I found myself in a fascinating conversation with the COO of one of our clients last week. We were talking about something I’ve been circling around for months, but this discussion finally snapped the pieces into place.
It’s what I’m starting to call the AI and Expertise Paradox.
We all know the demographic story by now. Baby boomers are retiring in large numbers and there aren’t enough Gen Xers to replace them.
In AEC, that often means we’re losing some of the deepest technical knowledge in our organizations—codes, construction standards, quality practices, the kind of judgment that only comes from decades of watching real projects go from concept to completion.
Technical experts possess the kind of deep smarts that can look at a drawing and feel that something isn’t quite right.
And they are retiring.
At the same time, we’re seeing a wave of AI-powered tools arrive that promise to help fill the gap. Automated code checks. QA/QC scanners. Plan reviewers that highlight potential issues a junior architect or engineer would never recognize. Assistants that allow someone to work across jurisdictions with different codes and standards and at least have a baseline level of support.
In some ways, it feels like knowledge augmentation—almost like the moment in The Matrix when the character Tank uploads the knowledge to fly a helicopter into Trinity’s brain.
Similarly, there are numerous emerging AI tools in our industry which, if they deliver on their vision, will enable a junior team member to run a basic code review, an expert who is stretched thin across multiple projects to offload routine checks, or an architect or engineer working on a project in a different region to get a helpful second set of eyes on local code compliance.
On the surface, this looks like the perfect solution: AI tools that allow those with less expertise or who are super busy to do more.
But here’s where the paradox emerges.
AI Still Needs Experts (The First Problem)
Right now, AI tools are most effective when an expert is in the loop.
An experienced architect or engineer can look at an AI-generated report and instantly separate signal from noise:
“This is a real issue. Excellent catch. Might have missed this one.”
“This doesn’t apply in this jurisdiction.”
“This is technically true, but not material to the design.”
“This won’t matter in the new version of the code which applies to our building.”
“This is just wrong.”
They can also see what’s missing—the issues the AI tool didn’t include in the report.
Without the judgement acquired through years of practice, junior staff can easily fall into false confidence, unnecessary rework, or worse, risky decisions. AI tools help them do more, but they don't yet help them know whether what they are doing is correct.
In other words, AI is not currently a replacement for expertise.
It’s a force multiplier for expertise.
Technical Experts Are Retiring (The Second Problem)
At the exact moment when AI tools require expert oversight, those experts are retiring in large numbers.
Many firms are watching their deepest technical knowledge walk out the door.
This isn’t simply the loss of skills—it’s the loss of judgment, intuition, pattern recognition, and hard‑earned lessons about what actually happens in the field.
And the timing couldn’t be worse.
Fewer People Want to Become Technical Experts (The Third Problem)
From what we’re hearing across our client community, many emerging professionals simply aren’t as interested in developing deep technical expertise.
They’re drawn to the front end of the profession:
design
visualization
design technology
developing custom software
— not the intricate, technical work of codes, detailing, constructability, and quality assurance.
You can argue this has always been somewhat true, but the difference now is scale and timing.
The Apprenticeship Model Is Breaking (The Fourth Problem)
For generations, our industry relied on a fairly consistent apprenticeship model.
Emerging professionals sat near someone more experienced. They listened to their phone calls. They watched them mark up drawings. They absorbed judgment by proximity—not through a training module, but by seeing how decisions were made in real time.
The craft was transmitted osmotically and serendipitously.
That model is breaking.
Hybrid work and distributed teams make it harder for an emerging professional to “listen over the shoulder” of a technical expert. Project teams are spread across offices and time zones. Much day-to-day interaction happens through scheduled Zoom calls and Teams chats rather than shared physical space. And the informal learning that once happened between those moments is evaporating.
Layer onto that a generation raised on Google, YouTube, TikTok, and instant messaging. The next generations are highly self‑directed and expect immediate answers and on‑demand knowledge in the flow of work.
Meanwhile, the half‑life of technical knowledge keeps shrinking. Codes change. Materials evolve. Project delivery models shift. Digital tools proliferate. There’s simply too much to teach relying solely on the old apprenticeship model.
