Verkada Study Exposes Physical Security's AI Adoption Gap

Verkada has put a striking conflict into the google news cycle: security leaders expect AI to reshape their work, yet adoption remains uneven and cautious.

The headline reflects a broader shift across physical security. Vendors are moving beyond recorded video toward systems that identify events, rank alerts, reconstruct incidents, and initiate responses. Buyers, however, still need proof that these tools are accurate, governable, and compatible with existing operations.

That gap places Verkada and other platform vendors under pressure. They must turn interest in AI into routine use without minimizing privacy, cybersecurity, false-alert, or vendor-dependence concerns. Genetec, Ambient.ai, and established video-management providers are making competing cases for how that transition should happen.

The news is therefore not simply that physical security teams want AI. They have signaled that interest for years. The real story is that expectations are advancing faster than many organizations can redesign their workflows, policies, and accountability structures.

What the Verkada Google News Headline Actually Changes

The latest attention shifts the argument from whether AI matters to whether security teams can use it reliably every day.

The original google news listing points readers to reporting about a Verkada study and an AI adoption gap. Google News is an aggregator, so its headline is a discovery signal rather than primary evidence. The underlying survey materials matter more than the listing itself.

Verkada has separately promoted an event titled “AI in Physical Security: What 2,700 Leaders Say Is Delivering Now and What’s Next.” Its AI security webinar was scheduled for July 30, 2026, and framed the discussion around current results from thousands of leaders.

That framing is important, but the public webinar listing offers limited methodology. It does not identify the respondents’ geographic distribution, organization sizes, job functions, or exact survey questions. Those details affect how confidently readers can generalize any findings.

A previous Verkada study provides a documented baseline. The company commissioned The Harris Poll to survey 1,518 IT and physical security leaders for its 2024 State of Cloud Physical Security report.

According to the cloud security study, 92% of respondents agreed that physical security’s future was in the cloud. Among organizations not fully transitioned, 86% planned to move toward cloud deployment.

The same research found that 75% expected to make a cloud transition within 12 months. Ninety-six percent expected to do so within 18 months. Those intentions showed substantial enthusiasm for modernization.

However, purchase intentions do not automatically become completed deployments. Cloud migration can require camera replacements, network changes, identity integration, retention planning, staff training, and new incident-response procedures.

The 2024 report also found that 82% expected AI-based video analytics to have a moderate or major impact on physical security. That result measured anticipated impact, not verified operational adoption.

The distinction is central to the current story. A leader can believe AI will matter without trusting it to generate alarms, identify people, or trigger an intervention.

Security teams also use the word “AI” for different capabilities. Basic motion classification, license plate recognition, natural-language video search, predictive analytics, and autonomous response carry very different risks.

A team using object detection to filter recorded video has not necessarily adopted AI for real-time decision-making. Likewise, a trial at one location does not establish organization-wide deployment.

The latest Verkada discussion therefore creates a useful pressure test. Vendors need to define what counts as adoption and distinguish experiments from production use.

Customers need more than a feature inventory. They need evidence showing whether AI improves detection, reduces investigation time, limits false alerts, and supports defensible decisions under actual operating conditions.

Without those definitions, a large survey can produce a dramatic adoption-gap headline without revealing where implementation truly stalls. The missing detail is not a reason to dismiss the finding. It is the reason to examine it carefully.

Security Leaders Want AI Before Their Operations Are Ready

Physical security’s adoption gap is primarily an operational problem, not a shortage of available algorithms.

Modern security environments generate more information than operators can continuously review. Cameras, door controllers, alarms, intercoms, environmental sensors, and visitor systems each create separate streams of activity.

AI promises to filter those streams. It can flag a person entering a restricted area, prioritize unusual movement, connect an access event with video, or locate matching footage across cameras.

These capabilities address a genuine constraint. Human operators cannot give equal attention to every camera or alarm. The practical goal is to direct their attention toward events that warrant judgment.

Yet an algorithm that identifies an event is only one component of a security process. Organizations must decide who receives the alert, how quickly they respond, and what evidence they preserve.

They also need escalation rules. A detected person near a loading area might indicate theft, an authorized employee, a contractor, or a simple classification error.

If teams do not redesign workflows around those possibilities, AI merely produces another queue. The technology increases signal volume without improving the final decision.

The problem becomes harder across multiple sites. A retailer, hospital network, school system, or manufacturer can have different staffing, layouts, risks, and legal obligations at every location.

A model that performs adequately in a controlled lobby might struggle in a crowded parking lot. Lighting, weather, camera placement, uniforms, and changing floor plans can affect results.

Security leaders must therefore evaluate systems in context. Accuracy reported by a vendor does not establish accuracy in every customer environment.

Organizational ownership can be equally unclear. Physical security might select cameras, while IT manages networks and identity systems. Legal and privacy teams may control acceptable uses of facial or behavioral analysis.

