AI-Driven Transformation in AEC: Navigating Data, Strategy, and the Future

Joe Giegerich: 

Hi, everybody. Thank you as always for coming out to listen to the ProjectReady Podcast. Today, we’re going to continue to work around this one theme that we’ve been working quite a bit, which is data process and today about work in the AEC as being driven by AI, so. Talk about the transformative processes that are coming into play in the AEC today. Which is everything from modern cloud tools. The progress of different CDE’s and tools out there, but more importantly around AI and its impact. And today we’re lucky enough to have Doctor Samaneh Zolfagharian on the line. Today she’s the co-founder and president of YegaTech and Sam, if you don’t mind, once you just introduce yourself for everybody. 

Samaneh Zolfagharian: 

First of all, thank you so much Joe for having me on your podcast. I appreciate that this is someone Sango favorite. Ian. I’m the president and we are helping the architecture, engineering and construction. Confirm with their AI strategy and governance based on what happened in the last few years, most companies wonder like where to start, how it all is going to impact our roles, responsibilities, even our business model. Shall I charge like less because now I’m using AI so we are helping them to navigate this? Place and understand the value that they can deliver. To their clients and also to their employees. 

Joe Giegerich: 

And so they’re on bottom line too. I mean, I am from New York. It’s always about the bottom line. So and this does pick up on the things that we’ve done earlier like with Salah who we both know Salah cart did garbage in garbage out. So it the future of data is not this second but the journey. Begins this second. Would you agree with that? Like you’re not just going to turn on AI? 

Samaneh Zolfagharian: 

That’s true. Yeah. As you pointed out, like garbage in, garbage out and AI eat data, we need data to build AI. And if we have like. Low quality data or not even having enough data. We can build an e-mail system that could be impactful and can help us with what we want to achieve. And the question is like people ask is like shall I capture whatever data is out there and that’s going to be difficult. And the first thing that most of them do is just. In building Data Lake, but on the other hand we have some solutions platforms out there like Project Ready or other solutions that are out there that they can. The question is like which data you need to capture based on the AI strategy that you set so that you can accomplish your goal and achieve what you want to do with the AI power. 

Joe Giegerich: 

Yeah. And a data lake is a man made lake, right. So you need to know how deep, how wide, and what kind of canoe to traverse it, if you will. And you know with the challenges in front of the industry right now. I mean, you know, this is a fourteen $15 trillion a year industry. There’s a lot of pent up demand for housing certainly in the United States and worldwide. So talk to me how you think just on a very quick level how AI will help the industry deliver more of the built world. 

Samaneh Zolfagharian: 

So as you pointed out like 14. Trillion dollars housing market that we have ahead and in the meantime we are facing the global warming. So we build and then there some of them they get destroyed with the global warming issues that we have natural disaster. That we have like flooding in Florida right now. So how AI can help the process of designing and billing takes time and the question is like how we can accelerate the design process and also construction process with AI. How we can eliminate some of the repetitive. Tasks that we have within this process. So that we can design and deliver faster. One of the things happened in the history like 100 years ago was in the factory. Everything was manual and people had to fasten bolts manually. But eventually with industrial revolution, some of those tasks. Delegated to machineries and that process has been accelerated and that’s why the economy of scale is possible today with. Why? I’m not going to. I’m not saying that it’s similar. Here is just a tool. Is just a little bit of a powerful tool because it can learn from the data that we have. And the question is, is that our power that we have on our hand, how we can eliminate some of those high volume? And repetitive tasks that we are doing through design and make more efficient decisions in terms of the design that we do so that we can meet the criteria, maybe the cost, how I can pick up. The billing products that are cheaper so that I can build more economic house for low income families so that they can afford the price later down the road in terms of energy like energy is one of the main factors today that is impacting the global warming. The question is how I can optimize. The building design and make it more energy efficient so that I can save and again make that house more economic in for the low income families. 

Joe Giegerich: 

Yeah, carbon footprint. Right. Carbon footprint is. My understanding is that the construction end of it certainly forget Ricky. I mean, sorry, Richard Metzel is. Yeah, some like that of the world’s carbon is produced by. 

Samaneh Zolfagharian: 

Yeah. 

Joe Giegerich: 

The built world. So you know if AI can help you make more intelligent decisions quicker, that’s very impactful all the way up. 

Samaneh Zolfagharian: 

I was going to say that 40% that you’re highlighting is like it’s throughout that design like as an architect, if I’m designing like what like you mentioned, like what factors I need to consider to make it more and like make critical decisions that are more efficient down the road and also the billing operations itself like. How we can make sure that not only through the design but also the operations is energy efficient? 

