Okay, everyone.

Howdy.

How are you all doing today?

Good.

I'm doing good.

Thank you.

So today we have architects and researchers from where Malcolm joined, joining us from different offices.

We have, doctor Nick Watkins.

We have, we have, Heather Griffin joining us.

We have Lauren Cheetham and Ashley Abbott.

So Doctor Nicholas Watkins is, he holds a PhD in environmental psychology and specializes in evidence based design for workplaces, health care in public places.

Heather Griffin, is an architect at where Malcolm focusing on sustainable and impactful design across corporate, healthcare and education spaces.

And she's also the director of the Interior Architecture and Design.

And, Ashley merges interior architecture with technology driven design innovation.

So I think some of you will have, a good conversation with her regarding the work that you're doing for the studio afterwards.

And Lauren, she concentrates in environmental psychology and human factors, and she's an alumni of Cornell University.

So thank you all for joining us.

Please help me welcome them on the podium.

Thank you.

Thank you.

We gotta check this out.

Hello?

Hello.

Oh, but I'm not going to hear here.

Oh, it's just for the.

You're going to edit this part out, right?

Okay.

Thank you.

My gosh.

All right, well, thank you for having us.

It's such a pleasure to be here.

And prior to such a fall esteemed series, it's been going on for quite some time.

I am doctor Nick Watkins.

Background in environmental psychology.

Director of research and strategy with.

Where?

Malcolm joined here with my colleagues.

And why?

I'm joined by a lot of colleagues today, or 3 or 4 of them, as well as because we wanted to show the translation of our work from research through AI and machine learning, our rule based logic to how we apply and to our work during design delivery and have new insights and innovations from that.

Now, where we are currently is we do a lot with computational vision.

You might be familiar with that and what cameras can do.

We're going to go in some detail there about, how it can help with brand consistency across projects, which is very important.

So you can recognize that Starbucks in any town you are at.

We're also going to talk about how it's been influencing our design delivery process to not only make it more efficient, but more accurate and have higher value to our clients.

So, we call this automated intelligence.

Because we take a human centered approach to our AI.

And we'll get a little more definition about that as well.

Now, put the cart before the horse here.

And what we'd like to do is share you share with you today some of the tools we've been actively working on.

And we are in progress with, that are adopting some best practice health care design guidelines.

You might be familiar with the VA, Fi and so forth.

As well as adopting evidence based design principles and some of the latest, so we would like to share some of that journey with you as well.

Now, I've been around since the beginning of this, I've been in this industry for about 20 years.

When I started, there was the question of what is evidence based design?

I said, I don't know, it sounds like something I was being trained to do.

So it's really why is this an exciting time and not something to necessarily be fearful of, which I think is kind of what's purveyed and pervasive in the media is that, for me, what's exciting?

It makes the data live.

It makes the research real.

Before we would, have kind of a publish or perish mindset or like, we produce results, it would go on a shelf or hope somebody would read it.

Guidelines.

Right?

Well, now it's automated.

It's in the moment.

It's right at the point of relevance for decision making, which is very important.

I spoke to how the team is translational, so we're going to be having a few handoffs in that regard as well to share how translational we are, with information.

That said, I my team.

Can you introduce yourselves, please?

Let's start with, Lauren.

Oh, yeah.

That's right.

I'm an interior designer on the research and strategy team, and I work with Nick, and we translate design research into app application.

So.

Hi, everyone.

I'm Ashley.

I'm a technology specialist on our digital transformation team.

We work with all the disciplines architecture, interior, workplace strategy, and create efficient solutions that kind of simplify things for our teams and, build efficiency and innovation.

Deliver.

Oh.

Very good.

Okay.

Just, But, Heather Griffin, I am in our Houston office, so just an hour and a half away.

Pop over.

Come see me.

I did want to touch on.

We are from all different places.

So Lauren is in Washington, DC.

Ashley is in Denver, Colorado.

Of course I'm in Houston.

And then, I'll let you tell us about Nick.

Tell me where he's from.

Scott.

Over the country.

Yeah.

Well, you have the lapel.

