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.
Trend 1: Slowing Down to Spend More Time on Strategy and Prioritization
The best knowledge management teams I know are slowing down. By design.
In an environment where new AI tools, platforms, and plugins seem to emerge daily, many firms are facing what you might call a paradox of opportunity. There’s no shortage of potential—new ways to streamline workflows, capture knowledge, improve onboarding, enhance reuse, and deliver value across the business. But the sheer volume of choices can overwhelm, distract, and increase the risk of chasing too many things at once.
It’s not just the technology that’s moving fast—the knowledge itself is changing, too. The half-life of internal expertise is shrinking. Legacy content needs revision. Systems need rethinking. And no firm can manage it all. That’s always been true in AEC knowledge management, but it’s even more true in the AI era.
The best KM teams are cutting through the noise by returning to first principles:
What are our firm’s core business goals and strategic priorities?
What knowledge matters most in service of those goals?
Which AI use cases meaningfully advance that strategy?
They’re becoming filters, not funnels—connecting new opportunities back to firm strategy, identifying high-leverage areas, and gently heading off “random acts of KM or AI” before they become distractions.
This kind of prioritization only happens with strong executive sponsorship. Whether it’s the CEO, COO, or a managing principal, the best firms ensure that someone at the leadership table is aligned with knowledge management—and empowered to say: work on this, not that. With that support, KM leaders don’t have to guess which projects or investments will matter most. They can shape long-term initiatives that actually move the business forward.
Yes, there’s a lot of opportunity right now. But there’s also risk—the risk of building what should have been bought, buying what should have been built, or investing time, money, and attention on things that don’t matter.
That’s why the smartest firms are taking a beat. They’re trading speed for strategic clarity—and setting their KM and AI efforts up to create real, sustained value.
Trend 2: Thinking Like Knowledge Architects
Another shift I’m seeing in leading KM teams is a deeper focus on designing knowledge systems—not just launching tools or programs, but thinking like architects.
Imagine the role of a campus architect. They’re not just placing buildings; they’re sequencing long-term investments, coordinating infrastructure, and making decisions today that will shape the flow of people, ideas, and resources for decades to come.
The best KM leaders are approaching their work in much the same way—but as knowledge architects. They’re designing the scaffolding, flows, and integrations that support smarter, more connected learning organizations. Not just managing content, but shaping the knowledge ecosystems their firms will rely on well into the future.
They’re asking foundational questions like:
Have we identified the right systems to serve as reliable single sources of truth for critical firmwide knowledge?
Are our systems integrated correctly to make knowledge available in the right applications for both human and agentic users?
Do we have the right people in place to design and maintain the knowledge ecosystem? Not just content owners, but stewards of structure and flow?
Are our human processes for capturing, updating, and sharing knowledge well-defined, monitored, and measured?
Can knowledge move across our organization in a way that’s timely, trustworthy, and tied to real business needs?
KM leaders are increasingly stepping into a knowledge architect role—working across a design canvas that spans:
Software platforms
Data pipelines
Human processes, like Design and Engineering Reviews, Communities of Practice, QA/QC, and Project Closeout
Cultural practices around stewardship and content ownership
The context and data needs of AI systems
They’re mapping how knowledge flows—or should flow—across the firm. They’re thinking not just about features, but dependencies. Not just implementation, but maintainability.
This long-view mindset is a necessary counterweight to the hype cycle of AI. The best KM leaders I know aren’t chasing tools—they’re building infrastructure. And that requires a different kind of vision: one that sees the future clearly, but understands how to get there step by step.
Trend 3: From Gatekeeping to Stewardship
In the early days of intranets and knowledge management systems, publishing content required technical skill—HTML knowledge, even light programming. Most firms relied on one or two people to act as gatekeepers: everything flowed through them. If you wanted something published, you sent it to the KM team. If it needed an update, you sent it back again.
That bottleneck was born of necessity—but it also created a certain culture: the KM team as gatekeeper. Not just of quality, but of access.
Today, that model no longer fits. Publishing tools are better. Content creation is easier. Platforms like Synthesis, and others, have dramatically lowered the barrier to sharing knowledge. The bottleneck is gone.
