Best of LinkedIn: Digital Construction CW 40/ 41

00:00:00: This episode is provided by Thomas Algeyer and Frennis, based on the most relevant LinkedIn posts about digital construction in calendar weeks, forty and forty one.

00:00:09: Frennis is a B to B market research company that supports enterprises across the construction industry with the market, customer and competitive insights they need to navigate dynamic markets and drive customer centric product development.

00:00:24: Welcome back to the deep dive.

00:00:26: Today, we're cutting through the noise to deliver the sharpest, most relevant insights from the digital construction world.

00:00:33: We're looking at the last two weeks specifically for you professionals in construction and manufacturing.

00:00:38: And what really jumped out from the sources this time is, well, it feels like the industry is hitting a real inflection point.

00:00:43: An inflection point?

00:00:44: How so?

00:00:45: Well, the big theme, the one that connects everything, is this major shift away from just trying things out, the POCs, the experiments.

00:00:52: Oh, OK.

00:00:52: So less experimentation.

00:00:54: Exactly.

00:00:54: We're seeing a clear acceleration toward field-ready enterprise scale execution.

00:00:59: The conversation has really moved on.

00:01:01: It's now strictly about tangible, measurable ROI.

00:01:05: Right.

00:01:05: Makes sense.

00:01:06: It's not.

00:01:06: if the tech works anymore is how fast it pays for itself on the job site.

00:01:10: Yeah.

00:01:10: So we've boiled these findings down into basically four key areas for

00:01:14: you.

00:01:14: Yep.

00:01:15: We're looking at how AI is actually being used day to day, that laser focus on cost and margin control, the strategic scaling of BIM and digital twins, not just playing around with them.

00:01:26: And crucially, the role of culture and leadership.

00:01:30: You can't forget the people side.

00:01:31: Absolutely not.

00:01:32: that people part seems more critical than ever.

00:01:35: Okay, let's dive into that first cluster then AI moving from well the hype to actual ROI and enabling folks in the field.

00:01:43: Yeah, it's definitely embedding itself deeper into the daily workflows.

00:01:46: We're seeing really concrete use cases now for everyone from the super onsite to the execs in the C-suite.

00:01:52: And there's some pretty impressive metrics emerging, right?

00:01:54: Oh, definitely.

00:01:55: Osama Aljanabi shared some numbers that honestly should make every project manager pause.

00:02:00: Think about this.

00:02:01: Superintendents completing these huge, eight hundred and forty seven point commissioning walkthroughs.

00:02:06: Okay, yeah, those are massive.

00:02:07: In

00:02:07: just twelve minutes.

00:02:08: minutes seriously wait.

00:02:10: they used to take.

00:02:11: what days?

00:02:12: multiple people

00:02:12: exactly days clipboards endless emails follow-ups.

00:02:16: now it's done in like less time than a coffee break.

00:02:20: It's kind of revolutionary when you think about it that way.

00:02:22: That's incredible time savings.

00:02:23: And it's not just time.

00:02:24: AI is being linked to a forty percent streamlining of design iterations, so better quality, faster, plus a measurable fifteen percent reduction in overall project

00:02:35: costs.

00:02:35: Okay, fifteen percent cost reduction is significant.

00:02:38: And maybe most importantly, predictive analytics from AI are being credited with a sixty percent drop in safety incidents.

00:02:43: Wow.

00:02:44: Sixty percent.

00:02:45: That's huge.

00:02:46: That's lives and massive costs.

00:02:48: Saved right there.

00:02:48: That's

00:02:49: the real impact human and financial.

00:02:51: Yeah,

00:02:52: and you can imagine the relief for field managers.

00:02:54: Elliott Christiansen pointed this out.

00:02:55: This tech helps them get back time that just gets you know sucked up by paperwork chasing drawings

00:03:01: admin burden

00:03:02: exactly so they can actually focus on safety Quality and leading their teams on site.

00:03:07: It's AI enabling better human management.

00:03:09: And on the finance side, the margin defense aspect is becoming really powerful.

00:03:14: Lea Sy discussed an AI margin defense system built specifically for construction finance.

00:03:19: Okay, margin defense.

00:03:21: How does that work?

00:03:22: Apparently,

00:03:23: it can identify somewhere between three to seven percent in margin leaks every year.

00:03:27: And we're not talking about finding typos.

00:03:28: No.

00:03:29: No.

00:03:30: They gave specific examples like finding two point eight million dollars in subcontractor double billing.

