AI in Workplace Investigations: Help, Not Replacement

Last Tuesday, I attended a compelling webinar titled “Mitigating Risk Using AI.” It was a strong panel of voices featuring Lindsay Kim Chung, JD, Wendy Lloyd-Goodwin, and Nick Tyler. The conversation spanned everything from automation and evidence analysis to risk mitigation strategies. But one message, repeated firmly by Kim, has stuck with me:

AI is here to help the investigator — not replace them.

It’s a simple distinction, but an essential one. Because as AI tools continue to weave their way into HR processes, workplace investigations are starting to feel the tension between innovation and caution.

I left that webinar reflecting not just on the potential of AI, but also on its boundaries — and, perhaps most importantly, on how we as investigators can make informed decisions about if, when, and how to incorporate AI into our work.

What Does AI Actually Mean in Workplace Investigations?

Before diving deeper, let’s define the landscape.

When we talk about AI in workplace investigations, we’re usually referring to tools that can:

• Scan and categorize documents (emails, text messages, reports)

• Analyze communication patterns (flagging potential misconduct or anomalies)

• Summarize or transcribe interviews and notes

• Assist in identifying inconsistencies or gaps in witness accounts or evidence

In short, AI offers efficiency and pattern recognition at a scale human investigators simply can’t match on their own.

But — and this came up often in the webinar — AI doesn’t think. It doesn’t interpret nuance. It doesn’t understand context. And in investigations, those are the exact ingredients we rely on to make fair and human-centered determinations.

This is where the friction point arises: data privacy.

The Biggest Friction: Data Privacy and Legal Boundaries

Throughout the webinar, the conversation circled back repeatedly to data privacy laws — and for good reason. Canada’s evolving privacy landscape (with legislation like PIPEDA and provincial privacy laws) and the evolving landscape in other jurisdictions (the UK and the US, for instance) place strict obligations on how personal data is collected, processed, and stored.

Introducing AI into investigations raises essential questions:

• Where is the data stored? (Domestic or international servers?)

• Who has access to it? (Just the investigator, or third-party service providers?)

• How is sensitive employee information protected?

The promise of AI’s efficiency can tempt organizations to rush adoption, but workplace investigations are a space where confidentiality and trust are paramount. When we extrapolate that point, we realize that the reputational risk of mishandling data often outweighs the operational gains AI might bring in the short term.

AI as a Tool, Not a Decision-Maker

One of my key takeaways — and something I’ve thought about often since — is the importance of positioning AI as an assistant, not an adjudicator.

AI can help:

• Organize complex data sets

• Flag patterns worth closer human review

• Automate routine documentation

But it cannot replace the human judgment required to assess credibility, understand emotional nuance, or weigh the delicate balance of procedural fairness.

In fact, relying too heavily on AI risks over-sanitizing investigations, stripping them of the very human elements — tone, context, subtle cues — that make them meaningful.

Three Ways Investigators Can Use AI Wisely

With that tension in mind, here are three concrete strategies investigators (and HR leaders) can adopt to make informed, ethical decisions about AI use in workplace investigations.

1. Conduct a Privacy Impact Assessment (PIA) Before Using AI Tools

Before integrating any AI tool, it’s essential to understand its full implications.

Steps to take:

• Map out the data flow: What data is collected, where it’s stored, and who can access it.

• Identify third-party risks: If the AI tool relies on external servers, what privacy safeguards are in place?

• Consult legal advisors to ensure compliance with Canadian privacy legislation.

Why it matters: This upfront diligence helps prevent legal pitfalls and reinforces to participants that their data is being handled responsibly — a cornerstone of procedural fairness.

2. Define AI’s Role Clearly in Your Investigation Framework

Transparency is key. If you’re using AI to assist with tasks (like document review or transcription), inform participants upfront.

Best practice:

• Explain AI’s specific function in plain language:

“We use software to help organize and search large sets of documents. However, all evidence is reviewed by a human investigator.”

• Clarify that all determinations are made by people, not machines.

Why it matters: Transparency builds trust and avoids misconceptions that a faceless algorithm is deciding outcomes. Not every engagement will require you to make that clarification, but it's not a bad idea to have a well-articulated answer to such a question if it's asked.

3. Pair AI Tools with Strong Human Oversight

Think of AI as a co-pilot (this is no shout-out to Microsoft, in case you wonder). It can alert you to patterns, but it can’t land the plane.

Practical steps:

• Set up regular review points where you manually verify AI-generated summaries or flagged patterns.

• Use AI findings as leads, not conclusions. Every insight flagged by AI should be contextualized and validated by a human review.

Why it matters: This ensures investigations remain nuanced and credible, even as you leverage technological efficiencies. AI has its benefits, but it's not an infallible tool.

A Supporting Tool

The “Mitigating Risk Using AI” webinar opened my eyes to both the potential and the caution required when adopting AI in workplace investigations. When used properly, AI is here to support, not replace.

We often think of AI as something futuristic and separate from our daily work, but in reality, it’s already woven into many of the tools we rely on without a second thought. Take something as simple as Grammarly: it won’t make you a bestselling author, but it does catch typos and grammatical issues that might otherwise distract the reader or muddy your message. It sharpens the clarity of your writing — but it doesn’t replace your voice, your ideas, or your professional judgment.

That’s the same way we should view AI in investigations. Yes, AI can make us faster, more organized, and more precise. But it’s still up to us — as investigators and HR professionals — to ensure that technology enhances rather than outsources the core work of fairness, empathy, and sound judgment.

At the end of the day, no algorithm can replace the trust built when a participant sits across from you, tells their story, and knows they’ve been heard by a person who understands that fairness is as much about process as it is about people.