Construction / 6 min read
As artificial intelligence (AI) embeds itself across construction workflows, litigation hold obligations must evolve beyond traditional records. Project teams are increasingly producing AI-generated artifacts—meeting bot transcripts, chat logs, prompt histories, and model outputs—that can be just as probative as RFIs, change orders, and emails. The defensible preservation of these materials requires both process and tooling that reflect how modern project data is created, shared, and transformed.
What is an AI-Generated Artifact?
AI-generated artifacts encompass a wide range of outputs created or influenced by artificial intelligence systems. Meeting assistants may record audio, produce transcripts, and generate summaries. Collaboration platforms capture chat histories and derive AI-generated action items. Generative models create narrative drafts, analyses, and recommendations, while predictive engines and simulations yield structured data, visualizations, and scenario evaluations. Across these categories, related metadata—such as timestamps, user or system identifiers, model version, system prompts, connectors used, and confidence scores—provides critical context for establishing authenticity, traceability, and proper interpretation.
Where do These Artifacts Live?
Construction teams encounter AI-generated content across many types of software and systems. The examples below show where this data may reside and what should be considered when defining the scope of a litigation hold.
- Construction Management Platforms. Systems such as Procore, Autodesk Build (formerly BIM 360), Trimble ProjectSight, and Newforma Konekt now include AI tools for search, summarization, and project insights, often linking to meeting assistants or third-party AI services. These may produce project summaries, draft logs, suggested actions, and AI-assisted document searches.
- Design, Coordination, and BIM Environments. Platforms like Autodesk Revit, Navisworks, Trimble Connect, ProjectWise, and BIMcloud generate model data, clash reports, and AI-assisted issue detection. Some now incorporate predictive analytics and rule-based quality tools such as Bricsys 24/7 or BIM Track. Cloud-based collaboration and model coordination tools can generate AI-based issue tagging and predictive quality or risk insights. Model exports, snapshots, and AI-labeled issues should be preserved with their version, timestamp, and author information.
- Field Reporting and Safety Tools. Applications such as PlanGrid, Raken, Fieldwire, OpenSpace, and Buildots apply computer vision and AI to identify defects, tag photos, and auto-generate inspection summaries. Annotated images, AI-flagged conditions, and auto-generated summaries (with confidence scores) are all candidates for preservation.
- Meeting and Transcription Tools. Common systems include Microsoft Teams, Zoom, Google Meet, Otter.ai, Fathom, and Fireflies.ai. Many integrate directly with project management software and generate transcripts, summaries, or AI-identified action items. In addition, video conferencing platforms and recording or transcription services create audio/video files. Meeting bots within collaboration platforms also generate structured follow-ups—all potentially within a hold’s scope.
- Team Messaging and Collaboration Hubs. AI-enhanced team communication platforms such as Slack, Microsoft Teams, and Google Chat can produce persistent chat histories, summaries, and automated task extractions—each potentially within the scope of preservation.
- Knowledge Management and Document Intelligence. Systems like Notion, Miro, SharePoint, Kahua, Kira Systems, and Microsoft Copilot assist with summarizing manuals, contracts, and specifications. When retrieval-augmented generation (RAG) features are used, prompt histories and source linkages become part of the record. These services produce retrieval traces, summaries, links between prompts, sources, and outputs all of which are subject to being retained.
- Document and Records Management Systems. Platforms such as OpenText, iManage, M-Files, and Microsoft Purview serve as primary repositories for documents created or processed through AI tools, along with their logs and metadata. Preservation measures should extend to the original systems where data is created.
- eDiscovery and Document Review Platforms. Some construction companies are bringing eDiscovery tools in-house. Platforms such as Relativity, Everlaw, Reveal and others now include an AI component. Prompts, search terms, searches, and associated output may be saved in the program’s history. This data may or may not be privileged and preservation measures should be implemented.
- Custom or Proprietary Platforms. Large contractors may operate internal data lakes (centralized repositories for large volumes of raw project data), analytics dashboards (interactive tools that visualize performance and trends), or digital twin systems (virtual models that mirror real-world assets in real time). These environments often use custom AI models to generate forecasts, risk scores, simulations, and decision logs. Understanding where the underlying prompts, outputs, and model versions are stored is essential for accurate data mapping and preservation.
- Financial, Bidding, & ERP Systems. Examples include Viewpoint, CMiC, SAP, Oracle; bidding platforms (e.g., BuildingConnected); and AI-powered invoice tools. Typical AI outputs comprise AI-generated cost forecasts, bid analysis reports, risk scores for change orders, payment anomaly flags, and automated invoice processing logs. Key risks include “black box” AI logic where outputs (e.g., a “risk score”) may lack accessible underlying data, and highly sensitive, siloed financial data. Preservation guidance: preserve system-wide audit logs; export AI-generated reports together with the specific data inputs used at the time; and document AI model versions used for forecasting.
- IoT, Telematics, & Sensor Platforms. Examples include equipment-native telematics (Cat, John Deere), SmartRock for concrete, Triax for worker safety, and environmental/vibration sensors. Typical AI outputs include AI-generated alerts (e.g., geofence triggers, worker fall detection, unsafe vibration), equipment health forecasts, concrete curing predictions, and productivity analyses. Key risks involve massive data volumes, separation of raw sensor feeds from AI-generated “insights” or “events,” and data residing on third-party vendor platforms. Preservation guidance: archive AI-generated alerts along with the associated raw sensor data streams covering the relevant time window; and export event logs with timestamps, device IDs, and sensor readings.
- Autonomous Systems & Robotics. Examples include drone mapping platforms (e.g., DroneDeploy, Pix4D), autonomous survey robots, delivery drones, and robotic excavators/haulers. Typical AI outputs encompass AI-generated 3D models and maps, automated “percent complete” calculations, obstacle detection logs, and autonomous path/decision logs. Key risks include highly proprietary data formats, data stored across on-device storage, controllers, and cloud platforms, and potentially ephemeral AI decision logs. Preservation guidance: secure and image on-device storage; export all cloud-based project data (e.g., flight logs, maps, analysis reports); and preserve all operational and AI decision logs.
Special Considerations for Custom Systems
Larger and more sophisticated construction firms often use their own internal data systems rather than off-the-shelf software. In these setups, AI-generated materials may be spread across many components—such as log files, messaging tools, and model tracking systems—instead of being stored in one location. Preservation planning should recognize these realities: some AI data (like prompts, responses, and logs) may be temporary, automatically overwritten, or dispersed across multiple databases. Clear policies for retaining this information—including audit trails for model inputs, outputs, and configuration settings—are essential to show the accuracy and integrity of what is preserved.
Implementing Defensible Preservation
A defensible preservation process for AI-generated artifacts depends on defining the scope, enforcing the hold, and verifying results.
- Scope: Clearly identify what types of AI materials are covered—such as meeting transcripts, chat logs, prompt histories, retrieval records, model outputs, and related metadata.
- Enforcement: Ensure that system owners and custodians are informed that automated deletion or overwriting is paused, and that—where possible—preservation is automated through APIs or connectors.
- Verification: Maintain a record of actions taken (a “chain of custody”), perform periodic completeness checks, and confirm that preserved files remain accessible and can be exported in standard formats.
The Bottom Line
AI-generated materials are now core project records. Treat meeting bot transcripts, chat logs, prompt histories, and model outputs with the same rigor applied to contracts and RFIs. Whether operating on commercial platforms or bespoke systems, the defensible path is to define scope clearly, preserve artifacts with their metadata, and verify that what is held can be produced reliably. Firms that modernize their litigation hold processes to account for AI will be better positioned to manage risk and maintain credibility when disputes arise.