You're good.

Oh, okay.

With both of you.

Yeah, I'm a little bit everywhere.

I am all over the country these days, from California to DC, working my way up to New York City.

So thank you for hosting us here today.

In part because your weather is fantastic.

Everywhere right now as I talk.

Right now.

Oh, I got it.

I can't, it's now.

And I can do that.

Okay.

So.

But you had, you know, a little bit more about ourselves.

I like to hand off and have other to tell you about our organization more broadly.

Yeah.

So where?

Malcolm, we're a national firm.

We have 28 offices across the US.

We're also in Mexico, and we have a couple locations in Canada as well.

We have a multitude of sectors that we specialize in health care, corporate, civil engineering.

Let's see, we have workplace automation, science and tech.

So we're we have our hands in a lot of different types of industries that we specialize in finance, manufacturing, cold storage, industrial.

The list goes on.

Oh, yeah.

So we are going to talk about the digital transformation.

So, the development of DCS has really been something that we've been pushing recently.

And that's why we have our experts here to kind of dive into that for you guys.

They are solving a lot of problems for us.

And that's what we'll touch on today for, just different industry types, different sectors that we're evaluating.

Let's.

All right.

So in me.

So what are the goals of today.

You might have read the learning objectives, to gain stronger insights on, what is human centered AI and how we've been practicing it and then sharing that information out, also where how it improves our efficiencies, but also the value we contribute to clients and to our, organization, I think is something you often hear as well.

It frees up more time.

And then there's a question of like, yeah, really?

What's most important that we're seeing in our practices actually increases the confidence of designers, when they're making design decisions and also helps them address things in a more timely fashion so they feel more empowered.

This is a very robust case for visions and potential path and periods.

And we take, recommendations and solicitations all the time, every day for multiple clients and how to juggle that.

So it's also helping us to be better, better particulars of our tasks.

That said, we also want to conclude with an exploration of some of our next steps and where we want to go, including the end process tool for model generation and what we call space planning for.

I won't be leaving that, that said, there's other learning objectives today.

And where are we now in the broader world of AI?

We addressed this in an article.

It's coming out this June, for Real Estate Journal.

We where we're at is we are an industry that is fairly well laid adopters that very eager.

So we're at that stage.

But I'll say, when you're part of my life, currently, I can promise $20.1 trillion in growth.

By 2030.

That's fantastic.

That's a lot of opportunity.

That kind of a challenge as a lot of us don't know where we are.

And so you have upwards of 75% of people surveyed by a variety of companies these days, saying that they feel like their jobs or some cats or part of their jobs are going to be a place, and that causes anxiety.

So there's the dilemma, there's the adoption, and then kind of what to do with all that time and capability and where do we move from there?

And we hope we are at least some example of that and provide lessons and learn.

The current automated landscape, I mason landscape for automation, intelligence, as we like to frame that is that, it is growing.

All right.

So information with chips is growing with doubling every two years.

Right.

And to say it's getting smaller is now a smaller.

Well now with AI you see more than doubling and information and capacity and capabilities every six months.

So that's a lot to take in.

It's also something we as humans cannot even comprehend.

It's like asking us, okay, what is the what is the size of the universe?

It's mind blowing, right?

You know, the distance to the moon is astronomical.

I don't.

So when he writes about it and what it means for implications across because of something you're I. Now, we hope that this falls, away.

Some of that concerns and focus on some of the benefits, based on what we experienced.

All right.

Yeah.

So paralleling, this push for human centered AI has been an increasing acceptance that the real estate and design processes should shift to earlier phases of projects to maximize value and minimize risk.

So shown here is the Boyd Paulson curve.

It's a concept in construction project management that illustrates the relationship between the ability to influence project cost and the cost of making changes over the project's life cycle.

So, as you can see, as the ability to impact the project decreases early in a project.

So that's the conceptual and planning stages.

There's a high influence over the cost because design decisions are still flexible and changes are relatively inexpensive.

But then as the project progresses into design and procurement and especially construction, the ability to influence cost decreases while the cost of making changes increases significantly.