Joe Giegerich: 

Yeah, full life cycle. Now so. Mentioned the data lake and the like and so from our position we really have built Project Ready as the Scalable data warehouse to turn AI onto and to just standard analytics and search from the like right. So we bind everything together by the project ID, it’s highly scalable and the information that we collate. Selective if you will. It’s all in context, but tell me how your point about great you built the data lake and you flooded the plane. Tell me about what you would advise people out there as. To go. On that journey to start to make sense of that data, to collate it, what they should be doing and how you. Would approach it. 

Samaneh Zolfagharian: 

So the way that I’m looking at is like the company, they invest millions of dollars and it takes them like a year, two years to build that data like and eventually since they don’t know which question that they, they can get answered by that data like that data like turns into like data. That is just there. They just dump all the data and they don’t know how to use it. And it’s really important to educate your executives and employees to understand how they can leverage data that is there so that you can make a use case of the data that you’re capturing when you are thinking about. Either billing data like or going an outsource it from the existing data platforms that are out there. The second one is just not having policy. So today, employees, they have even they have like data lag or something similar. Employees have this freedom to store data wherever they want and the context of some context of the projects are missing from the data that we have. So that decision making is not going to be as efficient as it. Could be because there isn’t any governance, any policy around how you capture data back in the time just had the policy to mandate API like everyone should have them shared so that they don’t have to do something. That it and also they could all these employees could have access to that, but how we can have such a similar policy regulation for our employees. So when we are storing data, we’re capturing data, we know that we are capturing all essential and necessary data to make the business decision. Down the road, I was listening to the podcast you had with Shelly. Last week and you brought up like 90% of the construction data project is lost. No one is leveraging that. But if we could have that regulation policies and making sure that employees know what to do with the information and where to store it, at least we can reduce that amount of data that has been wasted. Today and the third one is going back since we’re talking about. AI in the book, augmented by Doctor he was using a metaphor as grocery shopping. So and we are leveraging that metaphor in the current book that we are writing disrupted because it resonated with many people when they’re thinking about AI strategy versus data strategy. So your AI. Strategy definitely must be alone with your business strategy because your strategy is not going to drive everything. It’s your business, so you need to make sure that your AR strategy is aligned with your business strategy. But to make that AI strategy work, you need data and that data fit into your. It’s fed into your AI strategy and it’s like grocery shopping. You want to have a party, so first you need to know what you want to cook before going for grocery shopping. You can just go and buy whatever is off on the shelf and bring it home and then just decide what you want to cook because there will be some ways and you may forget to buy everything that you needed. For food that you wanted to cook so that data is like that grocery shopping item and AI is like that recipe. So if you know what you want to cook, you can follow it and make sure you have those ingredients in order to make it work. So just make sure your data strategies along with your AI strategy should be aligned with your business strategy to make that whole ecosystem works well. 

Joe Giegerich: 

Yeah, basically, if you’re going to cook a lot of Mediterranean, make sure you have olive. Oil and. Coal. Well, I keep it very well stocked, larder. I love to cook. But there. But you know there are some intrinsic challenges with this as well and one of which is to just to go to the social element of it. And Shelley, I would have you weigh in on this. You know, you we go to clients and we go, well, you should be doing this. Your employees should be doing that. I find frequently you get pushback going. Yeah, but my, my, my employees don’t want to do that. I don’t know how you address that but surely haven’t we met resistance like that? 

Shaili Modi-Oza 

Yeah, I think that is definitely one of the biggest problems with so many different systems and methods of doing things. Taking any workflow as an example, even within a company, No2 departments, no two people are. Doing things the same way, which makes it very difficult in terms of that data warehouse that we are trying to build that. For the data to be consistent to be able to track and log metadata properly, it needs to have that consistency. There are so many different systems, so many different ways of doing things, and as you mentioned, your people don’t really want to change how they’re doing it. Some people are just using Excel and some people are having various manual methods and different. Systems to set these things up, it’s a definite challenge to have. That. Consistency, even in a single organization. 

Joe Giegerich: 

Yeah, Sam, I think you mentioned Bezos at one point. You know, he can make people wear adult diapers. He can do whatever he likes inside an institution and. But. But I think one of the things that just struck me now though, is you mentioned, you know, this sort of triumvirate, the business drivers, the business needs, the data and AI and how they all have to fit together. And so if you have a business strategy that you want to apply to AI. I would imagine it’s easier to make the case to your staff as to why they want to embrace that and embrace that change and. And let me ask you this. So when you work with clients, what is the pushback you get from the rank and file, if you will as? It relates to AI. 

