AI for Owner's Project Requirements (OPR)
Turning owner interviews and goals into a first-draft OPR document.
Quick Answer
AI for Owner's Project Requirements uses natural-language tools to turn owner interviews, meeting notes, and reference documents into a structured first draft of the OPR — the document stating an owner's functional, performance, and budget goals for a building. It speeds up drafting, but the owner and commissioning authority still review, refine, and own the final document.
The Full Picture
Writing a strong OPR is a translation problem: an owner knows what they want in terms of outcomes — the lab needs to stay operational during a power outage, tenants keep complaining about drafts near the windows — but the OPR needs those turned into specific, measurable criteria a design and commissioning team can test against. Doing that translation from scratch, across dozens of stakeholder interviews and a blank document, is one of the slowest steps in predesign.
AI-assisted OPR drafting works from the raw inputs — interview transcripts, workshop notes, an owner's existing standards documents, and prior projects' OPRs — and generates a structured first draft organized by the categories ASHRAE Guideline 0 expects: functional and operational needs, energy and sustainability targets, indoor environmental criteria, systems expectations, and budget and schedule constraints. It surfaces contradictions between stakeholders, such as facilities wanting low-maintenance finishes while design assumes a material the owner's standards prohibit, for someone to resolve.
In practice, a hospital's facilities team and clinical stakeholders sit through a half-day series of OPR interviews. Instead of a consultant spending two weeks writing up a document afterward, an AI tool produces a categorized first draft the same week, with citations back to which stakeholder said what. The commissioning authority and owner then spend their time refining and stress-testing the criteria for measurability, not typing up a blank document.
The OPR sits upstream of a general contractor's preconstruction work — it's an owner and commissioning deliverable finished in predesign, before there's a drawing set to estimate or bid. But a vague or late OPR ripples downstream: performance goals that never got pinned down turn into scope ambiguity in the construction documents, which shows up later as RFIs, change orders, or a completed building that technically meets the drawings but not what the owner actually needed.
A good AI-assisted OPR draft still gets tested the same way any OPR should: is every criterion measurable, can commissioning actually verify it, and does it avoid vague adjectives like 'efficient' or 'flexible' that can't be checked? A weak implementation treats the AI's draft as finished, skipping the owner and commissioning authority's review — the exact failure mode that makes OPRs untestable in the first place.
Real Examples
Common Misconceptions
People assume: An AI-drafted OPR is ready to hand to the design team.
Actually: It's a first draft. Per ASHRAE Guideline 0, an OPR only works if its criteria are specific and measurable, and only the owner and commissioning authority can confirm the draft reflects what they actually want, not just what the interview transcripts implied.
People assume: AI can generate an OPR without owner input.
Actually: The tool only structures and drafts from real inputs — interviews, notes, standards documents. Skip the stakeholder interviews and the draft is just a generic template with the categories filled in by guesswork.
Frequently Asked Questions
What is AI for Owner's Project Requirements?
Software that uses natural-language processing to turn owner interviews, workshop notes, and reference documents into a structured first draft of the OPR — the document defining an owner's functional, performance, and budget goals for a building project, organized around the categories a commissioning process checks against.
How does AI-assisted OPR drafting work in practice?
The tool ingests interview transcripts and existing standards documents, extracts stated goals and criteria, organizes them into OPR categories such as energy targets and comfort criteria, and flags contradictions between stakeholders for the owner to resolve before the draft is finalized.
Who uses it and when in a project?
Owners, commissioning authorities, and sometimes the design team use it during predesign, right after stakeholder interviews and before the design team begins responding with a Basis of Design.
How does an AI-drafted OPR relate to the Basis of Design?
The OPR states what the owner requires, in outcome terms; the Basis of Design is the design team's document explaining how the design meets those requirements. An AI-drafted OPR still has to be specific enough for the design team to respond to — vague criteria produce a vague Basis of Design.
Why does getting the OPR right matter, even before a GC is involved?
Because unmeasurable or missing requirements in the OPR don't get caught until much later — as ambiguous scope in the construction documents, disputed RFIs, or a finished building that meets the drawings but not the owner's actual needs. A clear, testable OPR upstream prevents costly rework downstream.