Security
No Smoke And Mirrors:
What AI Adoption Really Means For Physical Security By Chris Russell, Chief Transformation Officer, The Keyholding Company
AI is making waves; it’s tantalising, exciting, and disconcerting all at once. In physical security, it promises new levels of service, but raises real questions about privacy, data and control.
The upside of AI is huge, but only if done right. The headline message? Tread carefully, but keep moving forward. If you don’t, you can be sure your competitors will.
Having spent many years at The Keyholding Company (TKC) thinking of new ways to transform how we operate, here’s what I think successful AI adoption really looks like for our industry.
Pressure Is Mounting To Adopt AI In Physical Security The industry is notoriously slow-moving when it comes to tech innovation. Many organisations still rely on manual, fragmented processes, where email chains and spreadsheets are still far too common.
The result is poor visibility, inconsistent reporting, and friction across service lines – especially in areas like guarding, where human error and low efficiency remain a challenge.
It can’t - and won’t - go on like this. Clients now expect smarter, more transparent services, and larger buyers are demanding it. Traditional providers are under real pressure to modernise - and fast.
From day one of my time at TKC, I’ve loved our commitment to use technology to push the boundaries of what’s possible in physical security. But AI isn’t a bolt-on; it requires a fundamental rethink of how you operate. Like any transformation, the risks are as real as the opportunities.
As TKC’s Chief Transformation Officer, my role is to help us navigate that shift. Here’s what I’ve learned:
AI Should Be Applied Where It Actually Solves Problems
Many companies want to adopt AI but face daunting questions like ‘where do I begin?’, ‘how should we implement it?’ or ‘what’s going to have the biggest impact?’
The biggest misconception about AI in security is that it replaces humans. In reality, the smartest applications enhance human performance, not eliminate it. The right approach is to find repeatable, high-volume, minimal subjectivity tasks and let AI do the heavy lifting. So, what does that look like in practice?
Example 1: Photo verification on job reports At TKC, we carry out 1.2 million mobile security jobs yearly. On every job, security officers take multiple photos on our mobile app. These images need to be validated for quality, location, context, and content – was the uniform correct, was the photo taken on-site, was the right room captured etc.
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