So, this is where Malcolm's reimagining of the Boyd possum curve.

And it's about expanding this idea that's decades old into automation.

So how can automation benefit corporate real estate engineers clients?

Earlier in project delivery, especially as projects have grown more complex over time.

And this is a positive trend that's emerging as organizations, in real estate tech industries, they begin to use automation to kind of meet the goals of the Paulson curve, which is all about efficiency and accuracy.

But oh, I think oh, I think you're oh, yes, oh, yes, I have a little yes, we have a lot of different media efforts on the internet.

That said, okay, so there are some challenges and opportunities where we are in this industry in architecture, engineering, construction, allied real estate industry, with AI adoption, and how we can improve on it with the greatest promise that it can really improve the performance of facilities and performance, human experience.

These are what our clients are interested in, kind of science and technology to health care, to workplace.

They don't think in terms, square per se.

If you're a CEO or somebody on the C-suite, you want to know about retention.

Are you building a facility or having an ecosystem feature, you know, the ecosystem that employees, are after them and that they're bringing their best to their jobs and, and even having some kind of fun?

So what we're seeing is a movement away from some, hopefully the fear, with some with what's called explainable AI that has practical purposes and is transparent in our world in establishing trustworthiness, all the way up to things that are more targeted and not generalizable solutions.

Right.

So, a lot of research throughout my career has been about finding the right solution that seems to address most people in the world and find the best practice or standard.

AI is particularly powerful at finding, personal preferences and meeting those personal preferences.

And the moment, for your kind of information, for the product you want to buy and so forth.

The experiences on Instagram every day, which that, it also enhances the value basis for design and what we offer in our clients AI is by being able to meet, to meet their requests in a much more timely fashion, with higher accuracy, with fewer errors.

Also with just better ideas.

When you're not in that reactance mode, you are definitely more in a creative mode.

Or I can choose to be.

And that is where we move towards innovation and performance for our products and what we deliver to the world.

Thank you.

So we take a stance, a human centered AI, sometimes called explainable AI, but it's really a large part about, AI that meets us in the moment of the decision you need to make, either your research moments, when and or consulting or insights into kind of strategic planning or master planning or operational efforts, or even just when working in the lab and and understanding what space options we have and how to lay them out for maximum visibility, say, applications or maximum of other companies in the world.

You have teammates between now and the with clinicians leads to better downstream, experiences for patients and better outcomes, including the big one, mortality.

If you think about EVs and some of those other settings, we are in the process, putting more evidence based design principles into our decision making tools.

We have been ingesting, if you will, the guidelines, sometimes specific guidelines that clients have, for their own brands and identities, but also the best practice ones like MGI, VA and so forth and are so publicly available.

We are also looking to turn to ingest some of our own operational data into the decision making process as well, so that we're just not this is a profound shift.

This view is a lot of the research from marketing and PR and thought leadership towards the actual application.

So if we do a nationwide survey of nurses or, physicians, then we can make able we are more able to in real time.

And of course, the highest quality data for of decision, one of our interior designers at that moment, I would have dashboard for when coming to the science fiction.

So we are on a journey with this.

I think there's a lot of whiplash and interest with AI.

It is a lot of work actually has to do with some of you.

Have some direct experience with it, with coding and with getting the right data that exist.

So it's not like money, time and effort goes out the window.

We also know and are very much aware of the energy costs associated with running AI models, at least in this country.

As we refer to as of late, which demands a really high infrastructure in electricity and other sources of energy.

So we start out with rule based logic.

So algorithms that is really are determinative in a certain kind of outcome, either in a simulation or our agents should be within that space, either to triage blood draw or, and I'm speaking to healthcare context, from observation that to ICU.

Right.

These are very sequential acts.

You can time all the way up to establishing of course, the point of decision making, which we're very heavily at right now, and then moving more towards establishing thresholds and goal relevancy projects.

We have been building that into a recent whole, you'll see in an animation and, which is very exciting.

And actually the team have worked very hard on, and finally, really looking towards the visualization of these results into augmented reality, that are very pliable, bendable and shareable with us, groups across the globe, not just in the same room.

