How AI can transform internal corporate investigations

As investigations grow more complex, organizations are turning to AI to improve speed, consistency and defensibility.

In brief

• Internal corporate investigations now span massive, fragmented data sets, making traditional review models difficult to scale and risk prone.
• AI allows investigation teams to connect structured and unstructured evidence, surface patterns and reconstruct narratives earlier.
• To realize value, companies must use AI with governance, auditability and human judgment to protect investigative rigor and credibility.

Internal corporate investigations are evolving quickly. As these matters increasingly draw on information from across the enterprise, investigation teams are expected to deliver outcomes with greater speed, consistency and confidence. In response, many organizations are exploring how artificial intelligence (AI) can strengthen investigative judgment and support more efficient review while keeping human oversight firmly in place.

Why traditional internal investigations no longer scale

As internal corporate investigations become more data driven and distributed, investigative teams need new ways to manage increasing scale and complexity. This reality has brought AI to the forefront as a powerful enabler of change. According to recent EY research, 94% of compliance and legal leaders believe that digital compliance tools are extremely or very important to expanding team capacity and effectiveness, further underscoring the urgency to modernize investigative approaches.³

How the rise of AI amplifies corporate investigation

AI solutions have evolved beyond basic summarization and keyword search, enabling more sophisticated analysis of investigative data. Modern models can analyze structured and unstructured data together, identify patterns across large data sets and synthesize information into coherent narratives that reflect investigative logic.

Crucially, AI also supports explainable analysis, helping investigators understand how insights are generated rather than relying on opaque results. In internal investigations, where findings may be scrutinized by regulators, auditors, boards or courts, this transparency is essential to defensibility.

Where AI can introduce risk — and how leaders stay in control

Without the appropriate guardrails in place, AI models can pose significant risks in investigations, from misinterpreted context to the introduction of bias and the inappropriate exposure of sensitive or privileged information. As such, clear auditability and human oversight are critical because they enable organizations to explain how investigative conclusions were reached.

In many organizations, management teams are actively encouraging the use of AI across functions. In the context of internal investigations, this pressure can introduce risk if AI is adopted too quickly or without a clear understanding of how AI should (and should not) be used in investigative work. In these instances, organizations risk deploying AI in ways that undermine investigative rigor or create challenges around explainability and defensibility in matters that are subject to heightened scrutiny.

At the same time, as data volumes continue to grow and cycle times lengthen, legacy approaches to internal investigations become less effective, making the status quo one of the most significant risks organizations face.

For leaders responsible for corporate investigations, the question is not whether AI has a role to play, but rather how to introduce it responsibly and in ways that strengthen, rather than strain, existing investigative models.

Where AI Is delivering value today

Across the internal investigation lifecycle, organizations are already applying AI-driven point solutions to unlock efficiency, consistency and earlier investigative insight. Common examples include:

• Analyzing large volumes of communications to identify themes and prioritize review
• Reviewing financial and transactional data to surface anomalies
• Supporting the drafting of investigative materials, including work plans, timelines and reports
• Enhancing interview preparation and post-interview analysis

These focused AI applications allow organizations to capture value while maintaining control, oversight and investigative rigor. They are increasingly used earlier in the investigation lifecycle to support early case assessment and help investigators pinpoint key documents sooner. By applying AI across both structured and unstructured data, investigation teams can more efficiently reconstruct timelines and align investigative outputs with established standards while maintaining clear human oversight and defensibility.

The next phase of internal investigations

AI is already reshaping how investigative work is carried out. As this shift takes hold, investigators are moving toward models in which they oversee and direct AI‑enabled analysis continuously rather than relying on AI at isolated points in the current investigatory process. Over time, these changes will continue to disrupt traditional investigative workflows and processes.

Modernizing corporate investigations with confidence

For organizations seeking to evolve their corporate investigations, several actions are critical:

In practice, many organizations are choosing to bring these elements together through platforms that support their investigative functions. One example is the EY Investigations Hub, which is designed to enhance internal corporate investigations by integrating evidence sources, case management and AI-enabled analysis within a secure, governed environment. By aligning technology with investigative workflows and oversight requirements, platforms like this demonstrate how organizations can move from experimentation to scalable, defensible adoption while keeping investigators firmly in control.