Samaneh Zolfagharian: 

It depends who you talk to. Some there are like a group of people and definitely in every other industries we have this that they want to wait and see. They see it as a height and they want to wait and see what happens. And if other companies are immersing this change. You’re going to have any arrow. Right. And I would say in the next 10 years, if we look at in the next 10 years, we are in the slowest time in terms of AI advancement. Is going to like be faster in the next 5 to 10 years. So if I want to embrace it later down the road, I might be late to the game. So there are groups that they’re just wait and see. Wanna see what happened? There are group of people that like executives, they value AI and they’re trying to get the buy in from their CEOs. And it’s difficult to convince them because they’re like, we don’t have capacity. We don’t have time to think about AI. We need to deliver these projects. And they’re trying to get the buy in and convince their CEOs in order to do the investment. And the third one, I think it goes back to what you mentioned, Joe, earlier, like how we get our employees along. The CEO is on paid and. The CEO is trying to see how. If he or she can align the whole organization with his or her vision, that has in mind. And it reminds me of Simon Sinek code. Like your employees buy, why and then they support you in order to achieve your goal. If we can justify why we’re doing it and why we should do that. It we can, it helps to justify it for our employees and get their bike if we just ask them to do that. The challenge is like most of them are scared of losing their job. Because they think if AI can do that, then am I going to be replaced by AI so they may not be supportive of that initiative. But if they understand that they are not going to be a replacement for them, is just increasing the efficiency, making sure that they can close their laptop, go home after 5:00 PM rather than working overtime. Or even during weekends. That that’s really a case for most of the clients that we’re talking to right now. Then they will be supportive of that initiative and our industrial problem is not the tech problem. We have many solutions out there. We have over 10,000 solutions out there. It’s just people problem allowing them getting their support. And one of the easy way is just making sure that they understand why and also setting incentives. Like the incentives can be in terms of bonus can be in terms of some encouragement awards, setting metrics saying like if we can increase the data that we store in this structure in this manner by 10% by the end of this quarter. Then we provide you this what we provide you this like something that could be. Like can make employees feel that they’re rewarded for their contribution. 

Joe Giegerich: 

Yeah, mutually beneficial, right? So AI will reduce rework, increase the efficacy of the bottom line, all that kind of stuff. So it shouldn’t be just isolated to, OK, I got all the money. Thanks, guys. Right. You know. So no, I agree. And there’s a lot of you know. Movement. Everywhere now to try to go to a four day work week, which actually kind of makes sense to me. But you won’t be able to do that unless you can do things more efficiently with some level of automation. 

Samaneh Zolfagharian: 

I was going to say something. I was thinking about the past, what happened in the past, back to industrial revolution or even forming in 19140% of US employment. It was forming, but then with industrial machinery with automation it decreased to 2%. So I don’t think like any of us want to go in form and do everything manually by hand today. It doesn’t make sense to us. So most of those have been automated and today the we are in the digital world. And in digital world, there are some tasks that you are doing, like if they’re repetitive and in high volume. And the question is like if we look at paths, how we can automate some of these high volume and repetitive tasks so that we can be more efficient and focus on something else that we like and back to your point, draw like how it can enable me. Help me to have like 4 days of work rather than like being burned out. Yeah. Yeah. 

Speaker 

Joe Giegerich: 

The quandary is, I guess, so for every shift in technology, everybody’s always been worried about job obsolescence and it’s been job replacement. I think the thing though that is most concerning. Though is. How quickly can the workforce adapt to take advantage? Right, I think that’s what actually scares people. But you know, you mentioned like the rivet, I mean, a nail gun means you can build houses faster. It doesn’t mean that carpenters are less valuable. It means that they’re valuable skills which are carpentry, aren’t getting bogged down using a hammer and a nail versus a nail gun. Right. Essentially the analogy. Yeah. And then one other thing I wanted to pick up on that you had mentioned. Was, you know, it’s like a 5-10 year journey, so there is hype around AI. I’ve made this argument, you know, certainly as it relates to the stock. Market. What’s been released, what it promises is enormous, but I don’t know if it warrants like, you know, stocks going up 120% within a year, right? So there’s that over a hype that I think really does exist. But to your point. It is here and you have to be ready for this because you know companies who wait till the last minute will be former companies. They will no longer be in business. That’s my absolute belief and I and even down to the so going back to the you have to map out what you want from AI to align your two business processes we did a. A podcast around copilot with our friend Luke Gucciardo not too long ago and Shirley, you’ve been working on copilot to try to bring it into our stack. I know we’re not quite there yet, but like talk about a bit. Like. You have to actually. Tell AI you have to give it a model to give you a return. Is that I’m probably doing a poor job of description, but can you pick up on that? 