Human resources can also become involved when video analytics affect workplace investigations or employee monitoring. Procurement must then evaluate data handling, contractual controls, and long-term vendor viability.

This creates a slower approval path than a simple software trial. Physical security AI observes real people in real places, often without an opportunity for those people to opt out.

The consequences can also be immediate. A mistaken document summary can be corrected later. A mistaken security escalation can interrupt work, involve law enforcement, or unfairly focus attention on an individual.

That difference explains why enthusiasm and implementation can coexist at very different levels. Security leaders see clear value, but they also carry responsibility for errors.

The cloud transition adds another dependency. AI features often work best when devices, metadata, identity records, and management tools share a common platform.

Verkada’s model emphasizes a cloud-managed, integrated system. The company combines video security, access control, alarms, intercoms, sensors, and visitor management within its Command platform.

Integration can reduce the work needed to connect an alarm with relevant footage. It can also give one vendor a larger role in a customer’s security architecture.

For buyers, that is both an advantage and a source of caution. A unified system can simplify operations, but migration and switching become more consequential.

This is why the adoption gap cannot be closed by adding another AI button. Organizations need governance, integration planning, operator training, and measurable acceptance criteria.

Those requirements slow deployment, yet they also protect the organization from scaling an unreliable process. In physical security, deliberate adoption is not necessarily failed adoption.

The Main Conflict Is Vendor Ambition Versus Buyer Trust

Verkada is selling a more proactive security model, while buyers still need control over how machines influence human responses.

Physical security platforms historically helped organizations record events and investigate them afterward. AI vendors now argue that systems can detect risks earlier and coordinate action before an incident escalates.

Verkada has described this direction as a transition from reactive monitoring toward proactive security. Its product strategy includes natural-language video search, connected alerting, deterrence tools, and unified incident timelines.

Natural-language search lets an operator describe a person, vehicle, or event using ordinary words. The system then attempts to locate matching footage without requiring the user to inspect each camera manually.

A unified timeline assembles related footage into chronological order. That can reduce the manual work required to reconstruct movement across several cameras.

AI-powered deterrence goes further. It connects detection with an intervention, such as an audio warning or another configured response. This shortens the distance between classification and action.

Each step adds operational value, but each step also raises the cost of an error. A poor search result wastes time. A poor automated response can affect a person immediately.

That progression makes trust the decisive product requirement. Buyers must understand what the system detects, how it ranks confidence, and where humans retain authority.

Verkada is not alone in pursuing this market. Genetec’s 2026 State of Physical Security report drew responses from 7,368 professionals across six global regions.

The company found that interest in AI adoption among end users had more than doubled from the previous year. AI also ranked beside access control and video surveillance as a leading project priority.

At the same time, 70% expressed concerns about how AI systems were designed and implemented. Respondents specifically raised data use and understanding how AI works.

Those global survey findings reinforce the tension behind the Verkada headline. Demand is rising, but concern is not disappearing as products improve.

Genetec also reported that more than 70% of respondents used unified or integrated systems. Sixty percent identified integration of new capabilities as their main reason for replacing legacy technology.

That evidence suggests buyers do want consolidation. It does not mean every organization wants a cloud-only architecture or a single vendor controlling every layer.

Genetec emphasizes flexible deployments spanning on-premises, cloud, and hybrid environments. Verkada’s identity is more closely tied to cloud-managed hardware and software operating together.

This creates a meaningful strategic choice. One route prioritizes a tightly integrated vendor platform. Another prioritizes architectural flexibility and mixed deployment models.

Neither approach eliminates AI governance. An open or hybrid system can introduce integration complexity. A unified cloud platform can increase concentration and switching concerns.

Buyers must decide which risks are easier for them to manage. A small team might value one console and automatic updates. A regulated enterprise might require more control over data location and system boundaries.

Long-term trust also extends beyond model accuracy. Genetec found that 73% of end users considered vendor stability important when evaluating solutions.

Product performance and price indicators followed at 45% and 43%. That ranking reflects the lifespan of physical security infrastructure.

Cameras and access systems are not disposable apps. Organizations expect them to operate for years, integrate with building processes, and remain supported through policy and technology changes.

AI complicates that expectation because models and cloud services can change more quickly than installed hardware. Customers need to know which functions run locally, which require cloud processing, and what happens when services change.

They also need export paths for footage, event histories, and identity data. A security platform becomes difficult to replace once operations depend on its proprietary workflows.

The main contest is therefore not Verkada versus one named competitor. It is the vendor promise of proactive automation versus the buyer requirement for accountable control.

Vendors that treat caution as mere resistance will misunderstand the market. Buyers are not only asking whether AI works. They are asking whether they can defend using it.

What the Adoption Numbers Still Do Not Prove

Survey enthusiasm cannot establish accuracy, safety, or business value without clear methodology and independent operational evidence.

