Delays are part and parcel of construction projects. But when things like late approvals, unexpected site issues, or supply chain problems slow things down, contractors face more than just missed deadlines. They have to reschedule teams, deal with idle equipment, push back other commitments, and often struggle with cash flow.

To deal with such delays, contractors usually submit an Extension of Time (EoT) claim — a formal request to extend the project completion date due to events beyond their control. But preparing an EoT claim is no small task. It involves digging through daily site reports, matching them with baseline schedules, pulling out relevant contract clauses, and putting everything together in a clear and convincing way.

It’s detailed, time-consuming, and often stressful, especially when deadlines are tight.

That’s where Artificial Intelligence (AI) is starting to make a real difference. AI tools can now help track delays, connect them to schedules, scan documents for evidence, and even draft parts of the claim, saving time and reducing human error.

In this article, we’ll look at how AI is changing the way EoT claims are prepared and submitted, and how it’s helping contractors and consultants work faster and smarter.

How is AI Reshaping Every Stage of the EoT process?

AI is no longer just a futuristic add-on, but it is actively transforming how Extension of Time (EoT) claims are drafted and handled. From identifying delays early to drafting well-supported submissions, AI is streamlining each stage of the EoT process, making it faster, more accurate, and far less manual.

1: AI-Powered Delay Analysis:

Delay analysis is the major aspect of any EoT claim. Every delay analysis report majorly answers two questions: what caused the delay? and who is responsible?

While these are just two questions, their answers require deep expertise in programming logic, documentation, and correlation of multiple data streams. Even the most seasoned delay analysis experts often spend weeks (if not months) manually analyzing critical paths and float erosion. They use traditional methods like As-planned vs. as-build analysis, Impacted as-planned, Time impact analysis (TIA), and more to identify the gaps, and reach an accurate conclusion.

However, AI uses schedule-analysing engines that are capable of reading and analyzing multiple documents to generate a comprehensive summary. When combined with machine learning models trained on past project delay cases, AI can even suggest probable causes of delay based on pattern recognition, saving time, and energy.

AI’s schedule-analyzing engines can:

• Automatically map critical paths and track how they evolve
• Pinpoint exact moments where progress deviated
  
  

2: Structuring and Drafting EoT Claims:

Once a delay has been identified and causation established, the next step is writing the actual EoT claim. An effective EoT claim requires:

• Clear narrative
• Referencing of relevant clauses (e.g., under FIDIC 1999 Clause 20.1 or 2017 Clause 20.2)
• Timeline of events
• Supporting evidence
• Impacted schedule logic
• New completion date
  
  

This is where many EoT claims become vulnerable — not due to lack of effort, but because early delay events go undocumented, records are scattered, or the link between cause and impact isn’t clearly established. Even experienced teams can struggle when the process is manual and reactive. Without structured delay analysis or a well-written narrative, even valid claims get rejected. 

Now comes Artificial Intelligence. AI language tools can assist teams in drafting coherent claims using pre-trained templates and claim frameworks. While human review is required, AI drastically reduces the time spent on drafting and ensures that the language remains aligned with the contractual tone.

Even firms like Masin approach EoT claims with a perfect blend of AI-supporting drafting with human-led validation. This balance helps ensure that the submitted EoT claims are accurate, persuasive, and backed by evidence.

3: Automating Document Management And Evidence Collation

One of the biggest pieces of evidence in EoT claims is the supporting documents – progress reports, site diaries, RFIs, drawings, photographs, videos, email conversations, variation instructions, and much more. And, in traditional construction projects, no one cares to maintain them, and they can be scattered across folders, emails, SharePoint, hard drives, etc. Therefore, gathering and organizing this evidence can be a nightmare.

However,  Artificial Intelligence is now solving this. The AI-powered model trained on construction-specific data to assist in generating structured and substantiated Extension of Time (EoT) reports. By analyzing contracts, schedules, and supporting documentation, the model helps contractors prepare stronger, more defensible EoT letters. To ensure accuracy, users need to upload all relevant documents — including baseline programs, progress updates, correspondence, and site records — so the system can extract, organize, and connect the right pieces of evidence to each delay event.

4: Better Communication with Employers and Consultants

Clear and timely communication is often the difference between a smooth extension of time process and a full-blown dispute. Contractors are typically required to notify the employer or consultant:

• That a delay has occurred
• The reason for the delay
• The expected impact on completion
• The expected timeline

However, in the rush of day-to-day site operations, these notices are either delayed, incomplete, or poorly structured.

