Vendor Due Diligence for AI Deployment in Construction Operations

Vendor due diligence for AI in construction has entered mission-critical territory, and the standard software procurement checklist was never built for it. Firms are now signing multi-year platform commitments on the strength of vendor claims that are harder to verify than at any point in the industry's adoption curve.

Why AI vendor selection in construction differs from standard software procurement

Construction AI procurement has crossed into mission-critical territory at exactly the moment when vendor claims are most inflated and hardest to verify. That timing is not incidental. The industry has moved past isolated pilots into multi-year platform commitments, and the 2026 Korea Proptech Forum report found that nearly half of listed firms have adopted AI as a core business function, with the market targeting rapid annual growth even though only a minority of firms have actually scaled AI across their projects. That gap between adoption rhetoric and scaled execution is the environment operations leaders are now evaluating vendors inside.

Those questions assume a deployment environment where failure is contained, where a bad output can be caught and corrected before it does damage, and where the software operates on data that is already reasonably clean and centralized. Construction does not offer any of those conditions. A live project runs on sequencing dependencies, overlapping trades, shifting field conditions, and data that lives across procurement systems, scheduling tools, and paper-based field records that were never designed to talk to each other. The harder question construction AI procurement has to ask is whether the system will hold under the coordination, sequencing, and data conditions that define an active job site.

The failure modes that matter in this environment are specific to construction, not generic to software. A delayed purchase order that never propagates into the scheduling model, an AI-generated RFI that cannot be reconstructed months later in a dispute, a sequencing error that cascades through downstream trades without anyone catching it in time: these are construction-specific risks that a standard software vendor questionnaire was never built to surface. An operations leader running a vendor through a generic procurement checklist will get answers to the wrong questions. The evaluation has to start from what actually breaks on a job site.

How AI Deployments in Construction Fail

Most AI project failures in construction trace back to fragile data paths, broken process assumptions, and silent error propagation, not to weak underlying models. That distinction matters because it changes where the diligence effort should be concentrated. A vendor can have a genuinely capable model and still produce a failed deployment, because the failure originates in what surrounds the model rather than in the model itself.

The most common failure is process-level. An AI layer applied on top of a broken procurement or scheduling workflow does not correct that workflow. It executes the same broken logic faster and at greater scale. A firm with a flawed approval chain or a disconnected data handoff ends up automating its own dysfunction. Speed applied to a bad process produces bad outcomes more quickly, not better ones.

In multi-agent systems, a second failure mode compounds the first: silent error propagation. An agent can complete a task with a confident, well-formatted output while the underlying answer is wrong, and in a chained workflow, the next agent in the sequence inherits that error and builds on it without any checkpoint designed to catch the mistake. In construction, a silent error in energization sequencing or a procurement scheduling conflict carries direct safety and project consequences right now. It is a mechanism that operates the same way whether the output concerns a scheduling conflict or a life-safety sequencing decision.

A vendor demo cannot surface either failure mode, because a demo runs on clean, prepared data against a controlled, scripted workflow. It does not simulate what happens when a source system slows down, a schema changes without warning, or a required field comes back empty. AKF Partners' 2026 technical diligence framework identifies model drift, retraining requirements, and inference costs as forms of ongoing technical debt that do not appear during early evaluation but compound steadily after deployment. That debt is invisible in a sales cycle and accumulates afterward, on the buyer's schedule, not the vendor's.

A complication in vendor marketing itself compounds this risk. Many vendors describe proprietary AI capabilities that, on closer inspection, turn out to be off-the-shelf implementations with no defensible technical differentiation, and standard diligence processes rarely test for this. An operations leader who accepts a vendor's description of its own architecture at face value is skipping the one diligence step most likely to separate real capability from repackaged commodity tooling.

The strongest objection to all of this is the pilot result: a successful pilot proves the deployment works. It proves something narrower. Pilots run on curated data sets, controlled scope, and the vendor's most attentive engineers working the account. Production runs on the firm's actual systems, its actual data quality, and its own team operating without that dedicated support. A pilot demonstrates that the system can work under ideal conditions. It does not demonstrate that it will work under the firm's conditions.