But in many firms, the expectations of gatekeeping still linger. Even as tools have lowered the barrier to contribution, the perception remains that the KM team is still the central publisher, the quality control checkpoint, the place where all content must pass through. It’s a legacy mindset—and one that many KM teams are now working to actively shed, because it’s no longer sustainable.
The best KM teams are moving away from managing all the knowledge themselves and toward creating the conditions for others to manage knowledge well. They’re not trying to control every document or library. Instead, they’re designing roads, lanes, and guardrails—establishing taxonomies, setting information architecture, configuring integrations, and building publishing workflows—so others can move through the system with clarity and confidence.
In this newer model, the KM team becomes:
A steward of the system, not the sole source of truth
A strategic facilitator, not a bottleneck
A coach and enabler, not a gatekeeper
They still set the standards. They still design the structure. But they’re empowering others—subject matter experts, department leaders, operations staff, committees—to contribute, maintain, and improve content within those guardrails.
This shift frees KM leaders to focus on more strategic work. But more importantly, it distributes responsibility for knowledge across the organization—which is the only way to sustain it at scale.
Trend 4: A Renewed Focus on Content Quality and Curation
One of the most immediate—and durable—shifts I’m seeing in AEC knowledge management is a renewed investment in content quality. Not just adding more knowledge, but improving what already exists.
AI has been the catalyst. Tools like Synthesis AI Search are surfacing knowledge that used to be siloed by department, locked in videos, buried in long documents, scattered across years of posts and comments, or isolated in employee and project databases. These tools are revealing what firms know—but they’re also exposing what’s outdated, redundant, or just plain wrong.
And that’s triggered a shift.
KM teams are doubling down on digital hygiene:
Proactively reviewing high-traffic content
Rotating through content areas on a cadence
Developing processes to capture and maintain key employee and project data
Using search analytics and feedback to identify top queries and outdated content
Nearly every KM leader I interviewed said the same thing: if they had to choose between filling knowledge gaps or cleaning up existing content, they’d start with cleanup. Because when something is missing, users assume it just hasn’t been added yet. But when something is wrong, they lose trust in the whole system.
This shift from collection to curation has made KM teams more operationally focused—and more collaborative. They’re designing frameworks that allow content owners to maintain their own material, while providing the structure, guidance, and tools to keep things consistent, current, and findable.
It’s a mindset shift: from knowledge as “something we store” to knowledge as a living asset. And now that AI tools are making that asset more visible—and more valuable—leading firms are treating it with the care it deserves.
Trend 5: The Way We Organize Information Is Changing
This question came up several times in our recent conversations with KM leaders:
“If AI can find anything, do we still need to organize everything?”
It’s not a wild question. AI search—especially semantic and vector-based search—has changed the game. In the past, if you didn’t enter the exact keyword or phrase, you might not find what you were looking for. Search engines were brittle, and the burden fell on KM teams to make content findable through tagging, naming conventions, synonyms, best bets, and structured taxonomies.
But in AEC, where KM teams are often lean and stretched across many responsibilities, the approach was always more pragmatic. Everyone knew structure mattered, but few had the time or resources to build elaborate ontologies or maintain perfect hierarchies. And now, with AI magically surfacing related content across silos, understanding context and synonyms, and summarizing results in plain language, it’s easy to wonder if we can stop worrying about organizing altogether.
Nobody I know is seriously advocating for that.
What we’re hearing instead is a more thoughtful shift—a move away from rigid, all-or-nothing taxonomies toward intentional scaffolding at the right level of detail. One KM leader put it this way:
“I still believe in strong Level 1, Level 2, Level 3 structure on our intranet—the major categories and subcategories that reflect how our firm works. But beyond that, I’m okay with being less organized at deeper levels of hierarchy. I know that AI can handle it.”
That framing—high-level scaffolding with deeper flexibility—feels like where many firms are landing.
The KM leaders I talked to believe you still need to signal what matters. You still need to make your values, workflows, and key knowledge areas legible to new employees. And you still need to support those in your firm who prefer to browse and navigate rather than search. But once you’ve done that, you can afford to loosen up.
It’s a question of resolution. Just like in BIM, where you choose the Level of Development (LOD) based on the purpose of the model, KM leaders are learning to tune their information architecture to the right level of development for clarity, usability, and sustainability. If the structure is too coarse, it becomes meaningless. If it’s too fine, it becomes unmanageable. But at the right resolution—that high-level scaffolding—it tells a story. It shows how your firm works. And it gives both humans and AI enough context to find their way.
