The Rise of Impact Discovery: From Buzzword to Results

In an EDRM article, Sheila Grela writes that rather than just a buzzword, “impact discovery” is about separating real innovation from empty hype. It asks a critical question: Which technologies actually change outcomes, reducing risk, saving time, improving quality, and strengthening defensibility?

Generative AI is leading much of the charge, particularly in summarizing custodian data and drafting privilege logs. But speed without oversight can backfire. According to Grela, the key lies in striking a balance, establishing guardrails, maintaining transparency, and ensuring validation to ensure that generative AI enhances discovery rather than undermining it. Meanwhile, hybrid models that combine knowledge graphs with large language models are emerging as a smarter, more explainable approach. They make it easier to answer the all-important “why” behind a document’s inclusion or exclusion.

The transparency theme runs deep. Explainability has become a defining feature of credible AI in legal contexts.  AI systems that reveal their reasoning processes are fostering greater confidence among attorneys, helping them in defensible decisions. Workflow automation, too, continues to prove its worth by eliminating repetitive tasks like Bates stamping, confidentiality tags, or quality checks.

True innovation also depends on who’s in the room. Participatory design and collaboration in the development of tools result in systems that align more closely with real-world needs. Cloud-based continuous review platforms extend this collaboration by enabling dynamic, real-time access to data across teams and locations.

Looking ahead, legal teams face three key challenges: adopting tools to handle emerging data types, such as chat apps, disappearing messages, and video; ensuring user adoption through trust and training; and maintaining defensibility in light of evolving regulations. True impact discovery will depend not on chasing every innovation but on future-proofing workflows by systematically testing, measuring, and scaling what works.