Next slide please.

The the opportunities, primarily the scientist on top is that the promise of them is becoming a real.

I think that's been been has created more work, and more.

And you might be familiar with some of the literature out there and seeing the studies that the math creates more actually, it's more more seats and more transactions versus, creating a more efficient process, which has not been ideal.

We do see it again, that opportunity is moving from just kind of the, like I said, the kind of alongside the decision point of making some kind of better resolution of imagery, which we will show you.

And moving along into, I think was particularly exciting.

We have a conversation earlier today seeing where there's consistency between the design program and ultimately, what the expectation of performance with design should be like in healthcare.

To be patient satisfaction scores, we could be, hospital infections in that it's unfortunately for health care highly quantifiable field in terms of performance.

So it it is definitely a strong pilot for this work.

The brand consistency score, when dealing with mind and body solutions, how scalable we can adapt this to a health care setting, with the right variables that in this setting, which was a banking and or let's see, an informational communication happening with that child.

And that's, we were looking for color consistency with the guidelines as well as layout consistency.

Now, if we look at what output is the decision among their decisions, among their stakeholders, among, stakeholders and as well as ourselves.

So we all have situational awareness regarding the thing so we can all make a decision around.

The most recent I thought I mentioned, we have this slide that, what you see at the top here is, some freshness that need to be addressed across the sites, across the nation and on the ground.

How many are being addressed and coming up in the queue?

It's very small, but that's what needs to happen.

Then you actually have easy to grasp moment, an opportunity for decision making and I think.

Next slide please.

What we would like to show you today and spent in process so you can get behind the scenes in the clinic design system.

We see this growing more over time to other aspects of health care and departments, and, you know, help you, and guide you behind that curtain.

I will pass afterward.

Yeah.

So we walked through a tool that we had already developed, brand compliancy.

And this is a tool that we are currently developing, the clinic design assistant, it integrates automated intelligence into the software designers are already using to enhance their decision making.

So in this case it's plugging into Revit.

And the goal is really to improve engagement with the designers and give more time back to the designers.

So applying best practice and evidence based real time decision making support during the design process, to increase efficiency and deliver greater value.

So we're freeing up time for more creativity for the designers.

We're building up confidence and insurance and their decisions.

Increasing awareness and inspiring, change and bringing these guidelines and evidence based designs, research into, into the process directly.

So how can human centered I help you may be familiar with some of these guidelines that already, exist from the center of health Design or from the Texas Department of State Health Services.

We chose to integrate specifically guidelines and checklists from the Pact module, developed by the Department of Veterans Affairs.

So patient aligned care team.

So the VA, they use, a consistent packed clinical space module for planning and designing, all of their VA facilities.

So whether that's primary care facilities, or other, care facilities, this module contributes to a standardized environment of effective and efficient care.

And so we we focused on implementing guidelines from the past module because, its guidelines are best integrated into automated intelligence tools when they're repetitive and general.

So they can be applied to multiple different projects.

And so we wanted to make sure that we were focused and kind of on like hard numbers compared to abstract concepts.

And so the VA specifically their pact is they use an integrated clinic approach.

So they configured the space into two different zones, the patient care zone, which they call on stage, and the team work zones, which they, call off stage.

And this is focused around improving workflow and privacy.

And the general objective of the VA pact module is rooted in providing patient centered care.

And then with that, coordinating the care and making sure that there's proper access to the care.

And they they do this through a team based model.

So they're they're focusing on patient care specifically, while also thinking about how the space can be flexible and adaptable with time.

So it may just it may be the same room size, but how can it be configured differently if the programing changes over time?

And so we wanted to implement guidelines that are focused with standard codes and requirements so that repetition is key.

So VA clinics, they have the same staff.

They had the same patients and the same needs.

So that is very well integrated integrated into automated intelligence tools.

So as Lauren had mentioned, the VA has a really prescriptive method for clinic designs that follows a strict rule set and a large component of the VA clinic can be recognized as what they refer to as this Pact module that Lauren had mentioned.

