Leveraging Technology In Internal Investigations: The Promise And Potential Of Generative AI

Investigations today are defined by complexity, scale, and increasing diversity of data sources. Investigators face the challenge of reviewing thousands – sometimes millions – of documents and communications across email, messaging apps, collaboration platforms, and cloud repositories. The task is not only about volume but also about connecting disparate data types to uncover patterns of conduct without clear starting points. Technology has become indispensable for managing these challenges, enabling investigations that are thorough, efficient, and defensible. 

Generative AI (GenAI) is the latest evolution in this journey, but at this point in time its potential lies in how it integrates with the broader technology stack – including eDiscovery platforms, forensic tools, and advanced analytics – and in developing the expertise to use these tools effectively. This article explores why integration and capability-building matter as much as the technology itself.

The role of an integrated technology stack in internal investigations

Historically, investigations have relied on traditional and often siloed technology tools such as e-Discovery platforms and forensic software to identify and review relevant documents, and analytics to identify trends and behavioural patterns. In recent years the sophistication of investigative technology has further developed. Advanced analytics, data clustering, and AI-driven solutions now enable investigators to gain early insights, identify trends, and track communications across multiple platforms. 

GenAI represents the next step in this evolution. Unlike conventional tools, GenAI can simulate the actions of a human reviewer, finding and describing key documents, summarising communications, and even identifying patterns of behaviour through the use of natural language prompting. 

Whilst the tools have evolved, integration has often lagged. 

GenAI in context: opportunities amplified by integration 

When embedded into a broader technology stack, GenAI unlocks significant value: 

• Accelerated document review and pattern detection: By analysing large volumes of documents, GenAI can detect patterns, flag unusual communications, and highlight potential areas of concern. This is especially useful when the scope of alleged misconduct is unclear. 
• Enhancing speed and efficiency:  GenAI can accelerate the review process, enabling teams to process vast datasets more efficiently than manual review alone. Investigators can then focus manual review efforts on the most probative or key documents.
• Predictive insights:  Advanced models can provide predictive indicators of risk, helping organisations proactively identify areas requiring further scrutiny.
• Automated reporting:  GenAI can quickly generate draft reports and summaries, helping investigators articulate findings and key themes with greater speed and consistency.

Developing expertise: the human factor

Technology alone does not deliver defensible outcomes – expertise does. Organisations must invest in:

Training investigators on prompt engineering

Effective use of GenAI depends on the expertise to craft comprehensive prompts and design robust validation workflows.

Understanding integration points

Teams need to know how GenAI interacts with existing tools to prevent duplication, avoid gaps and maximise its value. For example, GenAI often struggles with very large datasets, timing out before analysis is complete. They may also lack support for all file types and integrating data from multiple platforms (e.g., SMS, WhatsApp, Loop) can be challenging.

Validation and verification

Human judgment remains essential to verify AI outputs, interpret context, and ensure fairness. GenAI models can produce errors, including hallucinations and outputs which reflect any biases in the data it is analysing. Human verification remains essential to ensure findings are robust and defensible. It is important for investigators to bring their own expertise and judgment when interpreting GenAI outputs and making recommendations.

Governance and compliance knowledge

Expertise in regulatory frameworks ensures that technology use aligns with legal standards. Handling sensitive data also requires careful attention to privacy and security, particularly when using cloud-based GenAI solutions.

Building the above capability transforms GenAI from a tactical tool into a strategic asset.

Case study

The following case study demonstrates that GenAI's value is maximized when embedded within a broader technology stack, not used in isolation.

Context:  An internal investigation in relation to alleged inappropriate communications by an employee. The investigation required determining whether the conduct was isolated or part of a broader pattern of behaviour

Scope:

• Gmail, WhatsApp, SMS, and Signal data collected 
• 16,000 relevant documents identified after filtering

Approach:  By integrating GenAI with advanced analytics and eDiscovery tools, the team:

• Streamlined review across diverse data types
• Quickly interrogated Signal messages
• Validated outputs for accuracy and defensibility

Impact:

• Significant reduction in manual review time
• Improved accuracy and confidence in findings
• Delivered a defensible outcome under time-critical conditions

Mitigation strategies and best practices

To maximise the benefits of GenAI while managing risks, organisations should adopt the following best practices:

• Adopt an integration-first mindset: Ensure GenAI complements existing tools rather than replacing them.
• Establish clear usage policies: Define roles, responsibilities, and guardrails for GenAI use.
• Pair GenAI with human expertise: Validate outputs and maintain independent judgment.
• Continuous learning: Update skills as the tools evolve and new integration points emerge.

Looking ahead

The landscape of GenAI in investigations is rapidly evolving. We expect to see the following emerging trends over the next 12-24 months:

• Contextual analysis across platforms: Future tools will better integrate data from multiple sources, providing richer context and more comprehensive insights.
• Agentic workflows: We expect to see more sophisticated tools which will streamline and automate the many complex processes underlying investigations.
• Specialised AI tools: The market is moving towards more targeted GenAI solutions, such as specific tools for use in navigating disputes and investigations.

Balancing innovation with risk management will be critical. Human oversight will remain central to ensuring investigations are thorough, fair, and defensible.

Conclusion

GenAI is a powerful enabler in investigations, augmenting—rather than replacing—traditional technology and human expertise. Organisations that invest in both technology integration and capability development will unlock the full potential of GenAI-driven investigations while safeguarding accuracy, compliance, and defensibility.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.