Insights Articles

Why Chat Data Changes the Review Equation 

Traditional document review workflows were designed for emails, PDFs, and static files. Standalone documents and email families where the information necessary to complete a review was for the most part self-contained. Chat data breaks those assumptions.

Messages are short and conversational. Meaning is often spread across multiple participants, threads, and time periods. Key evidence may hinge on tone, sequence, or reactions—not just keywords. A single message taken out of context can be misleading, while an entire thread may tell a very different story.

For legal professionals, this creates a real-world problem: chat data must be reviewed thoroughly, but traditional linear review is slow, expensive, and ill-suited to the format.

This is where AI enters the conversation—and where skepticism often follows.

The Skepticism Around AI in Chat Data Review

Legal teams have good reasons to be cautious. Concerns commonly include:

• • • • • Loss of context when AI summarizes or classifies messages
• Overreliance on automation at the expense of legal judgment
• Unclear prompts or black-box decision-making
• Inaccurate or “hallucinated” summaries

These concerns are valid—but they don’t mean AI has no place in chat data review. They mean AI must be applied with precision, transparency, and strong workflow design.

What AI Can (and Should) Do for Chat Data Review

When implemented correctly, AI can support chat data review without undermining control.

1. Preserve conversational context

Modern AI tools can analyze chat data in threads and sequences rather than isolated messages. This allows reviewers to see how conversations evolve, who participates, and how decisions or intent develop over time—something keyword searches struggle to accomplish.

2. Accelerate issue identification

AI can help surface patterns across large chat datasets, flagging conversations related to specific issues, events, or timeframes. This is particularly valuable in internal investigations, employment matters, and regulatory inquiries where speed matters.

3. Reduce review volume without replacing reviewers

Rather than replacing human reviewers, AI helps prioritize what needs attention first. Legal teams remain in control of final determinations, while AI reduces the noise that slows review.

The Critical Role of AI Prompts

One of the most misunderstood aspects of AI adoption in eDiscovery is the role of AI prompts. Prompts are not casual questions—they are instructions that shape how AI evaluates data.

Poorly designed prompts can oversimplify conversations or miss nuance. Well-designed prompts, crafted with legal objectives in mind, can help AI:

• Identify intent versus casual commentary
• Distinguish operational chatter from substantive discussions
• Highlight decision-making moments within long threads

The key is that prompts should reflect legal standards and case strategy, not generic summaries. This is where collaboration between legal teams and experienced litigation support partners becomes essential.

Maintaining Control Through the Legal Workflow 

AI review assistance should never exist outside the legal workflow—it should be embedded within it.

A defensible AI-driven chat data review process includes:

• Clear documentation of how AI is used
• Defined checkpoints for human review and validation
• Quality control measures and sampling with defensible accuracy, precision and recall rates
• Transparent decision-making that can be explained to courts or regulators

When AI operates as a decision-support tool rather than an autonomous decision-maker, legal teams retain control while benefiting from efficiency gains.

For skeptical adopters, this distinction matters. AI is not being asked to practice law. It is being used to organize, prioritize, and analyze data so legal professionals can apply their expertise more effectively.

Bridging the Gap Between Reality and Readiness

Slack and Teams data are increasingly appearing in litigation and investigations. Avoiding AI does not eliminate the challenge—it often makes it harder and more expensive to address.

The real opportunity lies in bridging real-world data sources with disciplined adoption. That means choosing tools and partners that understand both the technology and the legal risk landscape.

At Array, AI-enabled chat data review is integrated into a broader, end-to-end legal workflow that helps legal teams work smarter and more efficiently. By combining predictive coding, Continuous Active Learning (CAL), generative AI, and attorney oversight, we create review workflows that adapt to each matter’s priorities, whether that’s accelerating timelines, controlling costs, or managing legal risk. Teams retain full control over how much AI is applied, ensuring that human judgment remains central to key decisions.

Our approach blends advanced technology with deep litigation support expertise, allowing legal teams to analyze complex chat data efficiently while maintaining defensibility, preserving context, and gaining confidence in every step of the process.

Final Thoughts

AI is not a shortcut around careful review—especially when it comes to chat data. But when used thoughtfully, it can transform one of eDiscovery’s most challenging data types into a manageable, defensible part of the litigation process.

For legal leaders navigating growing data volumes and increasing scrutiny, the goal isn’t blind adoption or total avoidance. It’s controlled innovation—using AI to analyze chat data without losing the context, control, or confidence that legal work demands.