00:03:34: Ouch.

00:03:34: Two point eight

00:03:36: million dollars.

00:03:36: and another four point three million dollars spotted in steel escalation clauses Buried deep in the contracts stuff.

00:03:42: That's almost impossible for humans to catch consistently.

00:03:45: So the AI is like a forensic accountant for contracts, working constantly.

00:03:51: That's a level of diligence we just couldn't achieve before.

00:03:54: And this isn't just theory, right?

00:03:55: People are actually using this at scale.

00:03:57: Oh, absolutely.

00:03:58: Sarah Buckner shared that Gilbing Building Company, a huge player, obviously is rolling out three different trunk tools, AI agents across over two hundred job sites.

00:04:06: Two hundred plus sites.

00:04:07: Okay, that's definitely not an experiment.

00:04:09: Right.

00:04:10: And the result, they're seeing nearly a fifty percent cut in their submittal review cycle times.

00:04:14: It's real world industrial grade impact.

00:04:17: But I guess the catch is you need good data for any of this to work.

00:04:21: That's the crucial point.

00:04:23: Noel Joseph really hammered this home.

00:04:25: For these AEC products to actually deliver, the industry has to focus on structuring its live data drawings, RFIs, costs.

00:04:33: So the AI can actually understand it and reason over

00:04:36: it.

00:04:36: Precisely.

00:04:37: His point was strategic.

00:04:39: You need to own the context, which is your structured data.

00:04:42: Then you gain the flexibility to, as he put it, swap the brain.

00:04:46: Use whatever.

00:04:47: the latest greatest AI model is because you control the underlying information.

00:04:51: Okay, owning the data structuring to write, that makes perfect sense.

00:04:54: And it leads us right into our second theme, cost and margin control.

00:04:57: Because like you said, AI analyzing bad data just gets you the wrong answer faster.

00:05:01: Exactly.

00:05:02: And Ibrahim Hegazi really underlined how critical precision is here.

00:05:06: In construction, he argues, accuracy is the competitive advantage.

00:05:09: Just one bad measurement can spiral into millions lost.

00:05:12: Delays, rework, hitting your reputation, yeah.

00:05:15: Cascades quickly.

00:05:16: And so the question becomes, is fixing this just about technology?

00:05:21: Erdem Evern argues, pretty convincingly, I think, that real cost control is actually proactive leadership.

00:05:27: it's needed at every single project level.

00:05:30: So it's not just buying software, it's a discipline, a cultural thing.

00:05:33: Totally.

00:05:34: It's about having protective measures in place, like using realistic baselines to start with, tracking your costs daily, not waiting until a month then when it's too late, daily tracking, and being absolutely ruthless about managing scope creep before it explodes.

00:05:48: It's leadership discipline first.

00:05:49: And you see that discipline.

00:05:51: in the firms that are successfully using tech for cost control.

00:05:54: And these are emo highlighted.

00:05:55: This predictable inputs give you predictable outcomes.

00:05:58: Tighter cost control, better use of working capital.

00:06:01: It comes from leveraging tech effectively, but only after that discipline is there.

00:06:05: But the big technical hurdle remains getting access to all that cost data in one place, right?

00:06:10: John Davies had this great analogy.

00:06:12: He compared the industry's data to the Mara's salt mines in Peru.

00:06:16: The salt mines.

00:06:18: How does that work?

00:06:18: You know, where the saltwater evaporates in thousands of small separate pools, he's saying our project data is like that fragmented, stuck in little isolated pools across different systems and companies.

00:06:29: Oh, okay.

00:06:29: I see the picture.

00:06:31: Data silos, basically.

00:06:32: Exactly.

00:06:33: But what's got people like him excited is new tech that can actually analyze data while it stays in those separate pools.

00:06:40: Decentralized analysis.

00:06:42: Analyzing it without forcing everyone to dump it into one massive database.

00:06:46: Right.

00:06:47: And that could finally unlock what quantity surveyors have wanted forever.

00:06:52: the holy grail of daily real-time costing across the whole project ecosystem.

00:06:58: Okay, daily costing is huge, but decentralized analysis.

00:07:02: Doesn't that sound incredibly complex?

00:07:04: And what about security?

00:07:05: Data ownership?

00:07:06: Are the sources talking about those challenges?

00:07:07: They definitely acknowledge the complexity.

00:07:09: It's not simple.

00:07:10: But I think the potential payoff getting that daily cost visibility is so massive that the excitement is kind of outweighing the fear right now.

