AI is quickly becoming part of the eDiscovery conversation, but speed alone is not enough. For in-house counsel, the real priority is knowing whether the data, workflow, and review process can hold up under scrutiny.
1. Start with the Legal Question, Not the Tool
AI should be tied to a specific litigation need. Is the goal to reduce review volume? Find key communications faster? Identify privileged material? Understand a messy data set? Each goal requires a different workflow.
In-house counsel should be wary of starting with a broad instruction like “use AI on this case.” A better approach is to define the problem first. For example, a team may need to isolate communications from a specific custodian, compare contract language across thousands of files, or prioritize review around a narrow set of issues. The more specific the legal objective, the more useful the AI workflow can be.
2. Know What Data Is Being Fed Into the Process
AI cannot fix a weak collection strategy. Before the review begins, in-house counsel should understand what data sources are involved and whether they were collected properly.
That may include email, laptops, shared drives, Teams, Slack, OneDrive, text messages, cloud repositories, or mobile devices. Each source has its own format, metadata, access issues, and preservation concerns. If important data is missing, altered, exported incorrectly, or collected without a clear chain of custody, the review process may be vulnerable before AI is ever applied.
3. Ask How the Workflow Will Be Explained
AI in eDiscovery must be more than fast. It must be explainable.
In-house counsel should ask how the team will document the process, including what tools were used, what decisions were made, how documents were prioritized, and how quality control was handled. This does not mean every technical detail needs to be included in a motion or production letter. It does mean the team should be able to explain the workflow in plain terms if opposing counsel, a court, or internal stakeholders ask questions later.
4. Keep Control of Sensitive Corporate Data
AI-supported eDiscovery often involves large volumes of internal business records, employee communications, contracts, emails, and cloud-based files. In-house counsel should know where that data is hosted, who can access it, how it is secured, and how the review process protects confidential information.
Before using AI, legal teams should confirm that the workflow supports controlled access, clear permissions, and careful handling of sensitive material. Speed is useful, but corporate legal departments also need confidence that their information is being managed securely and responsibly.
5. Do Not Separate AI from Human Review
AI can help legal teams move through information faster, but attorneys and experienced eDiscovery professionals still need to guide the process. Human review is especially important when documents involve legal nuance, privilege, intent, unusual terminology, or sensitive internal communications.
The strongest workflows use AI to reduce noise and surface patterns while relying on people to make judgment calls. In-house counsel should look for a process in which technology supports the review strategy rather than replaces it.
Choose a Partner That Understands the Full Discovery Lifecycle
AI is only one part of eDiscovery. A defensible process also includes collection, processing, hosting, review support, production, and documentation. When these steps are handled separately or inconsistently, issues can appear late, leaving less time to fix them.
Parcels helps legal teams bring these pieces together. With eDiscovery, forensic data collection, data analytics, Relativity support, and full-service outsourced legal support, Parcels works with law firms and corporate legal departments to make complex data easier to manage and review. Contact Parcels today to discuss how our team can support your next eDiscovery matter.
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