Shaping the role of the Quantum Expert in Construction Disputes with evolving Artificial Intelligence (AI)

As published in the Expert Witness Journal, Issue 62 August/September 2025, pp 58-61.

Background

An expert witness is a person with specialised knowledge, skill, or experience who provides opinion evidence to a court or tribunal on technical matters that fall outside the decision-maker’s expertise. In New Zealand, similar to other common law jurisdictions, the expert’s overriding duty is to assist the tribunal by providing an independent and impartial opinion on the instructed matters within his/her expertise by setting out all underlying assumptions, reasoning, and data supporting the opinion, rather than acting as an advocate for the instructing party.[1]

In construction disputes, quantum experts (typically experienced quantity surveyors or cost consultants) are appointed by parties or the tribunal/court to evaluate the financial impacts of claims pursued in the dispute. Their core function is to provide the tribunal with independent valuations of each party’s position, enabling a clear assessment of the monetary consequences under various liability scenarios. This generally involves reviewing project documentation and assessing the costs, damages, or overruns arising from variations, prolongation, disruption, or other project changes, grounded in objective evidence and professional judgement.

Quantum experts typically use structured cost models prepared especially for the dispute, usually in Microsoft Excel, to conduct their assessments. In preparing these cost models, the quantum experts generally analyse information from contemporaneous project records such as cost ledgers, timesheets, drawings, invoices, payment certificates, etc. These records assist the quantum expert in forming an opinion on the claimed costs and determining whether the claimed costs that relate to the alleged issues were actually incurred and are reasonable. Once the analysis is completed, the quantum expert’s findings are then formally presented in a written expert report, alongside appendices containing detailed calculations and supporting documentation, including the expert’s own valuation.

Due to the requirement for analysis of an enormous volume of data and the depth of analysis required, the traditional role of a quantum expert is resource-intensive and often results in substantial costs. Experts have traditionally relied on sampling techniques to manage these expenses and adhere to tribunal-imposed or agreed-upon timetables, wherein representative data subsets are thoroughly reviewed, and their findings inferred to the broader data set. However, even with such measures, expert fees can remain high, and the overall process remains time-consuming.

In this context, the recent advancement of AI, particularly in the automation of some quantity surveying tasks such as quantification, estimation, correspondence generation, and reporting, has raised a broader question: Will AI eventually replace a quantum expert?

This article examines how AI advancements are reshaping the role of quantum experts in the construction industry, focusing on current tools, their benefits and limitations, the admissibility of AI-generated analysis and reports in formal proceedings, and the importance of expert professional judgment.

Prevailing Tools that can assist Quantum Analysis

There is no doubt that integrating AI into the quantum analysis field would offer substantial benefits in speed, accuracy, analytical depth, and cost-efficiency, as AI is capable of processing vast amounts of project data and providing summaries much faster than manual methods. Its consistency and tirelessness further help eliminate human error, while machine learning uncovers patterns and correlations that may be missed otherwise. AI enables the execution of data-driven scenarios, such as predictive “what-if” analyses, which would be impractical to do manually. All these factors, including automating routine tasks, would allow a quantum expert to deliver deeper insight into the quantum matters and provide opinion evidence more efficiently.

Given the uniqueness of a dispute and the different types of analysis required before forming an opinion, no single AI platform can generate a complete quantum expert report by setting out all the data supporting the opinion. However, there are various tools that a quantum expert can use to assist in finalising some parts of a quantum expert report. Certain tasks a quantum expert can request AI assistance with, including a few advanced AI-enabled software tools that can be used, are discussed below.

1. Drawing Comparison and Change Detection

There are numerous AI-driven software tools such as CostX[2], CubiCost TAS[3], and Bluebeam Revu[4] available in the market to compare drawings and automatically detect changes between versions. These can help to quickly identify additions, deletions, and modifications between drawing revisions; verify the validity and extent of the variation claims; link drawing changes to the claims pursued in the dispute; and provide visual presentation of the evidence that can be incorporated into the report.

2. Automated Quantity Takeoffs

AI-driven software like Togal.AI[5] and Kreo[6] can automatically detect and measure elements such as walls, doors, windows, and areas on construction drawings and classify rooms and finishes in seconds, helping to reduce manual measurement time. This can also help reduce the time required to measure variations, generate visual summaries of the analysis performed by the expert, reduce manual input errors, and enhance reproducibility.

3. Document Review and Data Analysis

Microsoft Excel CoPilot (GPT integration) and Power BI with GPT plugins, which are integrated with spreadsheet tools, allow users to ask natural-language questions about ledger data, simplifying the data interrogation. AI can also generate pivot-table summaries, charts, and statistics from raw cost entries. Chat GPT Advanced Data Analysis modes can import large CSV ledgers and perform a comprehensive statistical review in one step.

