Conducting a modern internal investigation is very different from what it was ten years ago. At the time, fewer platforms meant fewer complications, but today, the amount of digital information is enormous. From Slack to Teams and Zoom, along with numerous messages, one may easily miss something while investigating manually.

“Technology completely transformed how people communicate within organizations; therefore, our approach to conducting investigations had to change accordingly. AI and machine learning technologies help legal and compliance professionals detect behavior patterns that cannot be observed by humans,” says Sarai Schubert of Hanzo.

Below are some examples of the use of AI and ML in contemporary internal investigations.

Detection of Misconduct 

Nowadays, people often use multiple digital communication channels simultaneously. One might begin the conversation on Slack, then proceed with an email exchange and continue on a video call. Such behavior makes investigation difficult because the message’s context is lost. With AI, this problem can be solved easily, since it tracks the entire discussion across all available communication platforms, detects patterns in how people communicate, and uses linguistic analysis methods.

Machine learning does not focus only on obvious signs of misconduct. It searches through thousands of messages and detects abrupt changes in language, repeating patterns, changes in behavior, mentions of new participants in the discussion, and jumping from platform to platform.

Accelerating Internal Investigation 

Traditional processes for conducting internal investigations usually involve long hours spent searching through large volumes of communications and case-related documents. To make their processes more  efficient , companies use AI-powered systems. 

With them, data is already sorted, tagged, and grouped, giving researchers the opportunity to analyze it without additional effort to find the information they need. Moreover, AI ranks communications by urgency and automatically detects any risky elements.

With the integration of AI into legal software, the process becomes even more efficient. The system not only identifies the facts but also seeks to establish the motivation behind employees’ actions. In the case of a harassment allegation, for example, all relevant messages and correspondence will be provided along with a chronology of events.

Taking Human Bias Out of the Equation

Manual reviews cannot always guarantee a good outcome. Tiredness, lack of attention to detail, or bias can significantly distort one’s perception of the message in question. However, artificial intelligence will not experience any of those issues. It will review all records systematically, thereby reducing the risk of overlooking evidence or crucial details.

Moreover, machine-learning-powered legal software becomes even smarter with each investigation. The more investigators mark an outcome as true, the better the system will be able to distinguish misconduct from irrelevant noise. Thus, the decisions will be based on data rather than personal biases, which is the only way to ensure objectivity.

Creating Defensible Investigations and Improving Efficiency

Being efficient is great, but the process should be reliable too. After all, many internal investigations may escalate into legal disputes. All actions and decisions made in such cases need to be thoroughly documented to ensure transparency and integrity throughout the process.

A platform with built-in machine learning algorithms provides an opportunity to properly document all actions taken during the investigation, the reasons for reviewing specific evidence, and the timing of specific decision-making processes. Such a level of documentation makes it possible to prove the objectivity of the review process, which can become important later on when you need to support your investigations, for example, when a matter goes to court.

Conclusion

Overall, the implementation of artificial intelligence and machine learning has fundamentally changed the nature of internal investigations. What took investigators months of hard work to complete can now be done within a few days thanks to these technologies.

When such approaches are combined with powerful legal software solutions, the process becomes even more efficient and effective. At this point, using AI is not just a shortcut. It is about building investigations that are open, reliable, and ready for whatever comes next.