AI search has changed how we think about structure, but it hasn’t made structure irrelevant. In fact, as one KM leader put it, “The way we structure our information says something about who we are, and what we value, to our employees.”
KM in the age of AI isn’t about abandoning organization. It’s about applying it more strategically and at the right level of detail.
Trend 6: Optimizing Content for Human and Agentic Retrieval
As AI becomes more deeply embedded in daily workflows, firms are realizing that the way content is written—and for whom—matters more than ever. What used to be internal shorthand or insider knowledge is now being surfaced, summarized, and remixed by AI agents.
As new hires and emerging professionals retrieve more information from AI, versus solely from experts, a challenge has emerged.
For years, many technical standards, guidelines, and process documents were written at an expert level. They were packed with acronyms, layered assumptions, and firm-specific shorthand that only seasoned employees understood. That might have worked when knowledge moved mostly through mentorship and meetings with experts on hand to provide the missing context. But now, as new hires and emerging professionals are increasingly turning to internal AI for technical or procedural advice, those missing layers of explanation and context become serious blockers to understanding—or worse, sources of misinformation.
So teams are changing how they write.
They’re adding context and backstory. They’re defining terms and acronyms. They’re organizing content with clearer titles and section headers. They’re asking subject matter experts to write as if they’re speaking to an intern.
And the beautiful thing is that when you make content more clear and accessible for AI, you also make it more clear and accessible for humans.
Nice.
This shift also includes a stronger focus on communicating the why, not just the what. Instead of simply stating that a window-to-wall ratio should be between 20–40%, teams are now adding explanations: Why does it matter? When would you want to push higher or lower? What trade-offs are involved? What’s the firm's preferred approach—and when might it make sense to deviate?
These kinds of insights help junior staff make better decisions. But they also help AI return better answers—ones that are grounded in firm context and aligned with real-world constraints.
In fact, some KM leaders are starting to describe AI agents as a new kind of learner—one whose knowledge, literacy, and developmental path must be managed just like any human team member. That doesn’t mean turning everything into rigid datasets or structured fields. It means treating content quality and clarity as essential infrastructure—the foundation on which human learning and agentic performance both depend.
When knowledge is written with both audiences in mind, it becomes more usable, more trustworthy, and more adaptable. And that’s not just good for AI. It’s good for humans, too.
Trend 7: KM Leaders as AI Scouts and Shepherds
As AI sweeps across the AEC industry, KM leaders are stepping up to help their firms navigate the shift. In many cases, they’re not just supporting AI initiatives—they’re leading them.
Some are acting as AI Scouts—testing new tools, exploring emerging capabilities, and experimenting with how existing systems can be extended or adapted. They’re often early adopters, curious coders, and strategic experimenters. They evaluate new features as they roll out in their existing software. They prototype integrations and pilot projects. And critically, they bring those learnings back to their firms—translating experiments into insights, and surfacing opportunities with real value potential. They ask: What’s possible? What’s practical? And what’s worth pursuing now?
At the same time, these same leaders are increasingly taking on the role of AI Shepherds—guiding their firms through the new territory of AI adoption. They teach AI literacy across departments. They facilitate training on creating effective AI prompts and validating AI-generated outputs. They create how-to guides and usage policies. They help teams assess what level of literacy different roles need—and what it really means to be an “informed user” of AI tools.
Some KM leaders are helping write firmwide policies on responsible use. Others are working closely with leadership, legal, HR, marketing, design technology, and practice teams to craft governance plans that reflect both risk and opportunity.
They understand that implementing AI isn’t just a technical challenge—it’s a cultural one. And that makes KM leaders, with their change management skills and deep understanding of how knowledge flows, uniquely suited for the job.
Some of them are officially part of AI task forces. Others are simply jumping in. But across the board, we’re seeing KM leaders play a pivotal dual role in this new era: scouting the terrain ahead, and shepherding their teams through it.
Trend 8: Accelerating Knowledge Transfer from Experts to Emerging Professionals
One of the evergreen promises—and persistent challenges—of knowledge management in AEC is scaling expert knowledge. In a business built on expertise and results, firms need to help emerging professionals become more effective healthcare architects, bridge engineers, project managers, or sustainability experts. Historically, that kind of learning has happened slowly: one project at a time, one mentor at a time.