The google news headline gives the adoption gap public visibility, but it does not resolve several basic measurement questions.

First, the term “adoption” needs a threshold. Does it mean purchasing an AI-enabled camera, activating one analytic feature, running a pilot, or using AI across every site?

A generous definition can make adoption appear high. A strict production definition can make the same market appear immature.

Second, adoption does not measure frequency. A company might use natural-language search during rare investigations while leaving real-time detection disabled.

Third, adoption does not measure outcome quality. An organization can deploy AI without reducing incidents, response times, false alarms, or investigative workload.

Fourth, survey respondents might not know which features use machine learning. AI can operate inside motion filters, search tools, identity matching, or alert prioritization without appearing as a separate product.

This uncertainty has appeared in earlier industry research. A 2024 Pro-Vigil survey covered leaders in sectors including construction, retail, manufacturing, utilities, and vehicle dealerships.

Only 6% of respondents said they knew their security strategy used AI. Seventy-one percent said it did not, while 23% were unsure.

The study also found that 57% did not know whether AI could help stop physical security incidents. Those adoption findings showed how limited awareness can distort reported usage.

That survey came from a security provider with a commercial interest in broader adoption. Verkada and other vendors also benefit when their research supports modernization.

Commercial sponsorship does not invalidate a study. It does make methodology, question wording, and complete data tables especially important.

Readers should therefore separate three kinds of evidence. Vendor surveys measure reported attitudes. Product telemetry measures actual feature use. Controlled operational studies measure outcomes.

A credible adoption claim should explain which category it represents. Combining them can make a market transition look more complete than it is.

Independent validation matters most for high-consequence capabilities. Facial matching, weapon detection, behavioral analysis, and automated escalation require careful testing across realistic conditions.

False negatives can leave a threat undetected. False positives can overwhelm operators or prompt unnecessary interventions.

Performance can also vary among demographic groups, environments, and camera configurations. A single accuracy percentage can hide those differences.

Privacy introduces another unresolved dimension. Physical security AI can infer patterns about employees, visitors, customers, students, patients, or residents.

An organization might begin with safety monitoring and later use the same data for productivity analysis. That expansion changes the purpose of collection and the expectations surrounding it.

Governance should address who can search video, which attributes are searchable, how long metadata remains available, and when information can be shared.

The AI risk framework from the National Institute of Standards and Technology offers a general structure for governing, mapping, measuring, and managing AI risks.

It is not a product certification and does not prescribe one physical security architecture. However, it gives buyers a better starting point than relying on a vendor’s feature description.

Cybersecurity is another reason for caution. Internet-connected cameras and access systems sit at the boundary between digital networks and physical spaces.

A compromise can expose footage, device controls, identity information, or building access workflows. Centralized management can improve patching but also creates a valuable target.

Buyers should examine encryption, authentication, administrative logging, software updates, vulnerability response, and third-party testing. AI performance cannot compensate for weak platform security.

Organizations must also prepare for model changes. A vendor may improve or replace an analytic model through a software update.

That update might change alert volume or classification behavior. Security teams need release information, testing procedures, and ways to monitor unexpected changes.

The skeptical interpretation is not that AI lacks value. The evidence shows strong demand and many practical uses.

The uncertainty concerns how quickly those uses can become dependable institutional processes. A product can be technically capable before an organization is operationally prepared.

That is the adoption gap the industry must measure. It sits between activating a feature and accepting responsibility for the decisions that follow.

Physical Security AI Is Moving From Search to Response

The next phase will connect AI-generated observations with actions, making governance more important than feature counts.

Early video analytics often focused on motion detection, object classification, or recorded-footage searches. Those uses helped operators find relevant material after an event.

Newer systems combine video with access records, alarm signals, intercoms, and environmental data. This allows an operator to investigate one event across several systems.

The value is easy to see in a multi-site business. An unusual door opening can be linked with video, a person’s movement, and subsequent alarms.

An operator can then review a connected sequence instead of switching between several consoles. AI can help rank the evidence and reduce time spent assembling it.

Retail provides another practical case. Teams can investigate after-hours activity, loading-area events, suspected theft, or unusual movement across multiple stores.

Manufacturing sites can monitor restricted zones, safety equipment, vehicle movement, or environmental conditions. Schools can use connected alerts to assess events across entrances and common areas.

Healthcare environments bring different constraints. Security teams might need fast incident reconstruction while protecting patient privacy and limiting access to sensitive footage.

These examples show why vendors want unified platforms. The more contextual signals available, the more useful an analytic result can become.

They also show why deployment is difficult. A useful system needs accurate data, consistent naming, reliable clocks, appropriate camera placement, and clearly assigned responders.

The technology’s direction is moving from observation to orchestration. An AI system does not merely identify an event. It proposes or initiates the next step.

