Featured Guests
Hillary Thompson, Principal
Webinar Summary
As AI-powered search and learning tools become more capable, their effectiveness depends on something far more fundamental: the quality, structure, and completeness of a firm’s knowledge foundation.
Over the past year, MBH Architects has advanced a series of focused initiatives to strengthen how knowledge is captured, structured, and maintained across the firm. What began as a small proof of concept quickly expanded into a broader effort spanning marketing, studio-level practice knowledge, structured project data, and now firmwide learning.
In this webinar, Hillary Thompson, Principal at MBH Architects, shares how MBH is deliberately investing in its knowledge foundation to enable better AI outcomes, improve access to expertise, and create a continuous pipeline connecting project experience, institutional knowledge, and learning.
We explore:
How MBH scoped and executed targeted knowledge capture initiatives to demonstrate early, measurable value
How capturing practice knowledge, studio standards, and best practices is becoming essential groundwork for smarter retrieval and collaboration
The role of structured project data in unlocking new possibilities for search, storytelling, and institutional insight
How MBH is beginning to translate project experience and expertise into scalable learning through Synthesis LMS
Practical strategies for communicating value, building momentum, and sustaining long-term knowledge management initiatives
This session offers a practical, behind-the-scenes look at how one firm is strengthening its knowledge infrastructure to ensure the right knowledge reaches the right person at the right time—and how that foundation is enabling the next generation of AI-powered knowledge and learning.
Enjoy!
Webinar Timeline
Introduction
00:00 Welcome and Introductions
01:33 The Knowledge Usability Problem
02:51 Background on MBH
Four Knowledge Layers
03:58 Overview of the Four Knowledge Layers
05:33 Where Knowledge Lives
06:40 Making Knowledge AI Ready
08:12 Framework for Progress
09:27 Case Study: Marketing Proposal Language Library
12:40 The Solution: Curated Proposal Language Library
15:26 Better Results with Curated Knowledge
16:31 Synthesis AI Search Flywheel in Action
18:57 Measuring Time Savings
20:58 Knowledge Agent Preview: Project Experience Matcher
23:59 Knowledge Agent Preview: Scope Drafter
26:04 From Recall to Structure
26:58 Case Study: Studio Workspaces
28:55 Adoption and Sustainability
31:31 AI Search Changes Retrieval
33:48 Case Study: Plan Check Comments Library
35:21 Knowledge Agent Preview: Firmwide Best Practices Codifier
37:52 Case Study: Project Data
42:03 Knowledge Agent Preview: Design Precedent Explorer
43:59 Case Study: Learning and Synthesis LMS
45:35 Example Course: Insights to Learning in Action
47:36 Knowledge Agents Snapshot + Capturing Expertise
Q+A
50:40 Q: When do you use raw vs curated knowledge?
52:44 Q: What is your process for knowledge curation?
54:45 Q: What is your plan for rolling out agents?
57:34 Q: How is your knowledge team structured?
58:44 Q: Where does the experience and skills data live for staffing agent?
1:01:15 Q: Have you built any HR agents?
1:02:48 Q: How are you identifying and prioritizing use cases?
1:04:26 Q: What is your strategy for critical knowledge transfer?
1:06:47 Q: What is the setup for the plan check comments library?
1:08:03 Closing and Upcoming Events