Shaili Modi-Oza 

Yeah, definitely. Basically in terms of the data that feeds into it, the models that are just available on the Internet, it takes the data from all over the Internet, the different websites, the more specific you make it, the more intelligent responses you’ll get like talking about copilot and M365. It now integrates to all of your documents, your emails, your conversations, and adding in any additional metadata and workflows. That project really works with or gives that. Feeds that data basically in like a whole project level approach where if you e-mail somebody about something, you’re having a conversation on teams and you have this workflow going on in Project ready about let’s say it’s all about an RFI, but all of that is fed into this model and because it’s all kind of connected, it can. Give that intelligence response. But it’s really important that. That data feeds in correctly in terms of all the data that goes, and would have the context of a project, and then it doesn’t matter if the RFI is and project ready or Autodesk. Or pro core. But the whole team is kind of working on things around it and then once that data is it, it can give you intelligent response. Is on what’s overdue across projects across programs kind of information, which otherwise it would take you hours to. See through reporting and try to find that information here you can just ask copilot something and it’ll give you a whole detailed, analyzed response of what’s going on a project, why it’s overdue. So I think it’s very powerful, but it does need that right input of data to make it powerful. 

Joe Giegerich: 

Well, and even in the configuration there was a video. Shared with me a few weeks ago where you have to train the AI model with what is essentially. What the business value would be if that makes sense, right? You have to train this model going with these parameters. Here’s your trust level and around these outcomes, which goes back to your AI strategy mapping to your business strategy, mapping to your data strategy, right, that’s that triumvirate. 

Shaili Modi-Oza 

Yeah, it needs a data model to be fed in in a in a way that it can learn from it for. 

Samaneh Zolfagharian: 

Sure. And back to your point about AI being hyped. I don’t think we had a day that we didn’t hear the word right in the last 2-3 years for sure, and one recommendation for people, audience for listening to this podcast and this is something that we recommend. There are many noises out there and it’s really difficult. To find out what to do and filter all those noises and one recommendation that we give. Or Members our client is trying to have inside out the strategy which goes back to what Chile and Joe they were talking about earlier like know what you want to do know what you want to solve and then go look for solution out there. Otherwise if you have outside in strategy. That makes it difficult because you’ll be in reactive mode and it’s going to be tech fatigue for your employees, and you probably end up to be all over the place without any MRI. The MRI will be like. The long term process it’s going, it’s not going to happen in like a night or six months or a year. You have to be patient, but make sure that you’re not reacting to all noises out. There you have. Inside out the strategy rather than outside in a strategy when it comes to AI. 

Joe Giegerich: 

Yeah, you’re right. There’s a ton of noise out there and being a New Yorker, you know, Wall Street is sort of central to the entire area, right, that that it reminded me of. So when we had the major crash and. 2001 I think it was or whatever. Yes, 2001 the.com bubble bursting. It wasn’t that the Internet didn’t matter. Clearly it does. We’re having a conversation using the Internet. We’re recording this over the Internet. It just wasn’t this instantaneous. If you will paying free journey it required understand what the business application was building the proper infrastructure, what you would use it for and development around it. And so it was a bubble not because the Internet wasn’t prior to AI. The most important thing that had been. It’s because it’s delivery, the desire of financial circles in large measure to monetize their value in the markets. That’s where it becomes overhyped. And that’s where I think it starts to drive the noise that you mentioned. You do have to filter out and it goes back to all right. So if you thought the Internet. Was a fad. It’s not. And if you think AI is a fad, it’s not. But like the Internet, it’s not going to happen overnight. In terms of the benefit that you will see. But if you’re ready for it. You can benefit from it. Well, let me ask you this. So what are common? You talked about aligning AI to business needs. If you give us some examples that you’ve, you know, working with your customers, some things that have. Some universal. Spark of interest. 

