[EDRM’s Editor’s Note: This article was first published here on May 12, 2024, and EDRM is grateful to Rob Robinson, editor and managing director of Trusted Partner ComplexDiscovery, for permission to republish.]
ComplexDiscovery’s Editor’s Note: The exponential growth in electronically stored information (ESI) has created immense eDiscovery challenges for organizations. Effective information governance and eDiscovery capabilities are crucial to managing legal risk and obligations. This content outlines key eDiscovery processes – from initial case assessment through defensible disposition of data – to inform discussions around developing comprehensive workflows aligned with organizational needs and resources. A thoughtful approach enables legal teams to control costs, avoid sanctions, leverage ESI for facts, and meet discovery duties. As data volumes and litigation continue rising, a sound eDiscovery framework is essential. This article has been republished with updates from earlier editions to reflect the latest eDiscovery technology and practice developments.
The exponential growth in electronically stored information (ESI) has created immense eDiscovery challenges for organizations.
Rob Robinson, Editor and Managing Director of ComplexDiscovery.
The Workstream of eDiscovery
From the trigger point for audits, investigations, and litigation to the conclusion of cases and matters with the defensible disposition of data, the myriad approaches data discovery and legal discovery professionals use to administer eDiscovery are as diverse as they are complex. Leveraging research aggregated from leading eDiscovery educators, developers, and providers, the eDiscovery Processes and Tasks listing provided herein serves as a strategic planning tool, guiding business and technology discussions and decisions concerning cybersecurity, information governance, and legal discovery-related eDiscovery projects. It’s important to note that the processes and tasks outlined in this document are not exhaustive but represent a singular perspective on the discipline of eDiscovery.
eDiscovery Processes and Tasks Checklist
eDiscovery Processes and Tasks Through the Lens of Generative Artificial Intelligence
This update also includes subjective assessments of Generative AI (GAI) productivity enhancements, aiming to provide a model for considering the holistic impact of GAI on eDiscovery tasks and processes over the next 12 months. These evaluations offer potential insights into productivity gains that can be achieved through the strategic application of GAI technologies.
GAI Enablement Model Background Notes:
- Categories: All 102 tasks are categorized into collection, processing, or review workstreams.
- Task Numbers: Each task in the workflow is assigned a specific tracking number.
- Process: Eleven processes are identified as subcategory organization areas for all tasks.
- Tasks: Detailed descriptions of specific workflow tasks for each process.
- GAI Enablement Rating: A subjective rating assessing the potential near-term (<12 months) productivity impact of GAI on specific tasks, ranging from 0 (No AI Enhancement) to 3 (AI LLM and Prompt Engineer Overview Enhancement).
- GAI Productivity Multiplier: Estimates of the short-term (<12 months) productivity impact of GAI on specific tasks, measured as Level 0 (0% increase in productivity), Level 1 (15% increase in productivity), and Level 2 (30% increase in productivity).
- Industry % of eDiscovery Spend: Estimated industry spend on specified categories. Provided as context to potential GAI impact.
- Industry eDiscovery Spend in 2024: Estimated industry spend on specified categories. Provided as context to potential GAI impact.
By integrating these GAI productivity assessments, this revised listing underscores the transformative potential of AI technologies within the eDiscovery field. It serves as a foundational guide for legal and IT professionals to explore and evaluate how AI can be effectively integrated to enhance operational efficiencies and meet strategic goals in a rapidly evolving digital landscape.
eDiscovery Processes and Tasks Checklist – AI Enablement Model
It’s important to emphasize that while this framework serves only as a model, it might help structure thought about the economics and efficiency of GAI-enabled eDiscovery.
Read the original release here.
News Sources
- EDRM – The Electronic Discovery Reference Model
- e-Discovery Team – Ralph Losey
- Defining Cyber Discovery? A Definition and Framework
- Buyers Guide Archive – Andrew Haslam’s eDisclosure Systems Buyers Guide
- Artificial Intelligence Cybersecurity Challenges — ENISA
- Continuous Active Learning for TAR (PDF)
- Even FLOE? A Strategic Framework for Considering AI in eDiscovery
Additional Reading
- A Running List: Top 100+ eDiscovery Providers
- The Next Five Years in eDiscovery: Market Size Forecast for 2023-2028
Source: ComplexDiscovery OÜ
Assisted by GAI and LLM Technologies per EDRM GAI and LLM Policy.