As one of our clients likes to say, “We can’t wait 30 years to get a 30‑year architect.”
We need to develop people faster.
Putting The Paradox Together
So now we have four problems converging:
AI still needs experts.
Those experts are retiring.
Fewer emerging professionals want to become technical experts.
The apprenticeship model that once created experts is breaking.
Put together, these four problems form the heart of the AI and Expertise Paradox.
It’s a paradox in the truest sense: the very thing that appears to solve the problem depends on the thing we’re losing.
So Where Do We Go From Here?
The COO I spoke with last week isn’t slowing down.
Their firm is moving ahead with AI adoption—not because they believe AI will replace expertise, but because they know it can help their experts work smarter today while helping connect emerging professionals to the right knowledge and expertise in the flow of work.
At the same time, they’re rethinking how learning and development happens inside the firm. They’ve already revamped their leadership development program. Now they’re turning their attention to project managers and emerging professionals, experimenting with new approaches and new technologies to help people grow faster.
They’re building a more intentional, modern learning environment.
And they’re not alone.
Across our community, we’re seeing more firms begin to rethink learning and development in deeper and more intentional ways.
Many are adopting a continuous onboarding mindset—recognizing that people don’t just onboard once when they join the firm. They onboard every time they:
take on new responsibilities, like project management or technical architecture
work on a new project type, such as shifting from retail to healthcare
enter a new project phase, like doing construction administration for the first time
Firms are looking for ways to continually equip their people with the knowledge they need at the moment they need it.
We’re also seeing firms:
create rewarding career paths for technical experts that provide leadership opportunities, meaningful influence, and strong compensation
invest in hybrid and flipped classroom models—using on‑demand training for the 101 basics, and reserving face‑to‑face time with experts for applied work and real examples
move beyond simply pointing people to hour‑long Lunch & Learn recordings to also offering short, modular, on-demand content that fits into the flow of work
apply adult learning principles to elevate the quality and effectiveness of their training
build feedback loops to continually improve learning efforts
automate the delivery of key knowledge so it reaches the right person before they need it, whenever possible
build integrated knowledge and learning teams, technologies, and processes
In other words, firms are starting to design modern learning environments—ones that help people grow faster, develop judgment, and apply what they’re learning in meaningful ways.
It’s early, but the work has begun.
What We’re Doing at Knowledge Architecture
Ari de Geus, in his book The Living Company, studied organizations that survived for generations. One of his most famous conclusions was:
“The only sustainable competitive advantage is learning faster than your competitors.”
That idea feels especially relevant right now. If anything, AI is raising the stakes. It is giving firms the opportunity to move faster, but only if their people can learn, adapt, and apply judgment at the same pace.
That’s the work we’re committing to.
From a technology perspective, we’re continuing to expand Synthesis into the integrated knowledge and learning platform that AEC firms need to support this transition—adding new capabilities like Synthesis LMS (Learning Management System) and Synthesis AI Search on top of the intranet foundation to provide the connective tissue that helps firms capture, share, transfer, and find what they know, as well as identify knowledge gaps and outdated knowledge, to help them evolve and grow their practices.
From a community perspective, we’re going to stay close to the question “What does a modern learning organization in AEC look like?” We’ll continue sharing what we’re hearing, what we’re seeing, and the stories of firms who are pushing the boundaries.
In fact, the theme of KA Connect 2026, our annual knowledge and learning management conference for the AEC industry, will be Designing Modern Learning Organizations.
We’ll keep exploring this theme both here in the Smarter by Design newsletter and in our new Smarter by Design podcast, launching in January 2026. And it will continue to shape the products we build and the conversations we convene.
I believe this is the work of the next decade in our industry. And if we’re successful as a community, we’ll have figured out how to build smarter, more adaptable AEC practices. By design.
Questions for You
If your AEC firm is investing in AI tools in technical domains—QA/QC, code review, automated checking, etc.—what are you seeing?
Is it true in your firm that emerging professionals are less interested in developing deep technical expertise?
And how are you thinking about approaching the problems and paradox I laid out in this issue?
I’d love to hear your thoughts.
👉 Email me at cparsons@knowledge-architecture.com.