These tools allow quantum experts to move beyond sample analysis and assess full datasets, such as complete cost ledgers with over 100,000 entries, providing much higher levels of rigour and eliminating potential sampling biases.

4. Valuation and Costing

Tools like Causeway Estimating[7], PriceAL (by C-Link)[8], and Buildxact enable the quantum expert to achieve improved speed, accuracy, and consistency in pricing and valuation exercises. For example, by using these tools, the expert can benchmark the assessment against historical project data to validate the reasonableness of claimed rates.

5. Report Drafting and Documentation

In line with other professional disciplines, we are also seeing the increased use of AI-driven tools such as ChatGPT Enterprise[9], Claude[10], Jasper[11] (among others) to generate structured, readable, and consistent report narratives. Whilst this may accelerate the drafting of repetitive sections and ensure a consistent structure across multiple claims, the same caveats apply as the quantum expert remains the author of the report.

Challenges and Limitations in Using AI for Quantum Expert Work

Although there are significant opportunities to increase the effectiveness, precision, and scalability of quantum expert work through AI, it is crucial to understand the limitations of its application to lower the associated risks. Since the role of an expert witness remains based on independence, and professional accountability, it is crucial to carefully control the use of AI to assist experts in data analysis and report drafting, thereby preventing potential challenges to the credibility or admissibility of the expert’s opinion. Rapid AI development necessitates the introduction of a new set of legal, ethical, and practical guidelines to ensure its implementation with caution and clarity. Below are some key concerns, illustrated with examples relevant to construction disputes.

1. Confidentiality and Data Protection

One of the primary concerns when using AI in expert work is the confidentiality of the project data. As many AI tools operate in cloud-based environments, experts must upload cost ledgers, drawings, or contractual documents to external servers to obtain AI assistance. For instance, using ChatGPT, Jasper, or Microsoft Copilot without an enterprise agreement could result in data being processed outside of a jurisdiction, potentially breaching confidentiality clauses or privacy laws. This risk is amplified when parties have not obtained consent from clients or stakeholders to handle sensitive records through third-party platforms.

2. Admissibility and Transparency

In formal proceedings, the admissibility of a quantum expert’s report hinges on several factors, with transparency, logical reasoning, and openness to scrutiny being particularly significant. Therefore, if a quantum expert presents conclusions based on AI analysis but cannot clearly explain how the AI tool arrived at its output, the evidence may be deemed inadmissible. Therefore, any analysis conducted using AI assistance must be accompanied by supporting documentation that clearly outlines the data inputs, logic paths, and algorithmic assumptions to avoid a tribunal considering it a “black box” output. The is explored further below.

3. Overreliance on Technology

While AI can efficiently surface patterns or outliers, it cannot determine contractual relevance or practical context. For example, a Power BI dashboard may flag a 20% cost spike in a specific trade, but only a human expert can interpret whether this is due to an agreed variation, late delivery, or misallocation. Relying solely on statistical output without expert interpretation risks drawing conclusions that are irrelevant or misleading.

4. Quality and Consistency of Source Data

For AI tools to generate accurate results, it is necessary to feed them with clear and structured data. However, in many construction disputes, the project documents may be incomplete, inconsistent, or misclassified. For example, subcontractors might submit invoices in various forms, or labour entries might be recorded under different cost codes in different months. Unless carefully cleaned and verified by the expert beforehand, feeding disorganised data into an AI model (whether via Excel Copilot or custom Python scripts) might result in skewed outputs and flawed valuations.

5. Lack of Industry-Specific Training

The majority of commercially available AI systems are not designed for construction-specific scenarios and are instead trained on large datasets. For instance, ChatGPT may misunderstand an expert’s request to write a narrative on ‘time-related costs’ as referring to general inflation rather than project prolongation costs as specified in the relevant contract. Similarly, GPT-powered Excel products may incorrectly group cost items if technical terms like ‘PC sum’ or ‘retentions’ are not recognised.

6. Interpretation of Unstructured Data

Quantum experts often deal with a wide range of unstructured information, such as handwritten site logs, scanned PDFs of current documents, and annotated architectural drawings. Even as AI-powered OCR programs – such as Adobe Sensei or Rossum – continue to improve, they may still overlook marginal notes, misread comments, or struggle to decipher scribbles that context-specific. Without expert review, for example, a scanned variation claim containing handwritten comments, could be misconstrued and lead to data loss or incorrect classification.