The McCarthy Building Companies deployment, announced in June 2026, is the clearest available benchmark for what enterprise-grade construction AI integration actually requires, and it raises the bar that any independent vendor now has to clear. McCarthy signed a multiyear, multimillion-dollar agreement deploying a connected AI operating system spanning the full project lifecycle, from early design through field execution. The centerpiece of that deployment, Pulse, is McCarthy's AI Operations Suite, an AI-native platform designed to give field teams real-time insight, scenario planning, risk analysis, and decision orchestration.

What makes the deployment structurally significant is not the scale of the contract but the scope of the integration. The partnership spans field execution, estimating, contracts, bidding, quality assurance, logistics, and equipment planning, all connected through a shared ontology system so that insight generated in one business area propagates automatically into the others. That is a materially different architecture than a point solution bolted onto a single workflow. It treats the entire project lifecycle as one connected data layer rather than a set of isolated tools solving isolated problems.

DL E&C offers a parallel case that reinforces the same lesson from a different angle. Since adopting a connected data platform in 2022, the firm has built what it calls a Flywheel ecosystem, linking design, construction, and maintenance data into a single decision layer fed by more than 87 years of accumulated cost, quality, safety, and design data. That flywheel was only possible because the data infrastructure existed before the AI layer was deployed on top of it.

The structural implication for vendor selection extends well beyond firms operating at McCarthy's scale. A large general contractor with this kind of platform infrastructure now has a connected data layer that any point-solution vendor has to integrate with successfully, and vendors who cannot demonstrate credible integration into that kind of existing operating system are selling into an architecture that will reject them. For firms that are not building at McCarthy's scale, the case still defines the right question to ask a vendor. The right question for a vendor is how its system creates a connected data layer across project phases, and what integration into the firm's existing systems actually requires in practice.

Data readiness as a precondition, not a vendor promise

The most common reason AI deployments stall in construction is that data readiness got treated as the vendor's problem to solve rather than the firm's prerequisite to satisfy, and no vendor can fix a data environment it never had visibility into before the contract was signed. That reframing puts the burden of the first diligence step on the buyer, not the seller. James Garner, Head of AI and Data at Gleeds and a member of the RICS Construction Professional Group Panel, has said that making data AI-ready is one of the most critical and least glamorous steps a construction firm has to take before any AI deployment delivers real results, and that this step consistently has to precede effective tool selection rather than follow it. DL E&C's Flywheel ecosystem makes the same point through its own history: the data layer that now feeds its live planning system took decades to accumulate, and it was built before the AI layer, not assembled afterward to support it.

Before any vendor conversation begins, an operations leader needs a clear internal answer to a small set of questions. Project data has to be located: which systems hold it, at what level of fidelity, and updated on what frequency. The organization needs to know whether there is a single source of truth for project records, or whether procurement, scheduling, and field data sit in separate systems with no live reconciliation between them. It needs a clear answer for what happens to any AI output when a source system goes slow or goes down entirely, because a system that fails silently under those conditions carries a very different risk profile than one that fails safely and visibly. And it needs a named owner for the data governance process, with that ownership documented rather than assumed.

The economic logic behind this is not just operational discipline for its own sake. AKF Partners' 2026 framework argues that the quality and uniqueness of training data often determines AI product differentiation more than the sophistication of the underlying algorithm, and firms with clean, structured, proprietary operational data get substantially more value out of an AI deployment than firms without it. Daewoo Engineering & Construction's BaroDAP AI is a clean illustration of that principle in practice. Trained exclusively on the firm's internal contracts and specifications, it answers only from within that document corpus, and the reliability it achieves comes from data discipline rather than from any particular model sophistication.

Only once those internal questions are answered does it make sense to turn the same scrutiny on the vendor. A firm should ask whether its data stays in place or gets copied into the vendor's own system, and what the egress costs look like if the relationship ends. It should ask what connectors exist for its core systems, and who is responsible for maintaining those connectors when a schema changes on either side. It should ask how the vendor handles schema evolution, field renames, and table restructuring, and whether the vendor can trace an output back to its original source after that kind of change has occurred. And it should get a concrete answer on re-indexing cost and timeline at the firm's actual data scale, since that number rarely resembles the one quoted during a sales cycle.

Process integrity: evaluating whether the vendor's workflow design survives contact with real construction operations

Vendors who build an AI layer on top of existing process assumptions, rather than interrogating those assumptions first, end up reproducing the inefficiencies of the current workflow at machine speed, which is a worse outcome than not deploying AI at all. A slow, error-prone process automated is still a slow, error-prone process, just one that now moves faster and touches more of the operation before anyone notices the problem.

