A quality engineer reviewing a large isometric drawing package works through it one document at a time, and errors that slip through are often found after fabrication.

AI-assisted review works differently. At the engineering contractor McDermott, researchers built a model that flags common design errors on engineering drawings, using examples from more than a dozen large projects. [1] Systems like this learn from past projects. The more ambitious ones aim to go further, identifying what has led to change orders before and which combinations of issues are likely to become rework.

AI-assisted quality assurance and quality control (AI QA/QC) review of engineering documents is now a real procurement category. As senior engineers, EPC firms, project owners, and developers begin evaluating these tools, the central question is how to judge what is actually being offered: what the system can review, what evidence supports its claims, and what a meaningful proof-of-concept should show before a vendor is selected.

What AI QA/QC Review Actually Means

Traditional engineering QA/QC is a sequential, manual process. Documents, including piping and instrumentation diagrams (P&IDs), isometrics, data sheets, and equipment specs, are reviewed by engineers working through packages at a rate determined by human bandwidth. Errors are caught when a reviewer happens to read the right document at the right time. Late catches, after fabrication or installation, are common. Change orders follow.

Document review begins with engineering drawings and specs. Photo: Chatham172/Envato Elements

AI-assisted review changes the workflow at three points. First, it processes document packages at a scale and speed that human review cannot match. It does not replace the engineer, but pre-screens for anomaly classes most likely to generate downstream rework.

Second, in more advanced systems, it applies pattern recognition across historical project data: if a particular combination of specification conditions has preceded a change order on previous projects, the system flags it on the current one. Third, it shifts the detection point earlier in the review cycle.

The buyers evaluating this category are deciding whether to move the detection point, and what that change is worth in contingency, rework cost, and internal rate of return (IRR).

The Four Things a Buyer Should Evaluate

1. Detection scope

The first question is less about headline accuracy and more about scope: What does the system actually review? AI QA/QC systems vary widely in what they are built to assess. Some are strongest on P&IDs, others are built around isometric packages, and some can check consistency across documents, such as whether a data sheet specification conflicts with a line list. A system that reviews one document type well but misses cross-document conflicts may be narrower than its marketing suggests. Vendors' answers on scope usually reveal more than a polished demo.

What to ask the AI solution provider: Which document types does the system cover? Which error categories does it flag? Can you show a sample output from a comparable project?

2. Integration with existing workflows
   
   

Integration means the tool fits into workflows engineers already use. Photo: DC_Studio/Envato Elements

The adoption risk on AI QA/QC tools is often integration, not capability per se. A system that requires a parallel document management environment, a separate review workflow, or significant change to how engineers submit and retrieve documents will face resistance regardless of how well it performs. The best tools tend to disappear into the background. Engineers keep working the way they already do, and the system surfaces flagged items inside the interface they're already using. 

What to ask the AI solution provider: Does the system plug into the document control platform already running on the project, or does it demand a separate login, a new interface, or another review step? How long does it actually take to stand up on a live job?

3. Audit trail

On large energy projects, QA/QC documentation must be defensible to lenders, regulators, insurers, and independent engineers. Any AI-assisted review system must produce an audit trail that satisfies those external requirements: what was reviewed, when, by which version of the model, with what result. If a system cannot produce a document-level audit trail that a project finance lender or independent engineer would accept, it can't be used on most projects where it would prove valuable.

What to ask the AI solution provider: How does the system handle audit trail requirements on projects with project finance structures, development finance institution (DFI) involvement, or regulatory permit conditions?

4. Change order signal detection

This is the criterion that separates AI QA/QC from traditional automated checking. In McDermott's study, for instance, a missing vent or bleed valve is a modest fix, but if the mistake is found only during construction, installing it can delay other work and affect the overall schedule. [1] The conditions that generate change orders (specification ambiguity, cross-document inconsistency, procurement misalignment) are often present in the document package well before the change order is raised. A system that only checks for errors against a standard has limited value. The stronger test is whether it can identify patterns that have led to change orders before: recurring specification gaps, cross-document conflicts, or procurement mismatches that may look manageable in isolation but become expensive when combined. This changes the financial equation.

What to ask the AI solution provider: Can the system flag conditions that are likely to generate change orders, not just conditions that deviate from a standard? What is the evidence base? How far in advance of a typical change order event does the system detect the signal?

What AI QA/QC Review Changes in the Financial Model

The financial case for AI QA/QC review is made at the project finance level. The logic runs as follows.

Catch defects earlier, and rework costs drop. That slows how fast the contingency budget is used up, which gives the project more room to maneuver right when cost pressure peaks. The result is direct IRR protection. On a $2 billion energy build, every 1% of cost kept out of overruns is worth $20 million.

Cost is only half the case. Projects can already be running behind before they even start. In the regions with available data, the typical power generation or storage project built in 2025 took more than five years from interconnection request to commercial operation. [2] Large power transformers alone can take more than two years to arrive. [3] With lead times like that already in the schedule, earlier detection of problems stops being optional. It needs to become a basic requirement for staying on track.

That requirement reflects a market in which every month of delay has a measurable capital cost. AI QA/QC review moves the detection point earlier, which reduces the frequency and cost of the events that squeeze the schedule in the first place.

The CFO case for this technology is often stronger than the engineering case. It is worth ensuring the evaluation process includes finance representation alongside the technical team.

How to Run a Vendor Evaluation

A meaningful vendor evaluation for AI QA/QC review has three components.

Collaboration on-site: reviewing documents together. Photo: Desizned/Envato Elements

1. Define the test package carefully
   
   

• The proof-of-concept should use a real document package from a completed project, one where the actual errors, change orders, and rework events are known.

• Give the vendor the same package a human review team processed.

• Compare detection rates, missed items, and false positives against the known outcome.

• A vendor unwilling to run against a known-outcome package is a vendor uncertain about their detection performance.
  
  

2. Ask for the false positive rate, not just the detection rate
   
   

• A system that flags everything generates noise that degrades engineering team trust and adoption. The practical question is what proportion of flagged items turn out to be genuine issues.

• A detection rate of 90% with a false positive rate of 40% is less useful in practice than a detection rate of 75% with a false positive rate of 10%.
  
  

3. Test the integration before the capability
   
   

• Run the proof-of-concept through the document control system the project team actually uses, not through a parallel environment the vendor has set up. If the integration works cleanly, the capability question becomes the primary evaluation. If the integration is difficult, the capability is largely irrelevant. An AI tool that engineers work around or avoid isn't doing its job.

• The questions to ask in a vendor demo: What is the typical implementation timeline on a live project? How is the audit trail structured for lender review? What is the false positive rate on comparable project types? How does the system handle multi-discipline cross-checks (not just within-discipline document review)?

What Good Looks Like

The first test for an AI QA/QC system is how naturally it fits into how engineers already work. It should review documents across disciplines, produce an audit trail lenders and independent engineers can trust, and plug into the document systems teams already use. Most importantly, it should catch the conditions that lead to change orders before they show up in a normal review cycle. That's the bar buyers should hold vendors to.