ECI Defensibility Is About Process – Not Technology
If you read, watch, and listen to industry content, you might think everyone is using AI all the time. Some certainly are, and many organizations work with these advanced tools. However, there are still many skeptics, and it’s important to understand their concerns.
Understanding the Skepticism
Some tools overpromise, and some outputs are imperfect. Many lawyers express worries about hallucinations produced by AI, while others are concerned about job security. Furthermore, defensibility remains a significant worry. These concerns are healthy and should not be dismissed.
The Real Question
The real question is not whether AI can generate insightful information—it can. The critical inquiry is whether we can operationalize it responsibly, transparently, and defensibly in Early Case Intelligence (ECI) workflows to generate better outcomes.
AI Is Not a Push-Button Decision Maker
One of the most persistent misconceptions is that ECI tools replace human judgment. They do not. Responsible ECI workflows begin with structured input, which may include a complaint, a request for production, investigative materials, or a carefully drafted matter overview. The quality of that input directly affects the quality of the output.
While AI tools can reduce effort—sometimes significantly—a simple upload and “press go” approach is rarely sufficient.
Effective Teams
Effective teams:
- Refine the case overview
- Clarify scope and objectives
- Adjust prompts to align with what matters in the analysis
- Ensure the system has complete and relevant context
AI provides a starting point, not the final draft. If the input is careless, the output will be unreliable. This is not a technology flaw; it is a reminder that process matters.
Calibration Is Not Optional
Before running analysis across an entire dataset, disciplined workflows should include calibration. This means:
- Running analysis on a thoughtfully selected subset of documents
- Reviewing results across relevance categories
- Identifying false positives and false negatives
- Adjusting the input as needed
- Re-testing before scaling
This structured approach should feel familiar. For years, similar discipline has been applied in TAR and CAL workflows. The tools may be different, but the obligation to test is not.
Common Mistakes
A common mistake is assuming that a “not relevant” classification means it’s safe to ignore. Even strong systems misclassify documents, and responsible use requires measuring what AI places in that bucket before setting it aside.
Validation Requires Structure
Validation is more than spot-checking. When ECI technology triages documents into relevance classifications, statistically meaningful sampling becomes critical. Sampling from the “not relevant” bucket helps measure what the system may be missing, while sampling across other categories assesses consistency and alignment with expectations.
In higher-risk matters, multiple validation checkpoints are appropriate. Courts have long accepted reasonable, defensible processes when parties demonstrate diligence and transparency. This principle does not change with the introduction of generative AI; if anything, documentation becomes even more important.
Key Documentation Questions
When a structured process is in place, the following questions become answerable:
- What was the input?
- What subset was tested?
- What adjustments were made?
- What sampling thresholds were applied?
- What estimates can we make based on that sampling and review?
Transparency and Cooperation Still Matter
As AI becomes integrated into early case workflows, cooperation is paramount. If documents are culled or prioritized using AI-generated analysis, parties may need to discuss:
- Construction of case overviews
- Validation methodology
- Documentation sharing
These conversations are not fundamentally different from traditional search term negotiations or TAR protocol discussions; they simply involve new tools.
Human Judgment Becomes More Important, Not Less
AI does not understand settlement posture, risk tolerance, or what resonates with a judge or regulator. Human reviewers and litigation teams provide that essential context. As AI tools become more capable, the value of experienced oversight increases.
Understanding a system’s weaknesses allows teams to design safeguards around them. The goal is not automation for its own sake, but disciplined acceleration toward better outcomes.
The Standard Has Not Changed
As technology evolves, the standard does not. The principles of reasonableness, proportionality, transparency, and defensibility still apply, whether an early case strategy relies on search terms or AI.
Used casually, AI introduces risk. However, when used with structured oversight, calibration, and validation, it becomes another defensible tool in the litigation toolbox. The difference lies not in the technology, but in the process around it.