00:07:18: Because if you see a cost issue today instead of a month from now,

00:07:22: you can actually do something about it.

00:07:23: You can intervene.

00:07:25: protect your margin, it shifts cost control from being reactive, looking backwards to being genuinely preventive.

00:07:31: Got it.

00:07:33: Okay, let's pivot then to our third area, BIM and digital twins.

00:07:39: Moving towards standards and again, that strategic scaling idea.

00:07:43: Yeah, and Nikolay Jovich had this brilliant sort of inverse guide on how to fail with VIM.

00:07:49: How to fail?

00:07:49: Okay, I'm listening.

00:07:50: Basically, complain a lot, model without any plan, always blame the software and just assume you're doing it right without checking.

00:07:58: Right,

00:07:58: sounds familiar maybe.

00:07:59: His

00:07:59: point was you need strategy before screenshots and crucially people before platforms.

00:08:04: stop just modeling stuff aimlessly.

00:08:06: Have a plan.

00:08:07: Strategy before screenshots, I like that.

00:08:09: Yeah.

00:08:09: And that strategic thinking is even more vital when we talk digital twins, isn't it?

00:08:13: Absolutely.

00:08:14: Toby Mills made a really sharp distinction.

00:08:15: He pointed out that most of what people are calling digital twins right now are really just BIM models with a GIS layer slapped on top.

00:08:22: So kind of a static, three-D map, maybe with some links?

00:08:25: Pretty much.

00:08:25: Well, it looks nice, but it's not a true twin.

00:08:28: He argues we need those functional, true digital twins if we're serious about building an AI-driven built environment.

00:08:35: They're the foundation.

00:08:36: OK, so if most are just fancy BIM-agees overlays, how would someone listening know if they've got a... Well, a fake twin.

00:08:43: What's the key functional difference a true twin offers?

00:08:46: The biggest difference is dynamic data flow and interaction.

00:08:49: A true twin is designed to ingest real-time data sensor readings, operational performance, energy use, wear and tear.

00:08:56: Okay,

00:08:56: live data coming in.

00:08:57: Yes.

00:08:58: And then it acts as a simulation and prediction tool.

00:09:01: based on that live data.

00:09:03: The fake twin is basically a pretty picture, a rendering.

00:09:06: The true twin is a live working decision engine.

00:09:09: A decision engine, okay.

00:09:10: But building that sounds, well, overwhelming.

00:09:13: How do you even start without getting lost in the complexity?

00:09:16: That's where Florian Humor's advice comes in.

00:09:18: He cautioned against trying to boil the ocean, don't try building a city.

00:09:21: scale twin right out of the gate.

00:09:23: That often just leads to budget black holes and endless analysis.

00:09:27: An

00:09:27: analysis paralysis, yeah.

00:09:28: His secret.

00:09:29: Start small.

00:09:30: Pick one high impact problem your twin can solve brilliantly.

00:09:34: Deliver fast ROI on that one thing, get the win, and then expand methodically from there.

00:09:39: Make sense.

00:09:40: Prove the value quickly, then scale.

00:09:42: But that requires standards, governance.

00:09:45: Exactly.

00:09:45: And Tony Hardy highlighted that the RICS, the Royal Institution of Chartered Surveyors, is bringing in a new global professional standard specifically on AI.

00:09:55: It takes effect in March, twenty-twenty-six.

00:09:57: ICS is getting involved.

00:09:58: Okay, that's It signals seriousness.

00:09:59: It really does.

00:10:00: It signals a shift towards accountability.

00:10:02: Professionals will be required to actively manage AI risks and transparently document any AI-generated outputs.

00:10:09: It's the industry putting proper guardrails in place.

00:10:11: And tying this back to the data structure needed for AI in these dynamic twins.

00:10:15: Right.

00:10:15: Christajan Velibich showed how things like Autodesk's AECData model, the AECDM, are becoming key.

00:10:21: By structuring project data properly, it lets users access, say, Revit elements outside the main Revit model.

00:10:28: Why is that important?

00:10:29: Because you can then add custom properties like real-time cost data or carbon metrics directly to that structured data without making the original design file massive and unwieldy.

00:10:40: It enables that crucial interoperability needed for smart workflows.

00:10:44: Got it.

00:10:44: Structured data is the key enabler for everything downstream from AI analysis to real-time digital twins.

00:10:52: Okay, that brings us to our final theme which you mentioned earlier the human infrastructure.

