What investigators really need to know about artificial intelligence – and what they don’t.
Artificial intelligence is everywhere. From chatbots and image recognition to automated translations and predictive tools, AI is often portrayed as a near-magical solution to complex problems. In investigations, this can create both excitement and unease.
In a recent Paliscope webinar, “AI and Investigations: Cutting Through the Hype,” (watch recording) our Head of AI, Mats Kvarnström, unpacked what AI truly is, what it can do today, and—just as importantly—what it cannot.
This article distills the key insights for investigators and analysts navigating AI in practice.
Despite the buzz, AI is not magic. At its core, AI is software trained to recognize patterns. It excels at tasks such as:
What AI does not do is “understand” the world in a human sense. Every AI system is narrow—designed for specific tasks and trained on specific data. Even advanced tools like chatbots are fundamentally language models predicting the next word in a sequence.
As Mats emphasizes: “Everything an AI produces is a prediction. You should never take it for granted.” For investigators, this distinction matters.
An important thing to remember: AI supports investigations. It does not replace investigators.
AI can:
But it cannot:
Investigators remain the decision makers. AI outputs should always be treated as signals, not conclusions.
Artificial Intelligence systems follow a predictable lifecycle:
Crucially, models only perform well on data similar to what they were trained on. If the real world changes—or the objective was poorly defined—the results degrade. This is why AI systems must be continuously evaluated and never treated as infallible.
Generative AI—chatbots, image generators, text summarizers—is what most people now associate with AI. These systems are exceptionally good at:
But they also hallucinate—confidently generating information that is incorrect or entirely fabricated. For investigators, this is critical. Generative AI should never be used as a source of truth. Instead, it works best when:
One promising approach is Retrieval Augmented Generation (RAG), where AI responses are explicitly tied back to known documents and sources—making verification possible.
Humans have an ability that AI still struggles to replicate: learning from very few examples.
A child can recognize a horse after seeing just a simple drawing. An AI model, by contrast, may need thousands of labeled images—and still fail when conditions change. Humans also:
These qualities are essential in investigations, where nuance, context, and accountability matter.
When AI is used in investigative work, additional considerations apply:
Sensitive Data
Investigations often involve personal and protected information. AI systems must respect data integrity, privacy laws, and organizational policies.
High-Risk Capabilities
Technologies such as:
are powerful but legally sensitive. Their use must be auditable, justified, and lawful—often on a case-by-case basis.
Transparency and Traceability
Investigators must always be able to:
There should be no “magic button.”
AI can dramatically reduce manual workload. Tasks that once required thousands of hours—such as reviewing video footage frame by frame—can now be accelerated. But rather than removing investigators from the process, AI allows them to:
The goal is not automation for its own sake—it’s better investigations.
AI is not here to replace investigators. It is here to:
Used responsibly, transparently, and with humans firmly in the loop, AI can become one of the most valuable tools in modern investigative work. The intelligence still lies with the investigator. AI simply helps bring the right information to light—faster.
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