But today, the urgency is rising. Many firms are facing a real-time talent crunch: senior experts are retiring, hiring is competitive, and there’s pressure to ramp up new staff faster than ever.
That’s where KM teams are stepping in—not just to collect or store knowledge, but to accelerate the transfer of critical insight from experts to emerging professionals. One increasingly popular method is structured expert interviews. Instead of asking a senior architect to write down everything they know (which rarely works), KM teams are capturing informal conversations on video, then using AI tools to transcribe, summarize, and transform them into searchable, reusable knowledge. In some cases, those interviews also form the basis for courses or training programs.
These interviews take different forms. Sometimes they’re one-on-one conversations recorded in a conference room or over Zoom. Other times, they’re held as live learning events—inviting staff to listen in, ask questions, and absorb the exchange in real time. In some firms, experts are even interviewing one another, creating space for reflection and storytelling while modeling a culture of shared learning. Regardless of format, the resulting videos become assets that can be reused across onboarding, training, and AI-powered search.
KM teams act as knowledge brokers, working across departments and generations to extract tacit expertise and make it available in the flow of work. That includes surfacing bite-sized insights through search, packaging repeatable methods into standards, or embedding lessons learned into onboarding programs.
AI is playing a major role throughout this process—making transcription and summarization faster, enabling retrieval through tools like AI search, and helping turn raw insights into new knowledge assets for experts to review. But the shift is as much cultural as it is technical. The best KM teams are creating lightweight, repeatable ways to scale expert knowledge without putting all the burden on the experts themselves.
For the first time in years, it feels like firms are making real progress on this long-standing challenge. And it’s not just about efficiency—it’s about continuity, capability, and preparing the next generation to lead.
Trend 9: Recording Knowledge in the Flow of Work
One of the most promising shifts in KM 3.0 is a growing instinct to record knowledge as it’s being shared in the flow of work. For years, we’ve asked experts to step away from their billable work and carve out time to share what they know—usually through interviews, write-ups, or one-off learning events. And sometimes that is still the right approach. But the best firms are now recognizing that some of the most valuable knowledge doesn’t get surfaced in a vacuum, it emerges organically in the flow of work.
Design crits, town halls, QA/QC reviews, tech talks, team debriefs—these are not new practices. What’s new is a heightened awareness that these moments often surface insights worth capturing. What used to be a fleeting comment or forgotten answer can now become a reusable, searchable knowledge asset with the tap of a record button. And increasingly, KM teams are positioning themselves to capture that magic in real time.
Sometimes that magic is obvious: an expert says something that surprises even them. A nuanced decision gets explained in a way that suddenly clicks for everyone. A great question surfaces a new pattern. You can feel the room shift when it happens. These moments aren’t just insightful—they’re irreplaceable. And when they’re recorded, they become learnable for others across time, location, and role.
There’s also a growing recognition that video is one of the richest, most efficient formats for knowledge transfer. People speak more naturally than they write. They gesture. They emphasize with rhythm, tone, or volume. They build on the ideas of others. With AI-powered transcription, summarization, and timestamped search, those recordings can now be turned into assets—either as raw clips searchable by topic, or as inputs for more polished summaries and structured documentation.
Some firms are migrating entire archives of recorded content—town halls, team meetings, learning sessions—into their intranet or search system to make them accessible long-term. Others are starting smaller: recording select moments and uploading them as short clips. Either way, KM teams are becoming curators of the recordable workplace, helping ensure that what gets said once doesn’t have to be said a hundred more times.
Of course, this shift also brings new tensions and decisions. Not everything should be recorded. Firms are actively negotiating when it’s appropriate to capture a conversation and how to handle sensitive topics like client names, project challenges, or QA/QC reviews that might carry legal risk. Some are developing shared norms or lightweight policies. Others are relying on good judgment—learning to recognize when the knowledge being shared should (or should not) be captured on video.
What’s most important is the mindset: a collective posture that says, this moment matters—let’s preserve it. Whether that recorded moment stands on its own or becomes a stepping stone to something more formal, the thinking is the same—if knowledge is being surfaced, let’s make it last.
