"I don't care about your security system," says your CFO
Tell finance a new system reduces incidents and watch their eyes glaze. Show them the black holes in the balance sheet and they sit up. Parker Miller on selling video AI internally.
If you want finance to take notice, show them black holes draining the balance sheet. If you want their eyes to glaze over, tell them your new security system reduces incidents.
In an agentic video system, cameras are endpoints collecting intelligence for agents to act on. But that distinction is not always clear to the person approving the budget. VP-level buyers may see video AI as "more camera spend" even when nobody is asking for cameras. What is being proposed is an investment in the intelligence layer, which can find missed opportunities across loss prevention, security, safety, and operations.
Those problems are usually worse than leaders realise. Parker Miller, Spot AI's VP of Partnerships, has seen enterprises unlock as much as $15 million in savings from agents on cameras they already owned. Dangle that in front of a decision maker and you have a more tantalising proposition than "let's spend thousands on cameras, again."
You're not updating cameras. You're activating a digital workforce.
When executives consider a video AI system they ask security questions. Does it reduce incidents? Does it catch shoplifters? Does it replace guards? The answer is yes, but that framing is narrow: it is about replacing human abilities, not surpassing what a team could ever do on its own.
Announcing a camera refresh won't get org buy-in. If you start talking about stopping vehicle break-ins or POS transaction fraud, leaders sit up and take notice.
Frame video AI as a security cost and you get a security-sized budget. Tell a story about revealing hidden losses and codifying process knowledge, and leaders get interested. Parker's three moves:
1. Start from a hard-to-measure loss
Every organisation has financial black holes. Leaders know internal theft or SOP adherence is a problem, but if they cannot quantify it they cannot address it. Find where finance is already relying on guesswork. In retail, that is a shrink number they are already tracking. In manufacturing, it is the process where line-down events are most frequent and least documented. That is your baseline, and video will show whether the company has been under- or over-investing.
Once we get a real idea of what's happening by pulling in video data, it's often about three times worse than they think it is.
2. Follow the data to growth
When you report back, skip the table-stakes outcome of incident prevention and go deeper. Build pilots that expand from the first use case. In retail, move from deterring loiterers to internal theft at the register, verified on video. In manufacturing, move from missing PPE to the practices that keep the safest teams compliant. In construction, from recording unauthorised entry to stopping it.
When it becomes clear that video AI isn't just a security system, that it's saving the company a ton of money, people across teams start getting interested.
3. Build process expertise over time
Watching for exceptions is the human model. With video AI you can monitor everything and learn from it. Encode new standards based on what actually works: which shifts and seasons need more staff, which lines perform best and why, where loading bottlenecks sit, whether a site is on plan. Organisations treating cameras as a compliance tool will spend the next decade catching up. Those who use them to define "good" now will compound the advantage.
You're either on attack mode or on the back foot. Where do you want to be?