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.

A few weeks ago I joined Katie Cash from Smartegies on the AEC Marketing for Principals podcast to talk about how knowledge management is evolving in the AI era. The episode is called “Knowledge is Power: How AEC Firms Are Leveraging Data to Win Smarter.”

The upside of AI for AEC marketing is enormous: faster research, better positioning, richer storytelling, and time saved on repetitive work. But those gains depend on high-quality firmwide knowledge. Marketers are discovering that AI doesn’t magically retrieve the right information from existing systems and network drives — it only accelerates what has been made accurate, structured, and accessible.

This is why marketing teams are increasingly becoming the builders and stewards of their firms’ knowledge foundation.

As I re-listened to the episode, five observations stood out to me about where I think marketing, knowledge management, and AI go from here.

The design technology landscape has fundamentally changed. What was once a manageable stack of tools has become a sprawling ecosystem—and the questions coming with it are no longer just about what the tools can do, but how they work right now, in this specific project context, under these particular constraints. Meanwhile, technical knowledge itself has a shorter shelf life than ever before.

This creates an impossible burden for centralized DT teams. No matter how talented or dedicated, they eventually hit the same breaking point: there's simply too much surface area to cover. Knowledge can't travel fast enough through a hub-and-spoke model when every market sector, project type, and team operates differently.

In this issue of Smarter by Design, we explore how Lionakis has approached this problem by making a fundamentally different organizational design choice. Rather than growing their central team, they distributed expertise into project teams—creating a network of 30 embedded change agents who bring design technology support directly to where the work is happening.

What makes their Design Technology Support Specialist (DTSS) program so compelling isn't just that it works—it's how it works. It scales support without scaling headcount. It builds resilience into the organization. And it transforms how knowledge moves through the firm—from hub-and-spoke to peer-to-peer, from reactive to proactive, from bottleneck to network.

But this distributed organizational design also does something else: it creates the foundation for AI-powered learning tools to amplify the network even further. By externalizing expertise from the core DT team's heads into structured digital learning content, Lionakis is making that knowledge accessible not just to humans, but to AI search and knowledge agents that can surface the right answer at the right moment. The human network and the digital infrastructure work together—each making the other more powerful.

This is a story about what happens when you stop trying to be everywhere at once and instead design conditions under which expertise can live close to the work. It's about the shift from instructor to architect, from delivering answers to building systems that help people find them. And it is a glimpse into the future of what the AEC learning organizations of the future will look like.

For as long as I’ve been thinking about learning and development in AEC, the guiding ambition has been clear — deliver the right knowledge to the right person at the right time. It sounds simple, but anyone who’s managed L&D in a design firm knows how complicated that really is.

When people have timely access to the right resources, they are better prepared for the projects in front of them, more confident in their roles, and more likely to grow into future leaders. But timing matters. If knowledge arrives too early—before there’s a chance to apply it on a real project—it often doesn’t stick. If it comes too late, you can miss a golden learning opportunity. 

That’s why the topic of automation drew so much energy in our recent conversations with private beta cohorts about Synthesis LMS. Could technology finally solve the “right person, right time” problem? What if assignments, reminders, and learning paths could run on autopilot?

It’s an exciting vision. But as we talked it through, a more nuanced picture emerged. Automation will help—sometimes enormously. However, you can’t automate all delivery of learning content. Just as we’ve learned with AI, automation without a human in the loop can quickly go off in wild directions. The real challenge isn’t simply automating more, but deciding when and how to keep humans close to the process—ensuring assignments come with judgment, empathy, and context.

We’re about ten weeks into the private beta for Synthesis LMS and the biggest lessons we’ve learned so far aren’t really about technology at all.

At first, there was a flurry of activity in the LMS. Firms jumped in, created content, uploaded videos, and built out their first courses. It was exciting to watch. But what happened next was just as important. Across both beta cohorts, people paused and said, “Oh, I see — if we really want to take advantage of this new technology, if we really want to maximize the opportunity here, it’s going to take more than just building content. We’re going to need to level up as learning organizations.”

And by that I don’t mean throwing away what firms have already been doing. Quite the opposite. These are some of the smartest firms in our community, and they’ve been investing in knowledge sharing and informal learning for years. They have lunch & learns, peer-to-peer mentoring, recorded talks from subject-matter experts, even entire libraries with hundreds of videos. 

Those are strengths, and they’ve served firms well. But what we’re realizing together is this moment calls for something more. It’s not just about uploading content into a new tool. It’s about taking the next step, rising to the next level, and moving toward becoming modern AEC learning organizations.

