Data Is Reshaping Intelligence-Led Policing in U.S. Cities — i2 Group — February 18, 2026

By Staff Reports - February 18th, 2026

For decades, American policing borrowed a familiar metaphor from mid-20th century television: gather everything, sift later, and let “just the facts” emerge. That dragnet approach made sense when information was scarce and investigations were bounded by paper files, radio calls, and eyewitness accounts. Today, law enforcement agencies face the opposite problem. Data is abundant, continuous, and fragmented across thousands of digital systems.

According to research from the Major Cities Chiefs Association, digital evidence from body-worn cameras, mobile devices, CCTV and other technology is now a defining feature of modern investigations, creating “significant challenges” for agencies as they manage growing volumes and varieties of data across disparate systems rather than a limited set of records.

The strategic question confronting state and local agencies is no longer how to collect information, but how to convert overwhelming volumes of data into timely, defensible decisions. That shift is quietly redefining intelligence-led policing across the United States, according to Roger Stokes, head of U.S. state, local, and education markets at i2 Group in an executive vidcast interview with BizTechReports.

“Analysts used to spend most of their time looking for data,” Stokes said. “Now they spend 70 to 80 percent of their time managing it.”

That inversion, he argues, is the core operational challenge of modern policing. As agencies, in addition to adopting body cameras, embrace license-plate readers, surveillance systems, IoT sensors, and telematics from vehicles and consumer devices, information arrives faster than traditional investigative workflows can absorb it. The result is not a lack of intelligence, but a shortage of analytical capacity.

Intelligence in the Age of Data Plenty

Full Vidcast Interview w/ Roger Stokes, i2 Group

 That capacity gap is compounded by the fact that much of today’s investigative data no longer originates inside law enforcement systems at all. Instead of relying primarily on records they generate themselves, agencies are increasingly required to ingest, interpret, and validate information produced by consumer devices, private platforms, and public infrastructure that were never designed for police use.

 Telemetry from vehicles, metadata from consumer electronics, and ambient data captured by smart infrastructure now routinely appear in criminal inquiries. Domestic violence investigations, for example, increasingly examine voice assistants or smart devices located at incident scenes, Stokes said.

“These are not traditional data sources,” he noted. “But they’re data sources nonetheless.”

 The challenge is less technical than organizational. Structured records, unstructured text, video feeds, and machine-generated telemetry rarely live in the same systems. As a result, analysts are forced to reconcile formats, timestamps, jurisdictions, and access controls before any meaningful analysis can begin. In smaller agencies, that reconciliation often falls to sworn officers or senior leaders already carrying operational responsibilities.

The consequence, Stokes warned, is a bottleneck from the very beginning of an investigation, at precisely the moment when speed matters most.

Strategy Without Technologists

 While intelligence-led policing has been endorsed for years by federal frameworks such as DOJ and FBI guidelines, implementation at the local level has often lagged strategic intent. Command staff and elected officials may support data-driven policing in principle, but lack exposure to the technical realities shaping modern intelligence work.

A 2025 Department of Justice COPS Office “Future Trends in Policing” report found that roughly 54 percent of U.S. law enforcement agencies intend to implement or expand intelligence-led policing in the near future, illustrating that meaningful adoption is still in progress rather than universally realized.

Stokes suggests that agencies are beginning to address that gap by widening the stakeholder base involved in strategy discussions. Closing that gap, Stokes argues, requires changes not just in technology investments but in who is included in strategic decision-making. Younger analysts entering law enforcement from academic programs, for instance, bring fluency in data handling that senior leadership may not possess, but their insights are not always incorporated into policy decisions.

“The knowledge base exists inside these organizations,” he said. “It just hasn’t always been invited into the room.”

That disconnect has cultural consequences. Technology can no longer be treated as a back-office function supporting investigations after the fact. That distinction has eroded as law enforcement agencies face mounting operational and public accountability pressures. Staffing shortages, budget constraints, and rising expectations around transparency have pushed analytics closer to the front line. In practice, that means patrol officers, investigators, and supervisors increasingly operate inside data-rich environments, whether or not they see themselves as technologists.

The Rise of Real-Time Crime Centers

One visible manifestation of this shift is the expansion of real-time crime centers (RTCCs). Designed to aggregate live video, sensor data, and dispatch information, RTCCs function as operational hubs that support officers responding to incidents while they unfold.

More than 300 such centers are now operating across the United States, according to Stokes, with more emerging as agencies secure funding and technical support. The model resembles corporate network operations centers or military command posts. While the concept is getting lots of press, however, its adoption in policing has been uneven.

“Integration is the hard part,” Stokes said. “Voice systems, video feeds, private-sector data, public infrastructure exist in different repositories. They were never designed to work together.”