Samaneh Zolfagharian: 

Definitely must come like firm they’re debating with. Lack of workforce, even though you’re saying that they are going to take over my job, but we’re still in like we are dealing with hiring, we’re dealing with finding talents out there. So getting attracting the right workforce is a big challenge for this industry. And most companies. I would say all of them that I’ve been talking to, they’re trying to see how they can manage their hiring process and can find the right talents based on what they want to do. And in that case, they want to see like how AI can help them with the resource management and how they can find out like for the projects that they have. Or they’re going to work on who to hire, how to navigate their skills, how to optimize it. Because some employees might be overburdened with the amount of work, and some of them may not have enough work. And how AI can help me to have visibility over the whole ecosystem. More my organization to optimize the workload of my employees and the other thing that they’re trying to do in terms of RFP’s. So today they spend lots of time in terms of preparing the RFP and managing everything and the. Ratio of winning a project is less is like 1:00 to 10:00 and they want to see if there is a way that I can help them so that they can optimize that process. The RSPB and just move on to the next. Leverage that capacity resource for something else. Trying to manage, optimize the workforce and trying to see like the repetitive tasks, with what LLM and enable today they want to see. Like OK, how it can enable and optimize my RFP process. 

Joe Giegerich: 

Yeah, I’m glad you mentioned that cause I was about to seriously there. There are two. So in conversations with Shelley, there’s two points here and this is sort of we just did just release that I think today about optimizing the back office and this is something that is ignored by AEC firms, not everything is. Is the design or the build? It’s the business behind it as well, and so we just did this podcast, just came out and it’s about optimizing the back office is one of the many things we. Do. Really a lot of value to it, but when we were talking about the benefit of AI for our customers, Shirley mentions, you can say. But that there’s plenty of application with the off the shelf AI components right now, like for instance around HR and to bring up your point on the RFP’s. I’ve been in the space forever. We’ve been doing collaboration, design work, cover some services and now product within that industry. It’s the marketing department that frequently answers the RFP’s and they have to access who certified and what kind of portfolio did they work on, what you know, what photos should I include. So if you have well orchestrated data about. Project of Type skill sets success and outcome that you can. Chew on with AI. That should greatly expedite the RFP process and so think about if you win 10% of those awards, but you can increase the number of RFP’s and respond to tenfold. That’s a huge net savings that’s outside the strict province of design and build. 

Samaneh Zolfagharian: 

Exactly. Yeah, that’s OK you saw me. Very well, Joe. Thank you. 

Joe Giegerich: 

I tried. I I’ve said this before on podcast, I’m very good at looking smart by report repeating smart things I’ve heard. So that’s I guess that’s my particular 4 type. Yeah. And. And so finally on that note, I would I would also say that. Start your AI journey there, right? You’d agree. Just the stuff. Well, you just said it right, you know, start with HR. Start with marketing, RFP responses. So that now your workforce can get a taste of it and they can see their lives improve as a consequence of that adoption. 

Samaneh Zolfagharian: 

Yeah, exactly like, like, these are common problem. Back to a point draw. And so if there is a solution out there is there is something then you. Can go and. Adopt it and in the meantime, when you’re addressing these common problems that you have, like the whole industry has think about some. Unique opportunities that is just for your company, for your organization. Because of what you do. Because the way that you do the work and see how I can help you to expand to scale that opportunity, the unique opportunity that you have, because if when it’s unique to you to your organization, big tech companies or startup, they’re not going to focus on it, it’s like. The 10 total available market is small, but so that’s where maybe it was to invest and explore. If you want to do something internally in house, but if something is general and applicable like the whole industry has that problem, why you should go and invest on it because eventually take vendor can do it better than an AC firm because that’s what they do. They build software and solution. 

Joe Giegerich: 

Right, because I’ve been asked on our own product. Well, do you? Who? Who? Who’s your? Who’s your audience? Right. Or is it architects, engineers, construction owners. And when I tell them, we have a pretty wide swath. We have government. The whole bit. They go well. Why I go? Because they have. These common problems. Right, you have common problems of efficiency, common problems of data aggregation that you can use. They have problems with simplicity of workflow management, reporting and all that OPS stuff that we’re always on about. I guess then you would agree if they start with the low hanging fruit, the universal stuff that’s out there for AI, that would be the beginning of the journey. And where would you go next with them at that point? So you start bringing on, let’s say, copilot. You’re doing stuff with HR. Or from. 