And you can see this here.

And the image on the left, the pack defines this as staff and patient zones, keeping corridors separate and having dual entries to both exam and consultation rooms, really bringing that care to the patients.

And beyond this layout, each pact is required to meet certain criteria such as counts, room types, team to patient ratios, different distance minimums and maximums, and so on.

And these hard values and standards are what make this VA pact very well-suited for an automation exercise and helps it verify if conditions are met or maybe not met within a model.

And so this image on the far right shows both consultation rooms and exam rooms.

And you'll see that they have the same ratio.

And they can be kind of interchangeable within this layout.

And then on the further right you'll see four packed modules and how they're able to be stacked within a clinic design.

So when we were first thinking about the development of a tool to kind of assist with this clinic design, our goal was to create a tool that would aid in maintaining consistency with the guidelines, as well as including embedded, evidence based research to help guide decision making for our design teams.

The solution we came to was the CDA, the clinic design assistant, and it helps visualize alignment with these BI standards or VA standards, as well as we've incorporated a foundational library of pre-designed base layouts, and we can use these as building blocks when beginning a design.

Thinking about test fit or space planning.

So how does the CDA look at a model?

What is it looking for?

The CDA looks at a variety of items within a model, mostly related to count layout, dimensions, quantities, and this might look like checking for separate patient and staff zones.

Could look at checking door clearances, or different dimensions related to the pact.

Also looking at reporting room quantities, ceiling heights, and then even applying these values further to evidence based design concepts to optimize design for desired conditions.

Starting to reach different goals.

So we'll show a demo here.

Oh.

Perfect okay.

So this is within the Revit model.

You can run the tool on your model.

And then it'll automatically pull out all of those packs within your project.

And you can review them all together or separately.

And below you'll see there's this pack checklist.

And this is some more of that find criteria, whether that be those ratios of exam rooms per team staff or different pack depth being how many rooms are stacked, different clearances.

And here we're just cycling through these different packs showing you how you can look at them individually.

And then there's a design optimization tab where these numbers are applied to this evidence based design concept.

Seeing if your team ratios are maybe saying if you're understaffed or overstaffed, starting to look further into your design and really apply those metrics to something, helpful in guiding your design.

So as a result, we ended up with two components to this design toolkit.

First being the library of the pre-designed base layouts.

That kind of help optimize your starting point.

And you'll see these on the left.

They're just kind of these exam rooms, consult rooms, and they're built respecting the VA guidelines already.

So it kind of gives you a head start.

And then secondary.

Is that Revit plugin that we just looked at.

And this really helps you review the VA guidelines and evidence based design standards.

And brings it down to a consumable format, something that's visualize something that's directly in your model, and brings everything to one source.

And this helps you visualize everything in real time, and helps you kind of to be prompted to fix or fulfill unmet requirements within your model.

Yeah.

And the impact of this tool, from what we see, it looks like with progressive use of the tool, it could cut, cut design time in half.

By what Ashley was saying, reducing manual checks, minimizing errors, streamlining workflows by bringing these guidelines and evidence based checklists into the program for designers to check against their model in real time.

That are knowledge, right?

So where are we at and going forward with this?

We have again, we brought up evidence based design a few times where we use that in the model is particularly with ceiling heights, with views into the rooms and with the privacy curtains and so forth.

So these are kind of like little on off switches we've been able to put behind a little f condition to be able to put it into the model.

We're wanting to layer and more with something called space syntax, which we call the layout forecasts.

That's the capability we have to simulate, visibility and accessibility to teammates and, patients and anyone.

This is an advocate for the these, veterans care.

We want to continue advancing towards that data driven design support.

Fortunately.

And something we're particularly proud of is we do monitor the progress, and how how how well, these are being adopted.

Ashley and team, have a pretty strong rollout strategy and monitor it.

And we have access to training videos and, and ourselves, at any point in time for our firm where a firm about 750 people.

So it's not prohibitive to just call somebody opportunity to, we also have big events that, transcend any office to bring all of us together, which helps in communicating these ideas and concepts as well.