That means going beyond volunteer-driven knowledge sharing into something more curated, something built around adult learning principles and intentional knowledge transfer. Because while serendipitous learning is valuable, firms are starting to recognize that it won’t be enough — not on its own — to prepare people for the challenges in front of them.

Lately, I’ve been thinking a lot about the relationship between AI and firmwide knowledge—not just how AEC firms use knowledge to power AI, but how AI is pushing them to care for their knowledge more intentionally.

When employees use AI-powered tools like Synthesis AI Search to ask questions, they’re finding useful answers from knowledge distributed across content silos and content types, including video. But these benefits are also revealing something else: the quality of those answers depends entirely on the quality of the content behind them.

Outdated content leads to outdated answers. Gaps in documentation mean missed opportunities. Conflicting versions create confusion.

As a response, AEC firms are becoming much more attuned to the importance of maintaining high-quality, up-to-date knowledge across the business. They’re not just investing in AI. They’re investing in the knowledge powering the AI—acquiring it, updating it, and retiring it when it no longer serves the firm.

This raises an old but important question:

How do you keep a large intranet from decaying over time?

Instead of treating a new intranet launch as a one-time project, it may be more helpful to think of it like painting the Golden Gate Bridge. Crews begin at one end, make their way across, and by the time they reach the other side, it’s time to start again. The goal isn’t to finish, but to stay in motion—a continuous cycle of proactive care. And the earlier you can establish that rhythm, the healthier your knowledge base will remain.

That’s why I want to revisit a story from the KA community that feels even more relevant today than when it was first shared at KA Connect back in 2019. It’s the story of how AKF Group, at the time a 500-person multidisciplinary engineering firm with nine offices in the U.S. and two in Mexico, built a simple but powerful process for keeping their knowledge fresh—and how that process became the foundation for what we now call Essential Content Reviews.

You can read more about the Essential Content Review Framework on our website, under Intranet Best Practices. But what I want to share here is the origin story—how one firm, led by a thoughtful and practical knowledge manager named Shannon Kaplan, created a rhythm of content care that’s still shaping how our community maintains knowledge today.

Every day, on every project, lessons are happening inside AEC firms. But without the right infrastructure, those insights often remain tucked away in meeting notes or siloed with project teams—making it likely that future teams will repeat the same mistakes on their projects.

It doesn’t have to be this way. With the right infrastructure in place, AEC firms can learn faster than one project at a time and turn individual insights into organizational knowledge.

At Affiliated Engineers, Inc. (AEI), Company Wide Teams (CWTs) provide that infrastructure. For more than 25 years, these peer-led, cross-office teams have served as the scaffolding for firmwide learning, continuous improvement, and leadership development. The idea is simple but powerful: Don’t just share lessons, learn from them—and not just as a project team, as an organization.

The structure is clear and repeatable. Project lessons are identified and prioritized during monthly CWT meetings. From there, lessons are shared across the firm through a variety of lightweight formats: curated presentations, short-form write-ups, and open technical forums. When a lesson points to a deeper need, it’s operationalized—transformed into templates, toolkits, or shared guidelines—improving how the whole firm works.

Of course, none of that happens without trust. AEI has also invested deeply in building a culture where people feel safe naming their mistakes, confident their insights will help others grow.

What follows is a look at one of the most durable and dynamic knowledge-sharing programs in the AEC industry—what it looks like in action, and what other firms can learn from it.

Over the past year, much of the conversation around AI and knowledge management in AEC has rightly focused on technology. Generative AI platforms, AI search offering, and emerging capabilities like AI agents are transforming how firms surface and share what they know. 

We’ve also spent a lot of time exploring the benefits: how AI can support emerging professionals, elevate subject matter experts, improve onboarding, streamline marketing, and accelerate learning and development, to name a few.

But one deceptively simple question from the Q&A session at the end of our KM 3.0: Connecting People to Knowledge and Expertise in the Flow of Work webinar has stayed with me:

“How is the work of AEC knowledge management teams changing because of AI?”

In many ways, that question became the organizing impulse for this issue—because KM 3.0, this new era of AI-powered knowledge management, isn’t just about technology. KM 3.0 is also about people, process, and culture, and how AI is reshaping the work of knowledge management teams.

Over the past few months, I’ve spoken with some of the most thoughtful and forward-thinking KM leaders in our community—members of Knowledge Architecture’s Research Council and others leading mature programs across firms of all sizes. 

And to be clear, not everyone doing this work has “knowledge” in their title. Across the AEC industry, we see knowledge management being led by a diverse mix of roles—knowledge managers, design technology leaders, operations directors, IT leaders, marketing and communications professionals, learning and development leads, innovation strategists, practice leaders, quality assurance directors, and even CEOs.

Regardless of their title or department, what they share is a deep investment in how knowledge flows across the firm and a commitment to making it more usable, accessible, and strategic.

