Why Law Enforcement Doesn’t Need More AI, it Needs Better Intelligence

As crimes grow more complex and criminal organizations more sophisticated, law enforcement must turn data into actionable intelligence to disrupt criminal networks. This is why governments and law enforcement organizations are placing huge bets on AI and backing them up with multi-million-dollar investments.  

In the UK alone, the Home Office is committing over £100,000,000 to AI and law enforcement in an all-out effort to stop money laundering, human trafficking, and cybercrimes.  Technology investments like these are critically important to solving crimes, but they will only add value to law enforcement if the solutions that emerge help law enforcement see risk more clearly, build trust in intelligence, support better decisions, and enhance accountability. 

That challenge sits at the heart of this conversation with Nick Dale, Director of Intelligence and Prevention at STOP THE TRAFFIK and a former law enforcement leader with more than two decades of experience tackling organized crime, exploitation, and complex investigations. STOP THE TRAFFIK is a global, intelligence-led NGO that works to prevent human trafficking and modern slavery. They do this by disrupting trafficking systems, identifying hotspots, and educating communities. 

Drawing on his journey from frontline law enforcement into intelligence and prevention, Nick reflects on what it takes to move beyond data overload and towards meaningful insight. He explores why context matters as much as volume, where the real structural and cultural barriers still sit, and why explainability, transparency, and human judgement must remain central as law enforcement adopts more advanced analytics and AI. 

If the future of law enforcements to be more intelligence-led, it also has to be more trustworthy by design. 

More funding doesn’t ensure AI reform 

While governments and law enforcement agencies clearly want and need better AI tools, big issues like ethical oversight need to be addressed. New technological tools funded by and that affect the general public must be deployed safely. Deciding what kind of platform to develop and which models to use is also critical. 

The sheer size and number of agencies involved, however, can often make addressing any of these challenges difficult. As Dale points out, implementing AI reforms across 43 independent forces across England and Wales is inherently difficult. 

The shift from data administration to data as intelligence 

Using data at scale as a tool to connect criminal networks is particularly important, as is knowing how to leverage that technology to ensure that AI is being used effectively and responsibly. 

Dale’s experience with analytics tools while heading the National Analytics Solution, however, uncovered a key gap: seeing networks isn’t enough. Investigators need context to understand risk and act effectively. Context is what allows investigators to move from simply administering data in the hopes of finding relevant information to leveraging data as an intelligence source. 

More data doesn’t guarantee better intelligence 

Gathering large datasets just for the sake of it is of little value. “You must be able to make sense of the noise in front of you…so you can then cut the wheat from the chaff and identify the most valuable data in there to understand the issues,” Dale explained. 

“It's not enough just to see a network and the person who is most highly connected. You must be able to understand how dangerous they are.” 

Gaining this kind of context, however, is highly unlikely without first having a database you can trust. 

Ensuring trust with data quality, context, and explainability 

One of law enforcement’s biggest challenges is bringing data together across disparate sources and siloed systems. Once curated, entity resolution and graph generation technology must be applied to create a connected and reliable data foundation. 

Without this single source of truth, it’s impossible to build an accurate, 360-degree view of criminals, counterparties, and their related networks.  

“What the tool should do,” Dale said, “is show you where the risk is. It should then lead the individual [investigator] to how the tool made that decision and what trade craft was [employed] that led the tool to make the decision.” 

AI makes this sort of deduction possible, but we must never overlook the human aspect of decision-making that is a key part of the process. 

Human judgment must always come first 

AI should support, not replace, human judgment. Officers must understand and be able to explain how conclusions are reached. This is why having a platform that can provide better decision-making is critical. 

As Dale explained, “[a tool] doesn't have to be that complicated to help law enforcement officers make a decision, but it has to be explainable if it has that immediate effect on the public.” 

Simply relying on technology without having a human in the loop that truly understands and can explain why certain law enforcement decisions were made is heading down a path that can jeopardize public trust and put case integrity at risk. 

Prevention requires collaboration  

AI in law enforcement is ultimately not about replacing judgment or gathering more data simply for its own sake. It’s about helping investigators understand threats more clearly so they can proactively disrupt criminal networks through better decision-making and prevent crimes like human trafficking from happening in the first place. It’s also about providing the right platform to allow them to conduct investigations that are ethical, proportionate, and explainable while safeguarding individual rights and ensuring public safety. 

Read this practical guide to learn more about human-AI decisioning.