Sources
1 KPMG internal metrics
2 KPMG internal metrics
Internal investigations have long been defined by the wait between question and answer. In our ongoing series on the Future of eDiscovery, we have tracked the move upstream toward early case assessment and the embedding of trusted AI across the discovery lifecycle. That shift is especially powerful in investigations, where generative AI is closing the gap, turning reactive scrambles into controlled, iterative, and increasingly self-service investigations.
Traditional investigations involving email, communications, and documents run on legwork. A typical matter involves multiple teams that collect the data, cull the data, negotiate keywords, and ship documents to an outside review team—then wait sometimes days or weeks for a memo. Every new question can restart the loop. For allegations that need triage in hours, or cross-border matters spanning multiple languages, that linear model cannot keep pace with modern data volumes or urgent demands.
Our Relativity aiR Investigations approach, built on RelativityOne and its aiR Assist capability, replaces that loop with a conversation. An investigator asks the corpus a question in plain language and receives a cited, source-linked answer in minutes—then drills in, refines, and asks the next question in the same session. Legal, compliance, and HR teams can drive it directly, while KPMG LLP manages the environment, handles collection and processing, and provides prompt engineering, quality control, and validation.
Answers arrive in minutes, not weeks; investigations run on demand, without an outsourced review cycle; and every answer is cited back to source documents—so conclusions can be traced, tested, and produced.
The impact is easiest to see in the numbers. In one recent engagement, aiR Assist helped filter roughly 724,000 documents down to 31 critical findings over a single weekend.1 In a separate multilingual investigation, approximately 50,000 documents across multiple languages were assessed in days rather than weeks.2 Both matters drew on the same workspace, where three aiR tools operate in a single workflow: Assist for conversational, cited interrogation of a document set; Review for responsiveness and privilege calls; and Case Strategy for narrative development.
This approach spans the investigations that most often arrive without warning:
| Investigation type | AI application & impact |
| Whistleblower & hotline triage | Test allegations against evidence in hours to decide whether to escalate, scope, or close. |
| Bribery & corruption | Rapidly assess exposure under the FCPA and UK Bribery Act. |
| HR & misconduct | Investigate workplace issues and undisclosed conflicts of interest quickly and discreetly. |
| IP & data loss | Track trade secret theft and IP loss by departing employees. |
| Market abuse | Quickly surface evidence related to insider trading, sanctions, and export controls. |
| M&A disputes | Uncover critical facts for both pre-close and post-close M&A disputes. |
The output feeds directly into the work product that matters—interview preparation, fact chronologies, early case assessments, source-linked narratives, and plain-language briefings for executives.
As you evaluate your organization's readiness for AI-driven investigations, keep these strategic imperatives in mind:
| Consideration | Recommended action |
| Rethink the clock | Revisit the timelines baked into your protocols. If a credible read is available in minutes, scoping decisions shouldn't wait on a full review. |
| Keep the human in the loop | Build practitioner review, sampling, and audit trails into the active workflow—not the post-mortem—to help ensure defensibility. |
| Insist on citations | Treat source-linking as a hard requirement. An answer you cannot trace to a source document is not an answer you can produce. |
| Match the model to the matter | Use conversational interrogation for triage, and pair it with structured traditional review standards as the matter matures toward litigation. |
This new operating model isn’t just a technology upgrade. Instead, it’s a strategic shift that empowers legal teams to become more agile, secure, and drive significant cost savings while unlocking the innovative potential of AI.
KPMG has designed a strategic approach to quickly assess your eDiscovery program's readiness – including technology requirements evaluation, financial modeling, and roadmap / business case development.
Contact us to learn more about how we can help inform your eDiscovery journey.
1 KPMG internal metrics
2 KPMG internal metrics