But, AI is now changing the game. With prompt-driven tools and automated templates, teams can quickly generate:

• Well-drafted early delay notices
• Letters  under clauses like clause FIDIC 20.1 (FIDIC 1999)  or clause 20.2 (2017)
• Follow-up communication requesting the Engineer’s determination

 These tools help ensure the communication is contractually compliant, factually accurate, and sent on time. Some platforms even track upcoming notice deadlines and flag delays before they escalate.

By reducing the friction in day-to-day communication and ensuring consistency in messaging, AI helps contractors maintain transparency, avoid procedural pitfalls, and foster trust with consultants and employers—long before a formal claim is ever submitted.

5: Enhanced Transparency in Dispute Resolution:

In disputes, clarity isn’t just helpful—it’s essential. 

One of the biggest issues in resolving EoT claims is inconsistency across schedules, reports, and correspondence. When timelines don’t match documents or when delay narratives feel disconnected from the evidence, trust quickly breaks down.

AI helps bring transparency by aligning the data. It can produce audit trails linking each delay to source documents.

This data-backed narrative gives all parties, that is contractor, employer, consultant, and tribunal, a shared understanding of what actually happened.

For expert consultants like Masin, this clarity not only strengthens the credibility of the claim but also reduces the room for debate over facts, keeping the focus on resolution instead of argument.

Challenges of Using AI in EOT Claims:

While AI brings speed and structure to EoT claims, it’s not without its limitations. Over-reliance or poor implementation of AI in these claims can lead to gaps, inaccuracies, or even rejection of claims. Here are some of the challenges that one can face while using AI for EoT claims:

• Data Dependency: AI is only as good as the data fed into it, which means missing or inaccurate records can lead to flawed conclusions.
• Lack of Context: AI may misinterpret contractual nuances or project-specific conditions that require human judgment.
• False Confidence: Auto-generated drafts may appear polished but still lack legal or contractual strength without expert review.
• Overlooking Human Insight: Important site dynamics or verbal instructions can be lost if teams rely solely on automated analysis.
• Privacy and Security: Using cloud-based tools requires careful handling of confidential project data and access permissions.
• Limited Adaptability: AI tools trained on standard contracts may struggle with bespoke clauses or unique project structures.
  
  

Therefore, it is suggested to use the hybrid approach. In the approach, use AI for speed, structure, and insight, paired with human expertise to ensure accuracy, judgment, and credibility.

This is how firms like Masin approach claims work: combining AI-driven insights with a strong team of delay experts, project planners, and claim professionals who know how to frame a claim that holds up under scrutiny.

What the Future Holds for AI in EoT Claims?

AI’s role in Extension of Time (EoT) claims is still evolving, but the direction is clear. 

As AI tools become more advanced and better integrated with project systems, their ability to handle Extension of Time (EoT) claims will move beyond automation into real-time decision support. The future is not just about saving time, it’s about raising the overall standard of how claims are identified, managed, and resolved.

What’s ahead?

• Smart Delay Alerts: AI will proactively flag potential delays based on real-time data helping contractors act before time is lost.
• Instant Claim Drafts: AI tools will auto-generate draft claims triggered by site events, variations, or missed milestones
• Smart Contract Review Assistants: AI will scan contracts and auto-highlight time-related risk clauses, helping teams avoid disputes before they arise.
• Tribunal-Ready Claim Packs: Automated tools will package claims with linked evidence, delay logic, and visual timelines, ready for dispute resolution.
• Continuous Learning Engines: AI models will get smarter over time, learning from accepted/rejected claims and adapting to contract-specific language and expectations.
  
  

Conclusion:

Extension of Time (EoT) claims have always been complex, demanding careful analysis, solid documentation, and clear communication. With AI, the process is becoming faster, more structured, and easier to manage. From identifying delays early to preparing detailed claim submissions, AI is transforming how contractors and consultants approach every stage of the EoT journey.

As the construction industry continues to evolve, the companies that will embrace this shift early by building digital workflows, training their teams, and using AI tools wisely, will not only reduce claim risks but also improve their relationships with clients and consultants. Firms like Masin are already applying AI capabilities across complex infrastructure projects, contractors and stakeholders and are better equipped to manage EoT claims with greater accuracy, efficiency, and confidence.

In a world where time really is money, small claim management powered by AI may well become the new industry standard.