yeah very interesting and so as you're you know you've been around for 20 years so you've seen SE I mean that was very early days of the internet you know we went through mobile we went through a lot of different transitions over the last 20 years so where do you see soft tensity kind of going in the next 5 to 10 years yeah if not for AI I suppose well that's the big one right and I think I call teams our teams are going to become super developers right that's the term I use until I actually wanted to get everybody like s shirts you know because everybody thinks well am I going to lose my job as a a guy going to take over I think it's the opposite there's so much demand yeah that I can tell you internally we have backed up projects because we just don't have enough people and time to do them so what I think is AI is going to transform where we can move so much faster in in in doing so many more things and all of us Humanity I think will realize there's so much more we're going to do so much faster that I don't think it's a loss of jobs it's just an acceleration just like uh you know the Industrial Revolution in a way yeah especially because you know what I've noticed even on the development side with AI is just that more structured the information that you give to these tools the better they're able to combine and align with what you're trying to do and and and assist you and so like you're saying these things may not replace us what they might do is really put a premium on people being able to organize and systemize problems in data in order to to graft nicely onto these tools if anything people are going to have to become even better at product management engineering problem solving requirements um you know those are always important but I think they might even be more important in the future well and I've always held the belief that if there's something that can be done by a robot or automation we should let robots humans should rise above and we have capabilities I believe that are not yet known to us because we are stuck you know doing the things that machines can do so I think our brains can do even more as we get rid of the stuff that today seems like it's important but it's actually mundane that our our our mindes expand and can do a lot more than than we even consider and you mentioned it it's the Industrial Revolution it's almost like we we're we're watching our industry go from you know being Craftsman to now going into like more of an automated system and that's going to be we're going to see that unfold over the next 5 to 10 years and see how it really impacts everything I'm on the same page as you though this is increased productivity for everybody and there's going to be more that we can do not less in fact one of our customers yesterday were we talking through I I again I don't deal directly every day but I was talking to one with yesterday and they're in the video production business and so one of the things they're talking about is um they produce a lot of educational content for school systems and the way they're looking to do it is writing the content once let's say in English and then once you feed it into a a a system you you get synthetic human like we call them right yeah yeah yeah to teach in their language so it can it can translate the English into any language so if you watch it in French Spanish the the the Avatar will talk in that language or the synthetic human so you write it once and it it proliferates you have every language available to AI to be able to provide the same instructions so now you don't have to be sitting in front of a video taking video the youate your content instantly once you write it once that's a huge step forward for this company using AI as one example I think about earlier that's you know when I sat down with teams to talk about how they would use AI I think that's one of the things that people need to learn is it's not that it's going to replace the things that you're doing it's sometimes finding the things you're not doing because it's not a good use of your time or you just don't have time to do it and often times AI can do this it's kind of like what you're talking about right like it's not replacing necessarily the creation of the core content it's saying well I would love to teach this in every possible language but I don't speak those languages and it's a lot to to translate it and stuff like that and it's those kinds of things and it's really fun to sit with a team and I'm sure you'd agree when you're sitting there and you're saying well oh oh that's what we can do with this like that's amazing I would love if I had time to do that well to that point there's a lot of resistance people are are used to doing things a certain way we have to have the video camera we have to have the mic we have to have the right conditions but in this specific at least example we had to peel them away from thinking that that you don't have to do things that way right this is a lot more efficient and especially for uh K through 12 they like the Avatar experience they don't mind the synthetic view of a human where my generation might look and say is that a real person they don't care and we've done this company's done avatars and synthetic humans and I think the kids like the avatars a little bit more yeah yeah but the resistance is something we have ingrained ideas that we need to let go of so that we can this is where expanding our minds is going to help grow AI the right way versus thinking it's going to just take over the world yeah well great well listen can you let us know where people can find out more about you or about soft density if they're interested in you know connecting with you online of course softens city.com easy to well I have it here but yeah we're glad to talk all right well wonderful well thank you so much that's going to be it for this episode of the engineering leadership podcast and we hope to see you next time thank you all right awesome thanks so much very great thank you this program is presented by this. laabs the framework agnostic consulting firm helping Enterprises realize their technical goals through staff augmentation Consulting project management on-demand subject experts training and other Professional Services find out more at this. labs.com