By:Jeff Johnson, Chief Innovation Officer, Purpose Legal
If you read, watch, and listen to industry content, you might think everyone is using AI all the time. Some certainly are. We work with many of them.
There are still many skeptics. I understand why.
Some tools overpromise.
Some outputs are imperfect.
Some lawyers worry about hallucinations.
Others worry about jobs.
Many worry about defensibility.
These concerns are healthy.
The real question is not whether AI can generate insightful information. It can.
The real question is whether we can operationalize it so that we use it responsibly, transparently, and defensibly in Early Case Intelligence (ECI) workflows and generate better outcomes.
AI Is Not a Push-Button Decision Maker
One of the most persistent misconceptions is that Early Case Intelligence tools replace human judgment. They do not.
In practice, responsible ECI workflows begin with structured input. That 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.
The AI tools we use can certainly reduce this effort – sometimes greatly. Even so, a simple upload and “press go” approach is rarely sufficient.
Effective teams:
- Refine the case overview
- Clarify scope and objectives
- Adjust prompts to align with what matters in the analysis at hand
- 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. That is not a technology flaw. It is a reminder that process matters.
Calibration Is Not Optional
Before running analysis across an entire data set, disciplined workflows generally include calibration (just as an effective search term development process does).
With an AI-enabled ECI workflow, that 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 should feel familiar.
For years, we have applied similar discipline in TAR and CAL workflows (ideally even search terms). We trained models. We evaluated precision and recall. We refined. We validated.
The tools are different. The obligation to test is not.
One of the most common mistakes I see is assuming that a “not relevant” classification means “safe to ignore.” Even strong systems misclassify documents. Responsible use requires measuring what AI places in that bucket before you set it aside.
Calibration is not about distrust. It is about control and preventing problems when we ultimately validate any reliance on classifications in subsequent review and production.
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. Sampling across other categories helps assess 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. That principle does not change simply because we use generative AI.
If anything, documentation becomes more important.
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?
Those are answerable questions when a structured process is in place.
Transparency and Cooperation Still Matter
As AI becomes integrated into early case workflows, cooperation matters.
If documents are being 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 search term negotiations or TAR protocol discussions. They simply involve new tools.
Responsible adoption means anticipating those conversations rather than avoiding them.
Human Judgment Becomes More Important, Not Less
AI does not understand settlement posture.
It does not instinctively know risk tolerance.
It does not appreciate what will resonate with a judge or regulator.
Human reviewers and litigation teams provide that context.
In fact, as AI tools become more capable, the value of experienced oversight increases. Understanding where a system’s weaknesses lie allows teams to design safeguards around them.
The goal is not automation for its own sake.
The goal is disciplined acceleration toward better outcomes.
The Standard Has Not Changed
Technology evolves. The standard does not.
Reasonableness.
Proportionality.
Transparency.
Defensibility.
Those principles apply whether an early case strategy relies on search terms or AI.
Used casually, AI introduces risk.
Used with structured oversight, calibration, and validation, it becomes another defensible tool in the litigation toolbox.
The difference is not technology.
The difference is the process around it.
Click here for more information about Purpose Legal’s ECI solution, PurposeXi CaseOptics or contact us to schedule a demo.