7. Resistance from Legal Stakeholders

Some arbitrators, judges, or opposing experts may be wary of AI-driven approaches, particularly if the methodology appears overly technical or lacks a clear explanation. For instance, there may be pushbacks or credibility issues if a quantum expert relies solely on AI-generated dashboards or automated summaries without walking the tribunal through the creation and validation process. Where expert roles are subject to stringent codes of behaviour, such as Codes of Conduct for Expert Witnesses, this scepticism may be extreme.

8. Ethical and Professional Responsibility

Based on the prevailing legal settings, the opinions offered are the responsibility of the quantum expert, not the AI tool. Therefore, using generative AI to draft reports, summarise claims, or value variations does not absolve the expert from their duty to independently verify and stand by their conclusions. For example, if an AI analysis concludes that the claimed prolongation costs were overstated by the instructing party or the opposing expert, the expert has an ethical duty to ensure verifiable data and proper reasoning support this, not simply because the model said so.

Admissibility of AI-generated Content in Proceedings

Courts have not yet imposed absolute bans on AI-assisted evidence, but they scrutinise its use under existing expert‐evidence rules. Some of the directions and guidance provided by courts in different jurisdictions are briefly explained below.

In the Matter of Weber[12], the Surrogate’s Court – USA (2024) ruled that the admissibility of AI-generated evidence should be evaluated through a Frye hearing to determine whether the methods employed are generally accepted within the relevant professional field. The Surrogate’s Court also highlighted the duty of legal counsel to disclose the use of AI in the preparation of expert evidence, given concerns regarding reliability and transparency.

In New South Wales (NSW), Australia, the Honourable Justice Brian J. Preston issued a Practice Note setting out the procedural rules for using generative AI in expert reports, which applies to all proceedings in the NSW Land and Environment Court and took effect on 12 February 2025.[13] It explicitly states that “Gen AI must not be used to draft or prepare the content of an expert report (or any part of an expert report) without prior leave of the Court.” This strict requirement reinforces the Court’s position on the importance of expert independence, transparency, and accountability in the preparation of expert evidence.

The Guidance for Judicial Office Holders in the United Kingdom, issued on 14 April 2025, outlines the challenges associated with AI-generated outputs.[14] The guidance makes it clear that judicial office holders are personally responsible for materials issued in their name, and that legal representatives are professionally obligated to ensure the accuracy and appropriateness of materials submitted to the court or tribunal. It implies that AI-assisted expert reports will be treated like any other: the expert must be able to justify their methodology, including the use of AI tools, in accordance with established evidentiary rules.

Outside of the above guidelines and practice notes, there are currently no reported case law decisions in New Zealand or the United Kingdom specifically addressing the use of AI-generated expert reports in formal proceedings.

Nevertheless, across most common law jurisdictions, it remains a core requirement that an expert witness must personally take responsibility for the content of their report and explicitly disclose all facts, assumptions, and materials relied upon. These foundational principles indicate that substantive reliance on AI in drafting an expert report, without proper disclosure and the expert’s critical oversight, would likely be inconsistent with the expert’s duty to the court. Such inconsistency may, in turn, be a relevant factor in determining the admissibility or evidentiary weight of the report in formal proceedings.

Conclusion – Could the Role of the Expert Witness be Fully Automated?

Depending on the scope of the quantum expert’s instruction, their duty is to typically assesses not just whether a cost exists but whether the price is causally linked and reasonable on the assumption that it is contractually recoverable. While AI can assist in improving speed, accuracy, and consistency in quantum expert tasks, its application is limited by the need for context, judgment, and legal reasoning, which still requires human expertise. Therefore, in my opinion, it is not practical for AI to fully automate the role of a quantum expert; however, it could be used to improve the efficiency, accuracy, and consistency of quantum expert reports.

The expert remains fully accountable for the accuracy, completeness, and conclusions of their report, including any sections generated with AI assistance. Accordingly, the quantum expert must exercise the utmost care when using AI tools to ensure that all outputs are properly explained and validated. Failure to do so may render part or all of the report inadmissible due to insufficient transparency around the AI-generated content.

About the Author

Damith is a highly experienced senior contracts and claims/quantum expert with over 15 years of industry experience, working on major construction projects in Asia and New Zealand. He has worked on numerous infrastructure projects (rail, airport, road, utilities, and earthworks) and buildings (residential, hotels, shopping complexes, factories, and warehouses) and has gained extensive experience administering various forms of construction contracts, including FIDIC, JCT, NEC3, NZS and Bespoke forms of Contracts. Damith’s areas of expertise are assisting in resolving disputes by producing claims (extension of time and costs), quantum expert reports (as the lead expert and primary assistant to the lead expert), and acting as a witness of facts in arbitral and court proceedings.

Damith Gayanga, BSc (Hons), LLM, MRICS FCIArb, RICS Registered Expert Witness
Associate Director, Quantum
+64 290 234 0568
[email protected]

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