How AI is modernizing investigations for high-stakes matters

AI is already strengthening investigations and the specific value these capabilities deliver to leadership teams, allowing teams to work faster, more accurately and with greater precision. For example, investigative teams currently employ AI tools to review large data sets of electronic communications to quickly identify critical discussions and focus on key issues, participants and timeframes. AI is also being used to analyze certain transactions by comparing separate datasets such as accounting data, supporting business records and related contemporaneous communications. Although each investigation is unique, several consistent use cases are emerging across legal, forensic and discovery domains. These capabilities are no longer experimental — they are being deployed today to enhance investigative judgment and accelerate outcomes in matters that carry significant legal, regulatory and reputational stakes.

Across industries, several use cases consistently demonstrate the tangible benefits that AI provides.

1. Rapid analysis of large volumes of communications

Organizations continue to generate immense amounts of email, chat, messages and collaborative platform content. Traditional review approaches — manual sampling, search terms and linear review — struggle to keep up with the volume and complexity.

2. Detection of financial and transactional irregularities

Investigations often require analyzing structured data—such as accounting records, payment files, procurement activity, HR data and transaction logs. These datasets are often too large and too complex for traditional manual forensic techniques to fully evaluate and, before AI, have relied upon judgmental targeting and sampling.

3. Accelerated drafting of investigative materials

Significant investigative time is typically spent creating chronologies, witness outlines, workplans, memoranda and interim reports.

4. Enhanced interview preparation and post interview analysis

Interviews remain one of the most judgment-led phases of an investigation. AI does not replace investigative skill — but it significantly enhances preparation and follow up.

Across all investigative use cases, AI delivers cross-cutting advantages that significantly enhance the efficiency and strategic value of the work. By surfacing key documents and early indicators sooner, AI accelerates initial case understanding and shortens the path to clarity. It unifies evidence by integrating structured and unstructured data into a more complete fact pattern, while reducing reliance on manual review — lowering cost, minimizing sampling risks and improving overall accuracy. Together, these collective benefits enable investigative teams to operate with greater speed, insight and confidence.

Investigations — particularly those involving potential misconduct, regulatory scrutiny or significant organizational impact — nearly always face some form of external review. Boards, auditors, regulators and government agencies routinely evaluate not only the findings of an investigation but also the sufficiency of the process used to reach them. As AI becomes more deeply embedded in investigative workflows, leadership should expect its use to be evaluated with the same rigor applied to traditional forensic and legal procedures. 

Defensibility depends on the professionals guiding the work — their judgment in selecting AI tools built on responsible governance principles, their decisions about how those tools will be deployed, their oversight of AI-generated outputs throughout the matter and their deployment in a manner to uphold privilege protections. A sound investigation begins with clarity about who the key stakeholders are and what information they will require, as this understanding shapes the investigative workplan, including when and how AI should be incorporated. Ensuring defensibility requires thoughtful decisions at every stage — beginning with this upfront planning to inform selection of the right tools and their application. 

Governance and transparency of AI tools

Corporate boards and leadership will rely on the expertise of its legal advisors and forensic specialists to select and deploy AI tools that are both appropriate and advantageous to the unique nature of the matter. This professional judgment is applied at the very outset — specifically in selecting AI tools that are built on responsible governance principles. AI models vary widely in their approaches to data security, model training, transparency and auditability. Professionals must therefore prioritize tools that provide explainability, maintain clear documentation of how outputs were generated and align with established governance principles. These features help ensure reliability before any analysis begins.

That same judgment is essential in determining how and when to deploy AI. The decision to apply AI cannot be based on convenience alone; it must reflect the size and complexity of the data, the nature of the allegations and the likely scrutiny the investigation will face. Whether AI is used to review communications, identify anomalies or synthesize documents, its application must be demonstrably reliable, reasonable and sufficient. By grounding both tool selection and deployment in expertise and responsible governance, professionals ensure that AI enhances investigative rigor rather than undermining it.

Testing and verification of AI outputs 

Two core concepts underpin the credibility of AI-generated insights: accuracy and explainability.

• Accuracy reflects AI’s ability to produce consistent and correct results. Investigators should apply reperformance techniques to verify that the system arrives at the same outcome under similar conditions. This not only validates the tool's effectiveness but also demonstrates to external stakeholders that results were not arbitrary or the product of a “black box.”
• Explainability applies to subjective or judgment-oriented assessments — particularly with decision-making often seen in Agentic AI — such as prioritizing documents, identifying thematic patterns, or suggesting relationships between data points. Here, investigators must be able to follow the model’s rationale, understand why it reached a conclusion and assess whether the logic is reasonable within the broader fact pattern. This ability to test and challenge the AI’s reasoning is essential to establishing that human judgment, not automation, guided the investigation and interpretation of results.

AI should enhance professional expertise — not replace it. Investigators remain responsible for interpreting outputs, assessing their reasonableness, adjusting inputs or parameters and documenting key decisions. They also oversee and verify the accuracy and explainability of AI generated results. Throughout the process, investigators must stay actively engaged: testing outputs, validating that they align with the broader fact pattern and ensuring no model operates with unchecked autonomy. Maintaining clear records of how AI supported the investigation, how outputs were validated and where human judgment guided the analysis is essential to demonstrating reliability and sufficiency during external review. 

Consistent with these principles, courts have made clear that uncritical or unchecked reliance on AI — particularly without independent verification, transparency and documented human judgment — can expose organizations to serious legal and evidentiary consequences. Recent federal decisions demonstrate that AI related failures are treated no differently than other lapses in professional responsibility.¹ Where parties have relied on AI-generated outputs without meaningful review, courts have imposed significant sanctions, awarded attorneys’ fees and costs and warned that such conduct can undermine the integrity of proceedings and jeopardize case outcomes. These decisions underscore a central expectation: AI may assist professional work, but responsibility for accuracy, reasonableness and reliability remains firmly with the humans deploying it.

Protecting privilege 

Further, investigators must exercise caution both in the degree to which they rely on AI and in the nature of the information they disclose to generative AI tools. Courts have clarified that the use of AI tools does not, standing alone, create or preserve attorney‑client privilege or work‑product protection. In particular, disclosures to publicly available AI platforms may undermine confidentiality and result in the waiver of otherwise applicable protections. Recent decisions emphasize that recognized privileges depend on a confidential, fiduciary relationship with a licensed professional and do not extend to interactions with non‑human, publicly accessible systems.² Accordingly, investigators and counsel must exercise care in determining what information is shared with AI tools and must ensure that AI is used in a manner consistent with privilege, confidentiality obligations and professional oversight.

AI is reshaping how investigations are conducted, enabling teams to work faster, more accurately and with greater precision, when paired with experienced professional judgment. The most effective investigative teams embrace AI for the acceleration and analytical power it brings, while maintaining the governance, oversight and disciplined reasoning required to withstand scrutiny.

Organizations that modernize their investigative approaches — by empowering legal advisors and forensic specialists to deploy AI wisely — will be better equipped to manage risk, uncover facts quickly, respond decisively in high-stakes environments and reduce financial and operational disruption. As the landscape of data, regulation and corporate exposure continues to evolve, shifting from traditional methods to human-led, AI-accelerated investigations is not simply an enhancement. It is a strategic imperative.

This article was co-authored by Vanessa Singh Johannes, Partner, Government Enforcement Defense & Investigations, and Holli Gray, Associate, Commercial Litigation, with Foley & Lardner LLP.