Welcome back to our MondayLive! Recap! This October, we’ve been deep-diving into the critical topic of specifying information for smart buildings. For our final session of the month, we shifted the conversation from the what and the how to arguably the most crucial question: “Who?”
Who is responsible for specifying this data? And who is it ultimately for?
The conversation moved beyond technical diagrams and data formats to focus on the human and organizational processes that make or break a building’s digital future.
The Core Challenge: The “Who” Determines the “Why”
The discussion kicked off with a fundamental point: if you don’t explicitly ask for data, you likely won’t get it. The frustration of hunting for critical information in an existing building—often with no single source of truth—stems from a lack of initial commitment.
As one member put it, “Who is responsible for the data that we say we want?” Is it the building owner, the specifying engineer, or the design-build contractor? Pinpointing this “who” is the essential first step.
Key Insights: Personas, Purpose, and Preparing for AI
The panel identified several critical angles on the “who” question:
- Follow the Persona, Find the Purpose: The “who” is never just one person. It’s a collection of personas, each with a different “why.”
- A hospital executive needs data to justify a chiller upgrade vs. a new MRI machine—a business case.
- An operating room manager needs real-time data on air changes and pressure for patient safety—an operational imperative.
- The specifying engineer needs engineering data to ensure systems are designed correctly from the start.
- The Operational Gap: A significant challenge is the gap between construction and long-term operations: the “who” changes, and the “why” evolves. Data specified for commissioning can get lost, leaving operations teams to reverse-engineer systems. The goal should be to change human behavior and processes to ensure data flows seamlessly throughout the building’s lifecycle.
- The Newest Persona: The AI: One of the most forward-thinking ideas was to specify data for a non-human persona: the Large Language Model (LLM) or Agentic AI.
- The argument: Everyone wants to use AI, but our systems aren’t ready. By specifying that all systems must be “AI-ready” (e.g., using RDF for semantic interoperability), we cut through debates about individual use cases.
- As one participant stated, “If you want to make it accessible for AI, it’s just as useful for AI as for humans.” This future-proofs buildings for the next wave of intelligence.
A Living Lab: The PAE Building Case Study
The conversation was grounded in a real-world example: the PAE Living Building (PLB). Interestingly, PAE is a specifying engineer, making its own building a perfect testbed.
The community is working on a retrospective project to create an ideal ASHRAE 232 specification for the building. This effort highlights a common struggle: even finding basic engineering data (e.g., which switch connects to which) can be a hurdle, despite advanced modeling in place. The PLB project is a crucial exercise in defining how to specify data correctly “next time.”
The Bottom Line
The October series concluded with a powerful consensus: Specifying information isn’t just a technical checklist. It’s about:
- Identifying the Persona: Who needs this and why?
- Bridging the Lifecycle Gap: Ensuring data is valuable from construction to decades of operation.
- Building for the Future: Preparing our buildings not just for human users, but for the AI personas that will inevitably manage them.
By focusing on the “who,” we can finally create the processes and specifications that deliver on the true promise of a smart, responsive, and efficient building.
From the chat Small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI.
https://research.nvidia.com/labs/lpr/slm-agents
Steve said I want my DAM data
A “data accessible model” is a system or strategy that defines and controls how users can access and use data within an organization. It determines who can view, retrieve, and utilize which data, and establishes the rules for data security, privacy, and usability. The goal is to remove barriers so data is available, understandable, and usable for decision-making without requiring specialized technical knowledge from every user.