What follows is a synthesis of 12 key trends—emergent patterns in how the smartest KM teams are evolving their work to thrive in this new era of AI-powered knowledge management.

The idea behind Shive-Hattery’s Plus One program is disarmingly simple: when you’re heading into a client meeting, proposal interview, or project kickoff, don’t go alone. Bring someone with you. Someone earlier in their career. Someone who wouldn’t normally be in the room. Have them observe. Encourage them to ask questions. Invite them to lead a small part of the meeting.

That’s it.

But inside that simple idea lives something incredibly powerful.

Shive-Hattery is a 600-person architecture and engineering consulting firm based in Cedar Rapids, Iowa, with roots dating back to 1895.

Plus One is both a knowledge strategy and an employee engagement strategy. It helps the firm transfer wisdom from senior staff, build client continuity, and develop future leaders in real time. At the same time, it helps early-career professionals feel seen, valued, and included in the real work of the firm, years before they might otherwise have had the chance.

It’s one of those rare practices that builds both business resilience and human connection.

In fact, I’d argue that’s part of why it works so well. It sits at the intersection of what firms need and what people want. Firms need succession planning, leadership development, and client coverage. People want mentorship, growth, and visibility. Plus One delivers both, and it’s exactly the kind of habit that helps a firm become a learning organization.

And because it’s lightweight, repeatable, and aligned with the natural rhythms of project work, it scales easily. 

Over the past month, I’ve been researching how the role of the AEC knowledge manager is evolving in the age of AI. One theme has come up again and again in surveys and interviews: AI literacy is quickly becoming a core responsibility of KM teams. In many firms, knowledge managers are stepping into a new role as AI educators, guides, and shepherds.

They’re the ones helping colleagues understand the art of the possible. Teaching prompting techniques. Introducing good habits like verifying sources. Navigating the messy intersections of internal data, external tools, and firm-specific workflows.

But a question keeps tugging at me:

Do we need to elevate everyone to the same level of AI fluency? Or will the future of AI learning look more jagged, role-based, and use case specific?

If you didn’t have the good fortune of meeting Ed Friedrichs before his passing in 2021, here are a few high-level facts: Ed was a visionary architect and firm leader. He played a pivotal role in shaping Gensler into the global design practice it is today. He joined the firm in 1969, founded the Los Angeles office in 1976, and served as Gensler’s President and CEO from 1995 to 2003. Under his leadership, the firm grew significantly in scale, sophistication, and ambition—while also codifying many of the cultural principles that still define it.

But for me, Ed’s legacy began long before I launched Knowledge Architecture—before I even knew what knowledge management was.

I first met Ed in 2005 at a conference called Firm of the Future, hosted by ZweigWhite. The conference brought together forward-thinking leaders to explore where the AEC industry was headed in the next 10, 15, 20 years. It was a fantastic premise, and it launched me on the path I’ve been walking ever since.

That event introduced me to firms that were being intentional about how they managed knowledge. These firms were investing in research, building systems for innovation, sharing what they learned internally, and connecting people across offices and disciplines. No one at the conference was using the term “knowledge management”—at least not that I can recall—but that’s exactly what they were doing. They were trying to make better use of what they knew.

Ed was one of the speakers at that conference. I attended his talk, then stayed afterward to ask questions. I later found him at a meal and sat next to him, eager to learn more. He was generous with his time—patient, encouraging, and deeply thoughtful.

What I didn’t fully realize until recently—when I went back and reviewed the old conference program—is that Ed didn’t just speak at the event. He chaired and designed it. He was the one who brought those people together. He shaped the experience that set me on this path.

Looking back, it all makes sense. The connecting thread running through the entire conference—the idea that architects and engineers shouldn’t only design buildings and infrastructure, but should also design their firms to be smarter—was Ed’s idea. His point of view gave the event its energy, its distinctiveness, and its sense of possibility.

That conference was the moment I first began to see knowledge management as a tool you could use to design a better business.

When Tricia Esser, KTGY’s CEO, initially brought up the idea of building a leadership development program from scratch, John Robison, COO, was skeptical.

John was already deep in the research process for leadership development programs. He had lined up a shortlist of external options: Stanford executive education, Korn Ferry programs, Vistage coaching networks. These were proven, respected, turnkey solutions. Why reinvent the wheel?

But when Tricia had a memorable conversation with a client who had built their own leadership development program in-house, everything changed.

She asked the client, "Isn’t building your own program a huge time commitment?"

The client answered: "Yes. Can you tell me something more important you're working on?"

That question landed hard.

Tricia and John realized they couldn’t entrust something this important to a third party.

That was the moment ELEVATE began.