Why Businesses Are Investing in AI, and What They’re Actually Buying
Companies are rushing to add AI to their products, and most of what gets sold under that name is a relabeled feature, not new capability. Here’s why the rush is happening, and three questions that show you which one you’re being sold.
Ask executives why their company is investing in AI, and the answer is often fear of being the company that sat out the latest technology race. Boards want an AI story, and investors and customers ask for one. Under that pressure, money flows into two buckets: investment that makes a business run better, and investment that makes a business sound current. Read on to learn how to separate the two before committing to your next AI investment.
What’s Driving Business Investment in AI?
Three forces are driving AI investment:
The first is fear of being left behind. Few leadership teams want to explain why they sat out the latest technology race.
The second is that data feels safe. A number on a dashboard appears objective, shapes strategies, and informs decision-making.
The third is that “AI investment” carries value. Executives afraid of being left out of the AI race choose vendors claiming to be “AI-powered” when in practice they relabeled a product they sold unchanged for years. In practice, what those buyers receive is a “mirage” of AI, or so-called “AI-washing” (QSR Magazine).
Why an AI Label Reads as Credibility
For much of the twentieth century, psychology struggled to be taken seriously as medicine; talking with a patient looked soft next to surgery and lab results. Medication changed the field because a pill is something physical you can prescribe and measure. Treatment did not suddenly improve on the day the pills arrived. Instead, the field looked like medicine at last, so it was treated as such.
Data and AI are playing a similar role for businesses. A company with models and dashboards looks like a serious, modern operation to its board and investors. That credibility, and the confidence it buys, is powerful. But it is not the same thing as running a better business.
Three Questions to Ask Before You Invest in AI
These are the questions we put to technology vendors for our and our clients’ businesses:
- Who is going to watch the data? Every new platform produces output: reports, dashboards, and alerts. Someone in your business has to read that output and act on it. If the answer is “the managers on top of everything else they are doing”, then the data may go unread and unused. Budget for your team’s time along with the platform, or hold off on the platform.
- Does the AI do what the marketing claim says? 79% of tech workers admit to pretending they know more about AI than they do, and 95% of generative AI pilots were failing in 2025 (IT Pro, MIT). Some of the “new AI” on the market is a visual rebrand of software that has worked the same way for years. So put a before-and-after question to the vendor: what did this product do two years ago, what changed under the hood, and what can it do better today?
- