I spoke at AEC Innovate last month about Synthesis Knowledge Agents and walked through several examples of what AEC firms in our beta program are beginning to build.
In the presentation I showed a contract and NDA review agent that could evaluate an agreement against a firm’s own standards and accumulated judgment. I showed a fee proposal agent grounded in how a firm approaches pricing and scope. I talked about proposal assistants that could draw not only on project information and boilerplate, but also on a firm’s own guidance for writing strong project descriptions and approach statements.
The agents were doing different jobs, but they had something important in common: they were all powered by their firms capturing some version of “this is what good looks like” from their experts.
After my talk, the CEO of one of our prospective clients came up to me and said he loved what I shared. The agents I showed seemed powerful and genuinely useful, and he could immediately imagine a whole collection of them his firm might want to build.
But there was one part he could not quite get past. Was he supposed to sit down alone and write all of those best practices from scratch? Were the people on his leadership team supposed to somehow find the time to document everything they knew before the firm could build useful knowledge agents?
The whole thing suddenly felt daunting and I saw his shoulders had slumped.
Seeing his reaction, I shared the behind-the-scenes story of how MBH Architects created their NDA review agent.
Before their CFO retired, Ryan McNulty, President at MBH Architects, made sure to get him in a room for a brain dump. Over the course of a few conversations, Ryan and his team walked through how he reviewed contracts, specifically NDAs. Which clauses were always unacceptable. Which ones the firm would negotiate, and under what circumstances. What unusual language tended to appear, and how the firm had learned to respond. Decades of contract judgment, compressed over time into instinct, were suddenly made visible.
They distilled those conversations into a set of NDA best practices. Those best practices now power a contract review agent. Any manager at MBH can upload a new client NDA, and the agent can go clause by clause, identify potential risks, explain its reasoning, and suggest a response based on the firm’s own accumulated judgment.
The CFO had not sat down alone with a blank page and written the definitive guide to reviewing NDAs. Ryan and his team had partnered with him to draw that knowledge out through conversation, examples, questions, and iteration. AI could help accelerate that process even further.
The CEO’s reaction was immediate.
“Oh my gosh. I didn’t even think I could use AI that way. It's so obvious now that you are saying it.”
Between the people on his marketing and operations teams, the experts throughout the firm, and AI, he could imagine capturing a great deal of this knowledge much faster than he had assumed.
Building useful knowledge agents no longer felt out of reach. What had changed wasn’t the technology, it was his mental model for knowledge capture.
He had been imagining an expert alone with a blank page.
But there’s a better way.
A Conversation, Not a Blank Page
The retiring CFO at MBH did not sit down alone in front of a blank document and write the definitive guide to reviewing NDAs.
Instead, Ryan and his team partnered with him through a series of conversations. They pulled up real examples of NDAs the firm had already reviewed and talked through what he had changed and why. They explored exceptions, asked questions, and surfaced judgment that, after decades of experience, had become almost instinctive.
From there, those conversations and examples could be shaped into something structured. The expert did not have to create a finished product from scratch. He could react to something concrete, clarify what was missing, and correct where his judgment had been misunderstood.
This is a fundamentally different ask than telling a busy expert to document everything they know on their own. It is also often a better way to capture expertise.
The longer someone has practiced a craft, the more their knowledge becomes invisible to them. They recognize patterns a less experienced person would miss. They skip steps because those steps have become obvious and embedded in their tacit knowledge. They know “what good looks like” without necessarily having articulated why.
A good interviewer can slow that process down by asking questions like: Why did you make that change? Would you always handle it that way? What would a junior person miss here? Can you give me an example?
The expert brings the expertise. The interviewer brings curiosity, and their relative naïveté can be an asset because it requires the expert to make their thinking explicit.
Increasingly, AI can help both of them. It can help an interviewer prepare questions, analyze transcripts, organize ideas, identify gaps, and turn raw conversations into useful first drafts. Sometimes an expert works directly with AI to capture their knowledge. Sometimes another person is much better at drawing the knowledge out of them. Most of the time, AEC firms use a combination of approaches.
The important part is that the expert doesn’t have to do this alone.
Introducing Modern Knowledge Capture
I have started thinking of this new approach as Modern Knowledge Capture.
Modern Knowledge Capture is a collaborative, AI-enabled approach to partnering with subject matter experts to surface, shape, activate, and maintain what they know as organizational knowledge and capability.
Instead of handing an expert a blank page and asking them to create best practices from scratch, Modern Knowledge Capture starts with the knowledge that already exists—in that expert’s head, in examples of their work, in conversations, recordings, documents, and other accumulated artifacts—and builds a process that makes their knowledge useful to others.
The subject matter expert remains at the center of the process, but they no longer have to do all of the work themselves. Other people can partner with them to surface what they know, shape it into something clear and reusable, and turn it into guides, learning experiences, knowledge agents, and other resources that can be put into the flow of work. AI can accelerate every step of that process.
The result is not simply a faster way to create documentation. It is a fundamentally different approach to knowledge capture.
The traditional approach treated knowledge capture as an assignment we gave experts. Modern Knowledge Capture treats it as an AI-enabled partnership we build around them.
Interviewers, Editors, and Producers: The Partners Behind Modern Knowledge Capture
At the center of Modern Knowledge Capture is a partnership between subject matter experts and the people who help capture, distribute, and maintain their expertise. I increasingly think of three roles as especially important: interviewers, editors, and producers.
Interviewers help surface what experts know through conversation, asking questions, revealing their assumptions, and drawing out examples and judgment that experts themselves may no longer realize need to be explained.
Editors help shape those raw conversations and materials into drafts of codified knowledge, practices, and processes, then work with the experts to clarify, correct, and improve them.
Producers help turn that codified knowledge into assets the rest of the organization can actually use, such as guides, videos, courses, knowledge agents, or some combination of them.
These do not necessarily need to be three different people. One person may play all three roles. AI can support all three. The expert may take on parts of the process themselves. What matters is that we are no longer assuming the expert must personally perform every step required to capture, shape, package, and distribute their expertise.
I am seeing versions of Modern Knowledge Capture emerge across the KA community. Here are just three examples of many:
At BA (formerly Boulder Associates), Todd Henderson has developed a repeatable practice of turning conversations with experts into shareable knowledge. One of his colleagues, Darci Hernandez, had deep expertise in the politics of healthcare construction in California. She knew she had something valuable to share, but also knew she was unlikely to ever find the time to sit down and write it all out. Todd offered a much simpler proposition: give him half an hour and just talk it through. He guided the conversation, asked questions, and drew out examples, then used AI to help turn the resulting transcript into assets the rest of the firm could use.
At Shepley Bulfinch, Jess Purcell and her team have been taking a video-first approach to capturing technical knowledge. For their Autodesk Construction Cloud (now Forma) learning series, the subject matter experts spent roughly two hours preparing and three hours recording. From there, other team members handled much of the editing and production, and AI helped turn what had already been explained into written summaries and detailed step-by-step guides. Jess estimates that producing equivalent highly technical documentation through a more traditional process could have required around 200 hours of subject matter expert effort.
At Lionakis, Kristina Williams and Laura Knauss are building a more intentional modern learning program in which a subject matter expert provides the core knowledge, while a learning coordinator helps turn that knowledge into presentations, edit the resulting videos, and draft lesson descriptions and course summaries using AI. Those materials can then become part of learning experiences that combine short videos, exercises, and live collaborative discussion.
The details of each firm’s approach to Modern Knowledge Capture are different, but the direction is remarkably consistent. Experts still provide the knowledge and judgment, while others partner with them to make that expertise accessible, useful, and easier to maintain across the firm.
Knowledge Doesn’t Have to Be Perfect to Be Useful
There is another old mental model I think we need to let go of: the idea that knowledge has to be complete before we can put it to work.
Knowledge management has often borrowed its operating model from publishing. Capture everything. Create the comprehensive guide. Review it carefully. Approve it. Launch it as a finished artifact.
This can make the work feel even more daunting. If the goal is to exhaustively capture everything an expert knows before anyone can benefit from it, the bar for getting started becomes impossibly high.
The first version of MBH’s NDA best practices did not need to anticipate every contract the firm would encounter for the next twenty years. It needed to capture enough of the CFO’s judgment to become useful. Once they had that foundation, they could begin testing it against real NDAs, see where it worked well, and discover where the guidance was incomplete.
This is another important principle of Modern Knowledge Capture: capture enough of what you know to make it useful, put it to work, and continue making it better as you learn more.
In a world where the half-life of knowledge is getting shorter, the goal becomes less about producing definitive artifacts and more about creating useful starting points that can evolve with the work.
Knowledge That Gets Used Gets Maintained
Putting knowledge to work also changes what happens to it over time.
One of the persistent challenges with knowledge management is that we can spend enormous energy creating something, publish it, and then watch it slowly become outdated. A document sitting quietly on a server may never tell you when something is missing, when the practice has changed, or when the guidance no longer reflects how the work is actually being done.
Knowledge that is put into the flow of work creates a very different feedback loop.
Once MBH’s NDA best practices were deployed through an agent, every new review became another opportunity to test the underlying knowledge. A new clause could expose a missing rule. An unusual situation could reveal that the guidance needed more nuance. The agent might even surface something more fundamental: a situation where the firm itself had never fully clarified how it wanted to respond.
Those moments create much better questions for the expert. Instead of asking, “What else do you know about NDAs?” you can ask, “The agent did not know how to handle this. What are we missing?”
The same thing can happen when knowledge becomes a course people regularly take, a guide powering common AI search queries, or another resource embedded directly into the way work gets done. People encounter the gaps. They ask questions. They provide feedback. The knowledge keeps generating signals about where it needs to improve.
And this is where interviewers, editors, and producers can continue to play an important role. Their work does not have to end when something is published. They can bring the right questions back to subject matter experts, refine the underlying knowledge, and keep the courses, guides, agents, and other resources built on top of it current.
In that sense, putting knowledge into the flow of work does more than make it useful. It creates the conditions for it to stay useful because it gets maintained.
Publication is no longer the end of the process. In Modern Knowledge Capture, putting knowledge into use becomes an essential part of how we continue capturing, refining, and maintaining it.
Start with the Passion Budget
If you're that CEO from AEC Innovate I mentioned earlier, this might be the point where a new worry creeps in. Even if you believe all of this, even if you can picture the agents you'd want to build, you might be wondering who on your team actually has time to partner with experts on knowledge capture.
For years, much of the best knowledge and learning work in AEC firms has been powered by what I think of as the passion budget.
Someone in marketing starts capturing project stories because they know the firm is losing valuable knowledge. Someone in design technology gets tired of answering the same question and creates a better resource. Someone in operations sees a recurring problem and begins documenting a better way to handle it. A project manager who loves developing people starts building training for the next generation.
This is how a remarkable amount of progress gets made, and I do not think firms need to abandon that model in order to begin working differently.
You do not need to build a large knowledge and learning team before you can start practicing Modern Knowledge Capture. The interviewer, editor, and producer roles I have described throughout this piece do not necessarily need to be new job titles. They may be played by people in marketing, design technology, operations, learning and development, or by a particularly curious and motivated practitioner who sees an opportunity and wants to help.
One person may play several of those roles at once. AI can help them move much faster. The important thing is that someone has enough interest, curiosity, and capacity to help an expert get what they know out of their head, shape it into something useful, put it into the flow of work, and keep improving it over time.
So start with the passion budget.
Find a problem that matters. Find an area where valuable expertise is trapped inside too few heads, where the same questions keep getting asked, or where the next generation needs to learn something faster. Give someone enough room to experiment and see what happens.
You may be surprised by how much one motivated person, working with the right experts and increasingly with AI, can accomplish.
Beyond the Passion Budget
The interesting thing is what happens when Modern Knowledge Capture starts producing meaningful results.
One useful knowledge agent reveals three more opportunities. One successful learning experience exposes an entire body of expertise that could be captured next. One expert realizes they no longer have to personally answer every foundational question, and starts seeing other places where their knowledge could travel farther.
The question is no longer whether the work is valuable. The question becomes how much of it the organization has the capacity to do.
At this point, having knowledge and learning work remain the ninth thing on someone’s priority list starts to place a natural ceiling on how fast and how far the firm can go. There are only so many experts they can interview around the edges of another job, only so many learning experiences they can produce between other deadlines, and only so many bodies of knowledge they can help organize, activate, and maintain.
This does not mean every firm needs to run out and hire a full-time knowledge and learning management team. The next step may simply be giving an existing person more dedicated time. It may eventually mean creating a new role or building a dedicated team. Different firms will reach this point at different times.
But eventually, every AEC firm will reach the breaking point of relying on the passion budget for knowledge and learning management.
The pace of change is accelerating, the knowledge our people need is changing faster, experienced people remain one of our scarcest resources, baby boomers are retiring, and AI is creating new ways to make what those people know available to far more people, in far more situations, than was possible before.
As Modern Learning Organizations see this opportunity, they are realizing they want to move faster than the passion budget allows.
They are deciding that helping experts transfer what they know, accelerating the learning of the next generation, and turning individual expertise into organizational capability deserves more than whatever time their most committed people can find in the margins of their weeks.
They are investing in Modern Knowledge Capture to become smarter. By design.
Will you join them?