Suffolk and MIT model 17% to 20% AI cost savings on one multifamily project

Thu, 24 Sep 2026 19:45:00 -0400

Suffolk, the MIT Center for Real Estate and the MIT Media Lab City Science group published “Construction in the Age of AI: An Industry White Paper and Research Roadmap” on September 16, 2026. Its headline figure comes from a model Suffolk built on one multifamily project in San Francisco. By that model, AI applied across six parts of project delivery would have cut total cost by 17% to 20% and total schedule by 22% to 25%. The workflows in scope run from permit applications and drawing review through schedules, RFIs, submittals and procurement. The authors describe their evidence as “suggestive rather than definitive,” and the methodology appendix says it cannot yet support causal conclusions.

What it does

The paper sorts AI uses into six domains, which the authors call levers: design automation, offsite manufacturing, permitting, scheduling, skilled labor and subcontracting, and supply chain and procurement. For each one it collects reported savings from a literature review of 178 sources, plus case studies, expert interviews and survey responses. A roundtable held on March 11, 2026, with more than 50 industry participants, settled the list. The acknowledgments name participants from Procore, Autodesk, Oracle and Bluebeam.

The percentages in the table below are the maximums the authors found for each domain. Most come from early-stage pilots, and the sources include industries next to construction. A footnote in the paper says to read them as directional, and warns that they are not benchmarks validated on construction projects. The design automation figures also apply to a narrower base than the others, as the last column shows.

Maximum reported gains by AI domain, from the white paper's literature review
Domain	Example AI application	Time reduction up to (%)	Cost savings up to (%)	What the percentages apply to
Design automation	Generative design and BIM AI	39%	21%	Design cycle time; design and engineering cost
Offsite manufacturing	AI-optimized factory prefabrication	32%	14%	Project timeline; total cost
Permitting	Automated code-compliance review and application preparation	24%	8%	Project time; cost
Scheduling	ML-driven sequencing and optimization	18%	11%	Project timeline; total cost
Skilled labor and subcontracting	AI trade development, coordination and performance management	17%	13%	Project time; total cost
Supply chain and procurement	Predictive lead time, delivery optimization and process automation	15%	10%	Total project time; total cost

Source: suffolk.com

Suffolk then took one completed job and estimated what the six levers would have done to it together. Section 5 of the paper describes a 180,000-square-foot multifamily development finished in 2024. Table 1 breaks the result down by phase.

Suffolk's model of AI savings on the sample multifamily project, by phase
Phase	Baseline cost ($M)	Modeled cost savings ($M)	Cost savings (%)	Baseline time (months)	Modeled time savings (months)	Time savings (%)
Acquisition	27	0		3	0
Entitlement	1.4	0.05	4%	11	2	19%
Design	6	1.2	20%	24	7	30%
Preconstruction	3	0.3	10%	9	1	14%
Construction	128	28	22%	30	7	14%
Finance	7	1.2	17%
Cost escalation	7	1.2	17%
Total	179	32	18%	51	11	22%

Source: suffolk.com

Start at the bottom row. The model takes $32 million off a $179 million baseline, which is 18%, and 11 months off a 51-month timeline, which is 22%. Most of the dollars come from construction, where $28 million of the $32 million sits against a $128 million phase. Design has the largest schedule cut, 7 of 24 months. The model leaves acquisition untouched. The months do not add to the total because phases overlap, and the paper notes this under its table.

The authors then run the savings through to developer returns. By Suffolk’s internal model, unlevered IRR, the project’s return before any debt, rises about 5 to 6 percentage points, for example from 15-20% to 20-25%. Yield on cost rises 1 to 2 points.

The readers who get the most from the paper are preconstruction leads, VDC and scheduling managers, and CFOs who have to justify an AI budget. The domain sections name tools already sold to contractors. They cite ALICE Technologies and nPlan for schedule optimization, OpenSpace for tracking installed work against the plan, and TrunkTools’ TrunkRFI agent for flagging unnecessary RFIs and drafting needed ones. Procore’s in-platform agents come up for RFI deflection and drafting, and LightTable and BuildCheck for drawing review. (The paper identifies nPlan as a Suffolk Technologies portfolio company.)

Evidence so far

The authors do not claim an independent measurement of AI on any construction project, and no job has recorded the 17% to 20% savings. The appendix calls the paper a methodology statement, and it lists what a real test would need: project-level data covering several hundred projects, and comparisons between AI-assisted and conventionally managed jobs that control for complexity, geography, delivery method and owner type.

The per-domain figures come from other parties, and the paper attributes them. Volumetric Building Companies reports up to 47% schedule savings from offsite manufacturing. The paper cites a Deloitte estimate that AI-assisted estimating cuts budget and timeline deviations by 10% to 20% and engineering hours by 10% to 30%. In its passage on ALICE Technologies, the paper says AI planning tools cut costs by up to 30% on one large solar farm, and it does not name the project or its baseline.

Suffolk calls its model a first pass, and some figures in the paper disagree with each other. If you plan to quote the paper in a budget request, check these first:

Baseline cost. The footnote under the summary table anchors survey responses to a $180 million project. Figure 5 labels the sample project $150 million. Table 1 totals $179 million.
Duration. Section 5 gives 4.5 years from initial design to closeout. Table 1 gives 51 months, which is 4.25 years.
Scheduling savings. The summary table gives up to 11% cost savings for scheduling, and the scheduling section gives 8%. A footnote there says the model uses a 15% schedule reduction, where the literature reports up to 18%.

For a contractor choosing where to start, the expert ranking says more than the model. Participants scored each lever by Borda count, a ranking method that awards points by where an item falls on each voter’s list. Design automation collected the most points, 206, but 89 of them came from the design group voting for its own lever. With each group’s votes for its own lever removed, scheduling ranked first with 163 points. The skilled labor and subcontracting group spread its points almost evenly, between 19 and 26 per lever, which the authors read as a sign that field productivity depends on work done upstream.

Borda points by expert group (rows) for each AI lever (columns)
Expert group	Design automation	Offsite manufacturing	Supply chain	Skilled labor and subcontracting	Schedule optimization	Permitting
Design automation	89	51	45	33	69	41
Offsite manufacturing	20	29	18	14	15	9
Supply chain	27	16	16	20	28	12
Skilled labor and subcontracting	24	19	26	23	23	20
Schedule optimization	26	12	28	16	28	10
Permitting	20	14	16	20	28	37

Source: suffolk.com

How to measure it on your projects

So the schedule is where to start measuring. Track forecast finish slip, defined as the calendar days between the baseline substantial completion date and the forecast date in each monthly schedule update. If an AI scheduling or progress-tracking tool helps, slip on the pilot job should grow more slowly than on comparable jobs without it.

Capture the baseline before the pilot starts. Pull the monthly updates from three to five completed jobs of similar type and size, and record the slip at each update and at completion. The white paper asks owners to fund pilots with outcome measurement written into the project brief from day one, and a contractor’s own trial needs the same setup.

A first step on one job: export every monthly update from the P6 or Microsoft Project file for your most recently completed project, and chart the forecast completion date by month in Excel or Power BI.

Availability and cost

The white paper is free, as a PDF linked from Suffolk’s news page. The MIT research team is designing data collection instruments for the next phase. Firms that want to contribute project data, take part in surveys or join the research advisory panel can register through the MIT Center for Real Estate. The paper gives no date for that phase.

Suffolk | Construction Metrics

Suffolk and MIT model 17% to 20% AI cost savings on one multifamily project

What it does

Evidence so far

How to measure it on your projects

Availability and cost