The design sequence that avoids this failure starts with identifying the specific painful process, mapping every decision point inside it, and asking what that workflow would look like if someone designed it from scratch for an AI agent rather than retrofitting AI onto the existing steps. The evaluation question for an operations leader is whether the vendor has actually gone through that exercise for the construction context they are selling into, or whether they have simply wrapped a generic workflow engine around construction terminology.

Scheduling AI that operates without procurement integration is a partial solution deployed into a mission-critical environment. A delayed purchase order that flags a schedule risk is only useful if the scheduling system and the financial system are connected end to end, and vendors who treat scheduling and procurement as separate modules are selling into exactly the gap where that kind of risk goes undetected. GS Engineering & Construction's AI Defect Prevention Platform illustrates what the alternative looks like. Developed internally and integrated directly into the firm's quality management system, it analyzes defect types and causes by construction process and was designed to be readable by foreign workers on multilingual job sites. The workflow itself was the starting point of the design process rather than an afterthought layered on once the model was built.

Testing for this kind of process integrity requires specific questions, not general ones. An operations leader should ask which specific construction processes the vendor has built and tested in production, as opposed to in a pilot or a demo. The vendor should be able to walk through a live construction workflow, whether that is submittals, RFIs, procurement, or energization sequencing, and identify precisely where its system sits in that sequence and what it hands off to the next step. The vendor should also be able to explain what happens when its AI output conflicts with a field decision: how the override gets handled, how it gets logged, and whether the system learns anything from that override afterward. A vendor without clear answers to these questions has not done the process design work the deployment requires, regardless of how capable the underlying model might be.

Supply chain integration deserves particular weight in this evaluation: supply chain failure, not model performance, is the actual cost driver behind the largest construction overruns on record. Forbes case studies of Hudson Yards, the Big Dig, and the Burj Khalifa point to material shortages, tariff exposure, and production constraints as the sources of overrun on these projects, and a vendor whose system does not address supply chain integration is solving a secondary problem while the primary cost driver goes unmanaged. Krane, a startup founded in 2022 by CEO Eshan Jayamanne, built its platform around exactly that logic, raising a seed round to expand a unified, real-time system managing submittals, lead times, purchase orders, and delivery schedules across the construction supply chain. The procurement-first, integrated design choice behind that platform shows what this category of tool needs to look like.

Operational controls and auditability: what the system must be able to account for after the fact

A construction AI system that cannot reconstruct why it produced a given output months after the fact is a liability. It is a liability that shifts blame around without ever providing the evidence needed to resolve a dispute or a quality failure. A large share of vendor evaluations fall short because the gap that shows up most often in audit assessments is the inability to produce evidence of what the tool actually did: which input it worked from, which model version generated the output, which reviewer signed off, which approval followed, and at what time each of those steps occurred.

When an AI system drafts an RFI, proposes a contract position, or changes a project record, the workflow needs to retain the source documents, the model's output, the identity of the reviewer, the final edited version, the approval, and a timestamp for each step. That record is what supports quality control, resolves disputes, and allows the model's performance to be evaluated honestly over time. Without it, a firm has no way to determine after the fact whether an error originated with the model, the reviewer, or a data source upstream of both.

Governance around that record has to be ongoing rather than a one-time checkbox at signing. The GLACIS 2026 AI vendor risk framework treats ongoing governance, meaning performance monitoring, incident response, and update cadence, as its own distinct evaluation category, and it requires vendors to produce monitoring dashboards, incident logs, and documented change management processes to satisfy it.

The audit-specific questions that follow from this are concrete. A firm needs to know what gets logged: prompts, retrieved source documents, tool calls, outputs, administrative actions, configuration changes, and access events. It needs clear answers on log retention periods, export formats, and whether an internal investigation could actually be run from those logs if it became necessary. It needs to know whether sensitive inputs can be redacted or masked in a log without losing the underlying traceability. It needs a clear description of how policy violations get detected and surfaced, and who is responsible for acting on them. And it needs to understand how prior outputs are preserved and versioned whenever the underlying model is updated or retrained, since a system that cannot preserve its own history cannot support a dispute or an audit that reaches back further than its current model version. A vendor able to answer all of these clearly, with documentation rather than assurances, has cleared a bar that most of the market has not yet reached.