To the whole the full suite and complementing, assistive generative design models, possibly even with more and more into the digital twinning domain as well.

So, if we go to the next slide.

That brings us to, I know you're familiar with AI models, and sometimes they have an extra finger or a thumb.

This is actually a situation, from a clinical setting where surgeons have a prosthetic additional, that used to help them during surgery.

So the nice way to end things and then launch pad into the future.

So any questions, thoughts or impressions?

Yes.

So don't forget, we will get them on the floor and and Brant.

Okay.

Hello.

I'm wren.

Thank you for your presentation.

My question is, so right now we're kind of in an AI bubble, so we are the expectations for AI are very high.

We we are in a time where we are, dreaming of what I can do and seeing where it goes.

But it's not like a finite source, which I'm sure you guys know depends on, real life resources.

So, what do you where do you think?

Realistically, if you could say, where would we be in five years?

Realistically, with your own experience of what AI is doing right now and not so much what we think it's going to do in five years, maybe want to go first or I think you know that.

So.

That will be so on my side as the designer and implementing directly in front of the client.

It's really going to be using the tools that are currently being designed.

Right now.

We're just kind of testing.

People are doing it on their own for whatever they find out online.

Like I saw this on Instagram.

I'm going to try it in front of the client.

So right now it's just the Wild West.

So I think within five years it's really going to be more of a structured implementation.

There were actually able to see results.

We had on one of our hypothesis that we feel like with progressive learned use, that it can cut the time in half.

Right now we're not seeing those benefits.

But I think at five years we will start to receive those benefits.

Yes.

Some of the benefits we are already seeing or in other markets, we're having advanced manufacturing facilities.

Some of our workplace corporate interiors projects as well, have seen benefits that have translated into millions dollars for the company that we can take and invest elsewhere, which has been particularly nice.

I think what I find exciting and then Ashley, sorry, I also take that it's pulling in more of the data that's related to the so of human experience, and how we fit into it, and going and really pioneering that Wild West to collecting that data, and calibrating model accordingly.

And I kind of see two options for it.

So the way we're doing AI right now is we call it automation intelligence.

And it's working with more predefined rule sets.

Things that are very repetitive have patterns, things we can predict.

So those are the easiest to automate right now.

And that can be things for health care checking guidelines.

Corporate companies do have very specific prescriptive kind of rules for how they layout spaces.

Those are the easiest to work on at the moment, as well as what Nick mentioned about bringing more evidence based design.

And this is something that's based on values, on metrics, on distances.

It's something we can calculate within our software currently.

It's just not being actively used too much.

So that's where we're headed.

Definitely bringing in more of the evidence based, evidence based design and bringing it directly to the designers is a big part of it.

And then in the future.

So five years, we're looking at getting more towards actually AI, where you're thinking about artificial intelligence and maybe generating test fits or space planning in a model before the designer even puts too much input in, and creating a starting point that's further down the line, so we can push designs out the door faster, give the designers more time to actually design, and kind of get rid of the repetitive, tedious tasks that aren't related to the profession as much.

So there's many options for it in the future, but we kind of see it as both of automation intelligence and artificial intelligence, with artificial intelligence being more closer to that.

Five year mark.

I my.

Gosh.

Okay, so can you go back to your, the model that you put into Revit and run the automation?

So what parameters in your Revit model do you already have to have set up to be able to run that.

Like what phase of your project do you need to be at.

Like is this something that you can have like just walls and room tags, or do you need to have, more developed project to be able to run that effectively?

So right now it's exactly what you said.

If you have walls and room tags, it's starting to do that.

So essentially what it is doing is based off that fact module.

This is a team zone and it collects every door that opens into that team zone.

So it's reading all those exam rooms and consults as belonging to that pact.

And being able to read those off of the room tags, then it can do all the other things, such as calculating those dimensions, quantifying them.

Program files.

Correct.

But since it is for the VA pact, they have kind of the specific naming for exam console procedure, certain room types.

So that is kind of like our naming schema.

So it works within our models.

But there's other ways to program it as well that maybe it's looking for a furniture component like maybe say for to recognize is an exam room.