RTCCs represent an attempt to collapse those silos. In so doing, however, second-order problem emerges:

Who manages the data once it is unified?

Human Capital as the Limiting Factor

Turning unified data into timely, actionable insight depends on the people and processes that curate and interpret it. Consolidating information is only the first step. It is here that there is often a mismatch between strategic vision and operational implementation.

Several agencies, Stokes observed, still assign senior sworn officers to oversee intelligence functions as an additional duty. While experienced, those leaders rarely have the time required to manage ingestion, validation, analysis, and dissemination cycles at scale. What is needed are dedicated human resources.

As a result, we are beginning to see departments experiment with civilian intelligence managers and professionals with law enforcement literacy, analytical training, and technical fluency. The approach mirrors trends in cybersecurity staffing in the private sector and emergency management in government agencies that understand continuity and specialization matter more than rotational assignments.

“Dedicated staffing is what guarantees success,” Stokes said. “You can’t run this [RTCCs] as a side project.”

That emphasis on stability also intersects with funding realities. Intelligence capabilities require sustained investment, not one-time grants. Indeed, agencies that treat analytics as episodic often struggle to demonstrate long-term impact, undermining political support.

Measuring Success When Nothing Happens 

The challenge, however, is that intelligence work does not always lend itself to visible or easily quantified results. Unlike traditional performance metrics, intelligence success is often invisible. When analysts disrupt criminal activity before it occurs, there is no incident to report, no arrest to tally, and no headline to cite.

“In intelligence, the biggest wins are the ones no one ever hears about,” Stokes said.

That creates tension with elected officials who expect measurable returns on public investment. Crime reduction plans increasingly reference analytics and intelligence capabilities, but translating prevention into accountability metrics remains difficult.

The alternative, Stokes warned, is perpetual reaction. When incidents occur, investigators are immediately asked whether intelligence existed and was acted upon. Without the infrastructure and personnel to answer those questions confidently, agencies remain vulnerable both operationally and politically.

Funding Beyond the Budget Line

While local budgets are constrained, Stokes emphasized that intelligence initiatives are not limited to municipal funding streams. Federal and state grants, private foundations, and nonprofit programs regularly support technology and analytical capacity building. The barrier, he said, is often awareness rather than availability.

“Many agencies don’t realize how many funding avenues exist,” he said, noting that grant literacy has become an operational competency in its own right.

As more departments pursue intelligence-led strategies, competition for those funds is likely to increase. Agencies that can articulate integrated plans—combining technology, staffing, governance, and oversight—are better positioned to secure sustained support.

Stokes often begins his engagements with clients by exploring those funding options.

"It is important to introduce this aspect of intelligence-led initiatives early in conversations with agencies, particularly when leaders recognize that traditional budgets alone are unlikely to sustain intelligence programs over time," he said.

From Analysis to Operations

At the tactical level, modern analytics increasingly inform how agencies deploy scarce resources. Pattern analysis tools can identify narrow time windows when criminal activity is most likely, allowing surveillance teams to be deployed for hours rather than days. The operational benefit is not predictive policing in a speculative sense, but precision planning grounded in historical behavior.

“Logistics matter,” Stokes said. “Using data to deploy resources more intelligently is often the biggest immediate payoff.”

By logistics, Stokes is referring to the practical mechanics of policing: how many officers or analysts are available, where they are positioned, how long they can remain deployed, and what other demands are competing for their time. Behavior analysis, in this context, does not attempt to predict crimes before they happen, but examines historical patterns — such as timing, location, and frequency — to identify when limited resources are most likely to be effective.

 The goal is efficiency rather than foresight. Leaders should look for opportunities to narrow surveillance or response windows so teams can be deployed deliberately and then reassigned, instead of remaining on extended standby with little actionable activity.

That dual use—supporting analysts while informing operational decisions—signals a broader shift. Intelligence is no longer confined to investigative units. It is becoming a management function, influencing staffing, scheduling, and risk assessment across departments.

A Structural, Not Technological, Transition

The evolution from dragnet policing to intelligence-led operations is not a technology story alone. It is a governance and workforce story, shaped by data abundance, fiscal pressure, and public scrutiny. Agencies that treat analytics as infrastructure rather than software are more likely to navigate the transition successfully.

As Stokes framed it, the central question is no longer whether law enforcement will adopt intelligence-led models, but whether institutions will align leadership, staffing, and funding to make them effective.

“In the end,” he said, “being proactive is the goal. And if nothing happens, that’s success.”

EDITOR’s NOTE: To Learn More i2 Group and their role in state and local intelligence-led initiatives click here.