Verkada’s 2026 predictions described agentic AI as an active partner for operators. Agentic AI refers to systems designed to pursue a goal through multiple actions, rather than produce one isolated prediction.

In security, that might involve identifying an event, gathering associated footage, notifying an operator, and recommending an escalation path.

The term carries more ambition than established evidence. Organizations should treat each action as a separate control point.

A model that reliably locates footage is not automatically qualified to decide whether police should be contacted. The second task involves policy, context, and consequences beyond visual classification.

Human review therefore remains essential, but “human in the loop” needs a precise meaning. A distracted operator approving dozens of recommendations provides little substantive oversight.

Teams must know what information the operator sees, how much time is available, and whether the interface encourages independent judgment.

They should also measure automation bias, the tendency to defer to a machine-generated recommendation. High-confidence interface language can influence operators even when the underlying model remains uncertain.

Training must include failures, not only ideal demonstrations. Operators should practice handling ambiguous detections, missing context, contradictory signals, and system outages.

Organizations also need fallback procedures. Physical security cannot stop when a cloud connection or AI service becomes unavailable.

A mature deployment should define which devices continue operating locally, which alerts remain available, and how teams investigate incidents during an interruption.

These requirements will shape the competitive market. Vendors that provide clear controls, audit trails, testing tools, and graceful fallback modes can reduce adoption friction.

Those capabilities may prove more important than another headline feature. Security leaders need systems that remain understandable during stressful events.

AI adoption will also depend on procurement collaboration. Physical security, IT, cybersecurity, privacy, legal, and frontline operations should participate before deployment.

That process takes longer than a departmental purchase. It also reduces the chance that a technically successful pilot fails during organization-wide review.

The market is entering a phase where integration quality will separate demonstrations from durable systems. Search tools introduced many teams to AI’s value.

Response automation will determine whether those teams trust AI with more consequential work. That transition will be slower, more regulated, and more revealing.

Three Signals Will Show Whether the Gap Is Closing

The strongest evidence will come from transparent methodology, sustained feature use, and independently measured operating results.

The first signal is a complete publication of the new Verkada study’s methodology and findings. The industry needs more than the 2,700-leader headline.

A useful report would identify respondent roles, regions, sectors, company sizes, and deployment types. It would publish the questions used to define AI adoption.

The report should also separate pilots from production deployments. It should distinguish retrospective search from real-time detection and automated response.

If Verkada releases that detail, the adoption-gap claim becomes easier to evaluate and compare. If it remains limited to headline statistics, uncertainty around the market’s maturity will persist.

The second signal is evidence that customers keep AI features active after initial trials. Sustained use matters more than activation.

Vendors should report anonymized measures such as repeat usage, alert disposition, investigation completion, and human overrides. Those figures require careful privacy protection and consistent definitions.

Customer case studies can help, but they should include baseline conditions. A claim that investigations became faster means little without explaining the previous process and measured interval.

Evidence from several industries would be stronger than one ideal customer. Retail, education, manufacturing, healthcare, and public-sector environments impose different demands.

If repeated operational use expands across those settings, the vendor-ambition versus buyer-trust gap is narrowing. If usage remains concentrated in search and pilot projects, the gap remains substantial.

The third signal is how competitors and standards-focused buyers respond. Genetec already reports rising AI interest alongside concerns from 70% of surveyed end users.

Watch whether vendors compete on explainability, deployment flexibility, audit controls, and documented model performance. Those areas indicate that buyer concerns are influencing product design.

Also watch large procurement documents and regulated deployments. Requirements for logging, data location, testing, human approval, and fallback operation reveal what serious customers consider nonnegotiable.

A shift toward independent evaluations would strengthen confidence. Vendor-sponsored surveys are useful for identifying questions, but third-party testing is better suited to validating answers.

The next one to three months should therefore produce more than additional AI announcements. The meaningful developments will explain how systems perform after deployment.

Security leaders evaluating the google news story should ask direct questions. What exact workflow does the AI improve? What decision remains human? How are errors measured?

They should ask what data leaves the site, how long derived metadata remains available, and whether the system’s behavior changes after updates.

They should also define success before a pilot begins. Useful measures include investigation time, verified alert rate, operator workload, escalation accuracy, and service availability.

A pilot should include difficult conditions and known failure cases. It should test the intended location rather than rely exclusively on a prepared demonstration.

Most importantly, organizations should avoid treating caution and adoption as opposites. Careful governance can support broader use by making responsibilities clear.

The Verkada study headline captures a real market tension, even while its latest public details remain limited. AI capabilities are becoming easier to buy than to institutionalize.

The winners will not be determined solely by who ships the most features. They will be determined by who makes those features measurable, controllable, and dependable.

That is the standard readers should apply when the next physical security AI claim appears in google news. Ask for the methodology, inspect the workflow, and measure the outcome before expanding the system.