Samaneh Zolfagharian: 

There one recommendation and this is what we usually do in our process is education. So make sure your employees understand the foundation of AI and it’s more than tools out there that are LMS based like chat and it’s more than that. It’s like it has different techniques that can help you with your job, and it’s really important to make sure that your employee is educated or educated, because when we understand. Something we are in support of that and we know how to manage that or how to work with it, but if not, then you’re just pushing it back. We don’t because it’s out of our comfort zone. And the second thing that we do with this company after education is having an AI task force because the AI is changing fast. I mentioned for the next 5 to 10 years, we are in the slowest speed today when we compare it to. The next 5. To 10 years, so you have an AI task force that can help you with that AI strategy. Setting the AI strategy looking out to see what’s happening and based on what you have the business strategy that what you have internally, what you need to do. And once you did that, you should be able to replicate that. Innovation process within your company, not just with AI, but anything else that come up later down the road. So you need to have like a team that are educated and are focusing on that. And there are companies AEC companies out there that they even don’t have, like any innovation technology team, any anyone that can take the lead on this aspect. And my recommendation is make sure that you have such a role who can take the lead. Because you’re busy with your day-to-day work, you can’t spend enough time on figuring out the landscape, so it’s good to have one person in charge of this. 

Joe Giegerich: 

Yeah, we, we I’ve referred to the rise of the digital innovation officer, right, it’s a title I didn’t even see until like five years. 

Shaili Modi-Oza 

Yeah. 

Joe Giegerich: 

Or at least in any great measure. And you’ve been seeing that escalate for reasons like this. 

Samaneh Zolfagharian: 

Yeah, exactly. Yeah. Or Chief Data officer, which is really important, yeah. 

Joe Giegerich: 

And to pick up on your committee. SharePoint is my original background going back 20 years and the like and that recommendation I always made to my customers and still do is to not push down but to collaborate in a structured way. But to be inclusive. And for AI to learn stuff to help you do stuff, it needs to learn from your employees, from your department leads and it there really should be a certain level of inclusiveness. I would think in getting those, you know, what would be a benefit to feed those people who would be managing that process right to be inclusive. That was my picture on democracy doesn’t always work though. Yeah. So as you, as you March toward. What would we like to do for AI? What would serve the business right on that journey? To not relegate that to like, you know, three people, you know, one guy you hired and two consultants. But to actually learn from the departments and the different areas, their vision of what they would find assistive, right. So if you can educate them on AI and its potential, wouldn’t it make sense to get a feedback from them as to what they think would have value? 

Samaneh Zolfagharian: 

Yeah, and this is a good point. Is like some companies, they just have that like task force from the executive level. And but that’s not how it works. You need people in week that they do day-to-day work and also people who are in the executive level. So the combination of these. Who can help you to lead your to sell and lead your AI strategy and data strategy? Because people who are in the business level. So even though they they’ve done some work in the weed level in the past, everything has changed. Yes. Yeah, exactly. So you need to have a combination of both. 

Joe Giegerich: 

They don’t do the daily work. Right. Because AI is supposed to assist you in doing the day-to-day repetitive stuff. So if you’re an executive, kind of by. Definition. You don’t really have a great handle on what that is. 

Samaneh Zolfagharian: 

Exactly. Yeah. So you come, you know, the business and you know, like the revenue, you know, those details, so you can bring those values in the conversation with people who do the work in their daily job. 

Joe Giegerich: 

Really, I can’t think of any place else to go at this point. Sam, is there anything you would like to add? 

Samaneh Zolfagharian: 

For the audience, if they’re interested to learn more about how they start their journey, we have lots of resources on our website and they’re for free and is negated.com/free dash resources. You can go even look at some of the. These studies out there and see what other AEC firms are doing in terms of their AI journey, if you like and are curious to know what’s happen. Me and with the leadership, those who are ahead of the game in the market. 

Joe Giegerich: 

And if you have any thoughts or comments on our podcast, we really do welcome feedback as well. Richard Matzelle, who’s head of marketing here. Rick, if I can ask you a question, can people post questions to us either via the blog or from the page where the podcast live? 

Richard Matzelle 

Yes, of course. So wherever you listen to your podcast, you could obviously leave a review or comment on the episode. And UM, if you receive an e-mail with this podcast, obviously you can reply to it as well, and we’re happy to answer some. 

Joe Giegerich: 

Questions there also, yeah. And take suggestions. Right. We’re always looking to try to figure out, you know, what’s of interest to all of us in the industry. So we’re. Very open to that as well. Anyway, I’m Joe Gingrich. Thank you for joining us today, Sam. True pleasure. 

Samaneh Zolfagharian: 

Thank you so much Joe, for having me, Richard, Sherry, for your support. I appreciate that. 

Joe Giegerich: 

Of course. And Shelley, as always. All right. Thanks, everybody. Talk to. You next time. 

Samaneh Zolfagharian: 

Thank you.