Autonomy requires measurability and continuous oversight

18 december 2025

In the first part of this series, we addressed a fundamental question: is AI in video surveillance primarily hype, or a valuable tool? The conclusion was clear. AI is not just a buzzword, but it is also not a “plug-and-play” feature. True value only emerges when technology is applied with professional expertise, realistic expectations, and an understanding of the operational context.

In the second part, we took a closer look at what “mature AI” in video surveillance actually means. We introduced a maturity framework which demonstrated that many solutions get stuck at perception and detection, whereas end-users often expect systems to understand behavior and account for context. This highlighted the gap between expectation and reality—a gap that technology alone cannot bridge.

In this third part, we take the next logical step. As soon as AI moves beyond observing and analyzing to independently preparing decisions or initiating actions, the discussion changes fundamentally. The conversation shifts from what AI can do to what AI is allowed to do. This is the moment when questions about responsibility, risk, trust, and governance become inevitable.

As long as AI is used exclusively to assist in analyzing footage and supporting operators, the impact remains relatively manageable. However, when AI begins to act autonomously—even within predefined boundaries—it directly affects operational processes, legal liability, and societal acceptance. Autonomy is therefore not a technical step, but a strategic choice.

The central question is not whether autonomous AI is technically possible within video surveillance; that question has already been answered. The question now at hand, and the focus of this article, is when it is responsible to permit that autonomy, and under what conditions it leads to genuine value instead of new risks.

Autonomy is a behavioral change, not a technical feature

In many discussions, autonomy is still approached as if it were an additional software feature—as if you simply activate a setting and the system suddenly acts “smart” on its own. In reality, autonomy is fundamentally different. It means that a system determines what is relevant, which situation deserves attention, and what the subsequent step should be, all without direct human intervention.

In video surveillance, this can range from automatically escalating alerts and prioritizing incidents to controlling other systems or bypassing human review for certain events. While this sounds efficient and attractive, it also means that decision-making authority shifts from human to system.

This shift is not a technical modification, but an organizational and cultural change. It touches upon responsibilities, liability, and trust. Therefore, autonomy should never be introduced lightly, regardless of how well the technology performs on paper.

Expectations are outpacing maturity

The desire to move toward autonomous video surveillance rarely comes from just one side. End-users see convincing demos and expect similar results in their own environments. Integrators feel the pressure to follow suit to remain relevant. Manufacturers present increasingly advanced AI capabilities, thereby creating high expectations.

What is often forgotten is that autonomy is not an end in itself; it is the result of maturity. Without reliable perception, deep contextual understanding, and proven consistency over time, autonomy is not progress—it is a risk.

In practice, we see that expectations often exceed what systems can handle in a realistic environment. This does not lead to better security, but to frustration, distrust, and sometimes even the complete deactivation of AI functionality. Not because the AI failed, but because it was asked to do too much, too soon.

Always start with the problem, not the solution

A crucial step skipped in many trajectories is clearly defining the problem one aims to solve. Autonomous AI is only meaningful for processes that are repeatable, clearly defined, and relatively predictable.

In video surveillance, this means fixed scenarios with little room for interpretation. Examples include access procedures outside of business hours, clearly defined violations, or situations where human judgment almost always leads to the same conclusion. In such cases, autonomy can truly add value.

If behavior does not follow fixed patterns and context is constantly shifting, autonomy must be deliberately restricted. The reality of video surveillance is complex; behavior is rarely unambiguous. What is suspicious in one situation may be perfectly normal in another. AI can support, but it should not be forced into decisions for which it lacks sufficient context.

Autonomy requires measurability and continuous oversight

A system that acts autonomously must be demonstrably reliable. This requires measurability—not as a one-off, but structurally. Autonomy without insight into performance is unmanageable.

In video surveillance, this translates to insight into error margins, stability under varying conditions, and clear metrics regarding the impact on workload, response time, and safety. Without these data points, autonomy remains based on assumptions.

Many AI implementations lack this discipline. There is a reliance on limited test results or demo scenarios, while true variability only becomes visible in production. Autonomy without continuous evaluation increases the likelihood of errors that are only noticed once the damage has already been done.

The choice between standard and custom is decisive

As soon as autonomy enters the frame, the choice between off-the-shelf solutions and customization becomes a strategic decision. Standard solutions are often perfectly fine for generic applications but fall short when dealing with complex behavior or specific environments. Customization can solve this but brings higher costs, maintenance requirements, and increased responsibility.

The right choice depends on the problem, not the technology. Sometimes partial automation is more effective and safer than full autonomy. Sometimes support provides more value than independent action. Mature organizations dare to make that trade-off without blindly striving for maximum automation.

Autonomy grows in phases, not all at once

A reliable path toward autonomy always moves in stages: first observing, then advising, then supporting, and only in a later stage, acting autonomously. This is not caution born out of fear, but out of experience.

In video surveillance, this means AI first provides insight, then assists in prioritization, and only much later acts independently within pre-established frameworks. Organizations that skip these steps encounter resistance from operators, misunderstood decisions, and increased risks. Autonomy must be built on trust earned through consistent behavior over time.

Humans remain ultimately responsible

No matter how far AI develops, human responsibility remains essential in video surveillance. This is especially true within Europe, where regulations explicitly require that decisions are explainable and that human oversight is maintained.

The role of the human is changing, however. Operators are shifting from constant observation to supervision, evaluating exceptions, and decision-making in complex situations. AI supports, filters, and prioritizes, but it does not replace human accountability. This is not a limitation of technology, but a prerequisite for acceptance and trust.

Demos convince, production is uncompromising

Many autonomous AI solutions perform excellently in demo environments. Controlled lighting, predictable behavior, and limited variables provide a distorted view of reality. In production, conditions are rarely ideal and almost never constant.

Only after long-term deployment, testing, and fine-tuning does it become clear whether AI should be allowed to act autonomously. This requires a mature implementation approach where errors are allowed to become visible in controlled phases, rather than in critical situations.

The role of IDIS as a knowledge partner

At IDIS, we recognize that the discussion surrounding autonomy is rarely purely technical. Partners and end-users are not looking for quick fixes; they are looking for guidance. They want to understand where autonomy adds value and where it introduces risk.

Our role, therefore, is not to promote autonomy for its own sake, but to structure the conversation. We help define problems, assess maturity, phase implementations, and safeguard responsibility. We do this not from commercial pressure, but from the conviction that well-applied technology strengthens trust in the industry.

Conclusion

Autonomous AI in video surveillance is not a future vision; it is a reality in specific scenarios. At the same time, it is not an obvious next step for every organization. Autonomy demands craftsmanship, discipline, and realistic expectations.

The central question is not when AI can act autonomously, but when it is responsible for AI to do so. The answer lies not in software, but in insight, experience, and the willingness to approach technology as a professional discipline.

Those who take that step build sustainable value. Those who skip it run the risk of confusing autonomy with progress.