In the KM 3.0 era, recording isn’t just about efficiency—it’s about equity and scale. It ensures that the insights from today’s conversation aren’t only shared with its participants. It turns a one-time conversation into a source of organizational (and searchable) knowledge. And it helps build a smarter firm—one recording at a time.
Trend 10: KM and L&D Are Converging
Historically, knowledge management and learning & development often existed as adjacent but separate functions within AEC firms. KM might report through IT, operations, or marketing—or sometimes directly to the CEO—while L&D often lived under HR, design technology, or as a standalone team focused on compliance and professional development. Despite their clear overlap in purpose and shared emphasis on capability building, these two domains have, until recently, followed fairly distinct paths.
Across the KA community, we’re seeing a clear trend: KM and L&D teams are partnering more deeply or merging entirely. In some firms, knowledge managers now lead the L&D function. In others, learning professionals are embedded in KM teams to strengthen coordination and ensure closer alignment between learning programs and knowledge strategy. And even when the two functions remain distinct, they’re working more closely than ever before.
Why now? In part, because of AI.
As AI search tools like Synthesis become central to the way firms access knowledge, the boundary between "learning content" and "knowledge content" is blurring. A video about stair detailing best practices might once have lived only in a learning management system (LMS) and only been accessed when assigned as part of an individual’s development plan. Today, that same video, recorded and transcribed, becomes searchable organizational knowledge for the entire firm. And so it matters less whether something is structured as formal learning or informal documentation. What matters is: Can people find it? Can it help them learn? Can it help them improve their performance?
That shift is reshaping how firms think about both strategy and structure. If AI search is going to index and surface both learning and knowledge assets, then those assets need to live in the same system—or at least in integrated systems. And increasingly, they do.
At Knowledge Architecture, we recognized this shift and decided to build a learning management system into Synthesis. With Synthesis LMS now in private beta—and headed for public release later this year—clients will soon be able to host all their courses, learning paths, and training videos in the same place they manage standards, policies, best practices, internal communications, Q&A, discussions, and employee and project data.
One platform. One search. One integrated experience.
This shift isn’t just about software consolidation—it’s about strategic alignment. When knowledge and learning content live side-by-side in one platform, they’re easier to manage, curate, and improve. It also becomes easier to deliver a cohesive user experience: employees don’t need to think about where to look, just what they need to learn or find. And with generative AI powering both search and structured learning, there’s a growing recognition that KM and L&D need a shared roadmap.
It’s too early to tell whether firms will formally adopt new titles like Chief Knowledge and Learning Officer or Knowledge and Learning Manager. That language hasn’t fully emerged yet. But even without title changes, the spirit of integration is unmistakable. Knowledge and learning have always been two sides of the same coin. In the AI era, firms are beginning to treat them that way—building programs, platforms, and teams that reflect the full arc of how expertise is developed and delivered.
Trend 11: Short, Flexible, and On Demand: The New Era of Learning Content
Across the AEC community, learning content is becoming more agile. Formal presentations are being complemented by quick, focused recordings. And behind it all is a thoughtful shift—driven not just by technology, but by a deeper understanding of how people actually learn and share knowledge at work.
From the learner’s perspective, shorter, asynchronous content offers flexibility and control. It enables people to learn in a way that fits their schedule, their preferences, and their energy. Some prefer to start the day with a focused burst of learning. Others absorb ideas better in the quiet of late afternoon. When longer training videos are broken down into shorter lessons, it’s easier to learn at your own pace.
It’s not just about convenience. This shift also encourages more intentional engagement. When content is delivered in digestible pieces, learners can focus more deeply on each idea. Some firms are even adjusting their defaults—aiming for 15 or 30 minute sessions instead of the traditional hour—not to cut corners, but to focus attention. The goal is clarity, not compression.
For content creators, shorter formats make it easier to share insights. You don’t have to build a long presentation around a single topic. You can speak to one idea—something fresh, relevant, or timely—and share it in a natural, human way. Whether it’s a one-take Zoom recording or a brief conversation with a colleague, the lower effort required makes it easier for more people to participate in creating learning content.
This more flexible approach doesn’t mean we’re saying goodbye to live training. In fact, many firms are elevating their live sessions by pairing them with asynchronous content. In a “flipped classroom” model, the core material is delivered in advance—through short videos or modular lessons—freeing up in-person time for questions, reflection, and group discussion. It’s a more intentional, more interactive use of shared time.