00:10:56: Culture, leadership, talent.

00:10:59: Yeah, and Jennifer Todd really just said the quiet part out loud.

00:11:02: AI is great, but it only delivers value if your people actually stick around to use it and benefit from it.

00:11:08: So, retention is key.

00:11:09: Absolutely.

00:11:10: She emphasized that culture is the critical infrastructure.

00:11:13: It has to be strong enough to actually sustain all this tech adoption.

00:11:17: If your foundation is leaky, putting fancy tech on top won't fix it.

00:11:21: Right.

00:11:21: Throwing tech at a bad culture just makes things worse, usually.

00:11:24: And that really demands a leadership reset, doesn't it?

00:11:27: Especially in construction, with its often deeply entrenched traditional ways.

00:11:31: AJ Waters called for moving away from what he called the rusty hammer approach built on intimidation.

00:11:37: The rusty hammer.

00:11:38: Yeah, that paints a picture.

00:11:39: I think many in the industry would recognize that.

00:11:41: He highlighted five pillars of leadership, drawing from the book Beyond the Hammer that are all focused on fixing toxic culture and actually keeping your

00:11:49: talent.

00:11:50: Moving away from that intimidation model sounds essential, but also really difficult, especially when projects are under pressure and margins are tight.

00:11:59: What's like the single most important first step leadership needs to take?

00:12:03: According to Waters in these sources, it seems to start with vulnerability and just clear communication.

00:12:08: Leadership has to actually acknowledge that the old ways are actively contributing to the labor shortages and the high turnover rates we see.

00:12:16: Admit there's a problem first.

00:12:18: Okay, acknowledgement and communication.

00:12:20: And retaining that talent is critical because as we know, decades of experience are literally walking out the door with the aging workforce.

00:12:28: It's a huge issue.

00:12:29: Vragan Paramanantham noted that AI could actually play a vital role here in knowledge retention.

00:12:34: How so?

00:12:35: Capturing knowledge.

00:12:36: Potentially yes.

00:12:38: By creating a sort of institutional knowledge graph.

00:12:41: Imagine AI being able to surface crucial insights buried in decades of old site reports, RFIs, variation orders.

00:12:49: So capturing the tribal knowledge from those senior folks before they retire.

00:12:52: Exactly.

00:12:53: Making that experience accessible even after they've left.

00:12:56: That could be incredibly valuable.

00:12:58: Yeah, no kidding.

00:12:59: But to even get to that future state, we probably need to fix how we run projects now,

00:13:03: right?

00:13:03: Definitely.

00:13:05: Felipe engineer Manriquez strongly advocated for breaking up with some of the outdated rigid project management styles.

00:13:12: He recommends embracing things like lean construction and agile frameworks like scrum to get better collaboration and flow.

00:13:18: Lean agile bringing those principles more fully into construction project management

00:13:23: And Adam Hooth's kind of brought it full circle, reinforcing that any successful lean transformation has to start with one core principle, which is respect for people.

00:13:32: Respect for people.

00:13:33: Okay.

00:13:34: So again, it comes back to culture.

00:13:36: It's not just a fluffy side note.

00:13:38: It's the mandatory starting point for digital transformation to actually succeed.

00:13:42: It seems to be the absolute bedrock.

00:13:44: Okay.

00:13:45: So wrapping this all up.

00:13:47: What's the main takeaway for you, the professional listening to this, trying to lead digital strategy?

00:13:52: It really feels like the industry is shifting gears, operationalizing AI and... digital tools.

00:13:57: Yeah, it's about accelerating schedules, definitely defending those razor thin margins, managing risk much more proactively.

00:14:05: But the bottleneck isn't really the technology itself anymore.

00:14:08: What is it then?

00:14:09: The biggest ROI potential now seems to lie in two areas.

00:14:13: First, investing seriously in standardizing your data foundation, get your data house in order.

00:14:19: Right.

00:14:19: And second, maybe even more critically, investing in fixing your culture.

00:14:23: The hard at onsite might be ready for AI.

00:14:26: that the organization as a whole needs to be ready first.

00:14:29: People

00:14:29: first, then platforms.

00:14:31: People

00:14:31: first, then platforms.

00:14:33: A strong concluding thought.

00:14:35: If you enjoyed this deep dive, new episodes drop every two weeks.

00:14:38: Also be sure to check out our other editions on smart manufacturing and digital power tools.

00:14:43: Thank you for joining us and don't forget to subscribe.