AI in Construction Litigation: Better Evidence, Better Strategy

Read the PDF version here.

Construction litigation is document-heavy by nature. Major projects generate large volumes of emails, schedules, change order records, drawings, photos, and reports. The legal issues may be identifiable early, but the practical challenge is often the same: finding the evidence that actually matters quickly enough to shape strategy without driving up cost. That is where artificial intelligence is starting to change the model. 

Canadian reporting suggests that adoption is moving quickly, particularly among in-house teams, with recent survey reporting indicating strong perceived usefulness by our clients[1]. At BD&P, we feel the same and use AI-enabled workflows across the litigation lifecycle, from pleadings to discovery to hearing support. While many firms are still testing where AI fits in practice, we have been using these workflows extensively in construction litigation, where the scale and complexity of the record make the value especially clear. The objective is not to replace legal judgment. It is to help lawyers reach the right evidence sooner, reduce time spent on manual searching and review, and improve the quality and consistency of work in document-heavy disputes. 

What have we implemented, and what does it do?   

There is no single AI system doing all of the work. Instead, we use a combination of AI-enabled tools at different stages of a matter, which include:

• eDiscovery platforms with analytics, chat and AI-assisted review capabilities
• LLM's for drafting, synthesis, chronology preparation, legal research and general productivity
• Document translation, transcription and optical character recognition (OCR)
• Concept-based and natural language search capabilities
• Modelling for decision-support and outcome analysis
• Object detection and segmentation of images and other media

Just as important as the software itself is having a department with the technical proficiency and workflow discipline to apply those tools effectively, defensibly, and in alignment with legal practice. That is a capability we have been deliberately building within our eDiscovery & Litigation Support team.  

Early in a matter, AI-assisted document search helps us review broader record sets sooner and identify important records that may not surface through traditional methods alone. Those same workflows can support chronologies, targeted reviews for witness preparation and oral submissions, and more proportionate decisions about the timing and scope of collection. In practice, these workflows are integrated with traditional legal processes rather than used in isolation: lawyers and litigation support professionals frame the issues, test and refine the search or review approach, and then validate the results through targeted human review. In discovery, AI-assisted review narrows large record populations through an iterative process with quality control, sampling, and escalation of borderline calls, allowing for more efficient identification of the potentially producible record set while maintaining consistency and defensibility[1]. For questioning and hearing preparation, concept-based and natural-language searching allows lawyers to move beyond rigid Boolean logic and focus on the ideas, themes, and factual issues that actually matter. 

In some matters, modelling can also support analysis of possible outcomes or risk scenarios. That said, it is best understood as a decision-support tool rather than a prediction engine, and any such use must remain grounded in the evidence, the applicable law, and lawyer judgment.   

How does this create efficiencies?   

The biggest shift is earlier access to meaning. AI-enabled workflows allow us to review more of the record sooner, identify key documents earlier, and shape the theory of the case at an earlier stage. In traditional workflows, that fuller picture often emerges much later, sometimes months after pleadings are drafted.   

In construction disputes, where delay, causation, notice, and project communications are tightly intertwined, the relevant story is rarely contained only in a client’s own records. These workflows can also be applied to records received from other parties, allowing the litigation team to assess the dispute more fully from both sides. 

In discovery, AI workflows reduce the time spent reviewing irrelevant records and make it easier to build focused, issue-specific, and witness-specific subsets for questioning. At hearing, they improve responsiveness by allowing teams to search transcripts and supporting records when unexpected issues arise rapidly.  

How does this benefit clients?   

The client benefit is straightforward: better decisions earlier, and less money spent getting there. Construction litigation is expensive not only because the issues are complex, but also because the evidentiary record is often massive. Every inefficiency in document review, evidence identification, or hearing preparation adds cost. By improving access to the right evidence earlier in the process, AI-enabled workflows help counsel assess claims and defences sooner, plead more precisely, and avoid unnecessary time spent on blind review exercises. 

In some cases, the benefit is even more significant. A fuller early review of the evidence can confirm the right causes of action or reveal that the evidence does not support a proposed allegation at all. That can save substantial time and costs and, in some cases, may avoid unnecessary litigation altogether.  

Where has this been used successfully?   

The most useful examples are practical ones. In pleadings, earlier AI-assisted key document identification from a broader record set has improved the quality of the case theory advanced at the outset. In discovery, AI-assisted review has been used under tight timelines to help identify the potentially producible record set more efficiently while maintaining consistency. In questioning, concept-based searching has allowed teams to move beyond broad record requests and focus instead on the specific factual issues to be examined.  

At hearing, AI-assisted transcript review has also proven useful. In one arbitration, concept-based searching across a transcript database enabled the legal team to locate relevant passages in real time during closing submissions and respond directly to inaccurately represented evidence from the other side. That kind of support does not replace advocacy. It strengthens it.   

The Takeaway   

For document-heavy disputes, AI is no longer theoretical. It is already changing how litigation is conducted. Its value is that it helps lawyers get to the right evidence sooner, organize it more effectively, and make better decisions under tighter time and cost constraints. But those benefits depend on disciplined implementation. AI outputs still need validation, quality control, and legal judgment. Over-reliance, poor prompt design, or inadequate review can introduce risk, particularly where discovery decisions or evidentiary positions must later be defended. In construction litigation, where the record is often sprawling and the issues are intensely fact-driven, the strongest results come from integrating AI into a governed workflow that remains transparent, auditable, and lawyer-led.

Reach out to Steven LeBlanc-Gauthier, leader of our eDiscovery & Litigation Support team, to explore how BD&P can help find the right evidence faster, surface key facts sooner, and bring sharper insight to complex, document‑heavy disputes.

 
 
[1] “Legal AI Adoption Outpaces Expectations, Especially In-House” Canadian Lawyer (May 28, 2025), Canadian Lawyer article

[2] "Faster Reviews, Lower Costs: The Case for Law Firm and ALSP AI Partnerships" BD&P