And from a KM perspective, shorter lessons offer real advantages. Yes, AI Search can find insights in long-form recordings, but short, focused content is easier to maintain. If a policy or workflow changes, it’s much easier to update a ten-minute clip than to revisit and revise an entire hour-long training. This lowers the cost of maintenance and increases the overall health of your learning library over time.
Ultimately, this trend reflects a broader cultural shift. We’re moving toward a learning environment that’s more learner-centered, more adaptive, and more sustainable. We’re giving people the tools and permission to share what they know in ways that feel natural—and making it easier for others to find and apply that knowledge when they need it most.
And perhaps most importantly, we’re adjusting to the pace of change.
In a world where the half-life of knowledge is shrinking—where technologies evolve, workflows shift, new materials become available, and new ideas surface daily—we need learning strategies that can keep up. Not everything we teach will be relevant in five years. Sometimes, it needs to be relevant this quarter. Or this week. Or just long enough to move a project forward.
That’s why short, flexible, and on demand content matters. It meets the moment. It embraces the velocity of change. It lets us respond in real time, without being slowed down by perfectionism or production bottlenecks. In this environment, lightweight doesn’t mean lower quality. It means better fit.
Trend 12: Orchestrating the Learning Organization
Knowledge management has never been a solo act. It’s been more like a band. But in the AI era, it’s becoming a full-blown orchestra.
For years, KM teams have worked to cultivate a culture of contribution across their firms—to help people understand that knowledge management isn’t just a department, it’s a shared mindset and responsibility. That’s not new. What is new is the level of energy, engagement, and demand that KM teams are now experiencing from across their organizations. Everyone wants in.
AI is the accelerant. When people see how easy it is to retrieve knowledge—how fast and accurate AI-powered search can be, how rich and accessible video transcripts are, how training content can now be tracked and tailored—they get it. Senior leadership, subject matter experts, business unit leaders, project managers, and emerging professionals are all suddenly leaning in. They understand the value of well-organized project data. They see the benefit of surfacing hard-won expertise. They realize that what used to live in silos—spreadsheets, email threads, hallway conversations—now has a place to land, and a way to be retrieved.
That enthusiasm is reshaping the role of KM leaders, too. Many are stepping into new, cross-functional roles as educators and guides—teaching AI literacy, co-writing usage policies, and coaching teams on how to contribute content that’s both human-friendly and machine-readable. Some are helping their firms explore what’s possible with AI; others are helping shepherd adoption responsibly. Often, they’re doing both. This work goes beyond deploying new tools—it’s about building confidence, trust, and shared capacity across the firm.
That’s the opportunity. It’s also overwhelming.
The best KM leaders are meeting this moment not by trying to do it all themselves, but by building distributed capacity. They’re developing networks of curators and contributors across the firm. They’re embedding knowledge management roles and responsibilities into project teams, practice areas, and operational departments. They’re formalizing stewardship of key content areas, investing in reusable patterns and systems, and coaching others to think like knowledge managers—even if that’s not their title.
KM is shifting from gatekeeping to orchestration.
This transition requires new mindsets and new muscle: teaching people how to manage content with care, encouraging them to think about storytelling and context, showing them how to align their contributions with firm-wide knowledge and learning goals. It means getting serious about content ownership, metadata, and governance—but doing it in a way that scales. It also means helping teams connect the dots across learning, operations, digital tools, and culture.
The work is no longer just KM work. It’s organizational work. It’s the work of building a learning organization.
That phrase—learning organization—has been the North Star of this whole Smarter by Design series. And in many ways, this final trend is the foundation that makes all the others sustainable. The future of knowledge management in AEC isn’t just smarter technology or better systems. It’s a shared commitment to learning, growth, and contribution—distributed across the firm and guided by a strong, strategic core.
And on a personal note: I’ve never been more excited about this work than I am right now. The past year of collaborating with KM leaders, technologists, and firm executives across the AEC industry has been one of the most inspiring of my career. The energy, the creativity, the generosity—this moment we’re in feels like a once-in-a-generation opportunity. And I couldn’t be more grateful to be part of it.
Let’s keep going.