Maybe you're looking for an exam table belonging in a room, and that's a way to do it as well.

So there's never one way to code something.

There's normally like 5 or 10 other ones.

So there's lots of opportunity within that.

So look it gives it if it runs a system and it finds that affected the way your layout can do it will it rearrange that stuff for you or will it kind of give you like notes suggestions and you may not have to update it?

Right now we're at that point where it's more of prompting you, hey, maybe you should look at this and think of a change, and these two might be more optimal ways to do it.

And then you go in and manually make those changes.

At that five year mark, again, we're hoping to get to more of that point where it's suggesting things and maybe it gives back options.

And then the designer can go through select some of those, make the changes they see fit, and get to more of an AI generative workflow.

So we're already working on the industrial estate plan.

So you can put in Frankfurt like you see that scene and say there are no pressure building for you.

So that's it's not that far.

Correct.

It's very, very it's like you know.

Very very effective.

Thank you guys.

Thank you.

Yeah.

Thank you so much for the questions.

And try to have some type of a utility because yes.

And doing the video recording with that, that so we have one more question.

So Marcia and then escape.

Hi.

I was wondering about, like, implementing the AI in your office.

What is the reaction to the employees like?

Has it been easy for them to learn or utilize?

Is there feedback within the employees if it's working well or if there's like frustrations with it?

You know, I feel.

Okay.

So I think right now, today, that is actually the biggest, kind of hurdle that we're trying to get through.

So it's, it's it's having the DCS team really developed something that's easy and basic enough to use one when we're in a meeting.

So it's quick.

We can just quickly think on our feet.

And two, it's easy enough to take the knowledge that we have and put it in there and have the time cost savings.

So that's really the runway that we're we're trying to, target and bridge that gap in between there because right now it is very difficult unless she's sitting right next to me, it's far less likely that I'm going to be using what she has to offer.

I would probably bring that one.

It's tough because it depends on your role within the company and what you're working on.

So if you're a technical job, captain that's in the software every day, you're going to be far more likely to adopt whatever is being rolled out to you versus a dinosaur that you know is not in this every day.

It's going to be harder for me to learn that language and really implement that on my day to day.

So a lot of this is part of a roll out strategy, especially with here.

A lot of the ground breaking work also has been in advanced manufacturing.

I mean, a lot of firms have their bread and butter and know and have a reputation, but they're known for, we have a very strong bench in advanced manufacturing.

And right now we have a site planning tool that is heavily adopted.

I work right next to the team.

It's been working with it for a few years and manages it along kind of like it or kind of like the roles of a band manager.

We see the roll out for it to roll out workplace and health care, having the same kind of trajectory.

We're also setting up internal metrics of success as well, with the various surveys.

So that's some progress.

I think what I found interesting, just from where I've sat is in the Chicago office is, the designers of like it, because they get to have a little more work life balance, and focus a little more on decisions and capabilities that are other than, addressing some sheet of paper or looking at legalities and so forth, so they can actually focus on their job, and have a nice lunch for the day, for example.

So it's kind of, I dare say, and kind of a little controversially, it's really kind of helping humanize the profession.

I see, which is a profession which is largely, you know, been built on all nighters and studios and so forth.

So the more we can value our own profession and our activities, and the more we actually empower, actually, whether this profession will exist in ten, 20 years.

That's a big question as well.

And I think some of it too, is the visual.

We can run visual images all day long.

Anybody can run those.

But to really know how to build them and implement them properly with the proper codes, the proper, materials, proper lighting, egress, that's where this is starting to take the data and put that into the visual conceptual that we can run quickly.

So that'll be nice.

When those two complement each other a little more.

Well thank you so much.

So we have more questions for the team.

And of course the team is going to stay here for a few minutes.

So, we can, continue our discussions.

Well, thank you again.

Where, Malcolm, for joining us and introducing this tool to us.

It is absolutely fascinating.

And it's such an innovative work.

And we'll look forward to see what you can actually achieve as you build on this tool.

So thank you so much again for joining us.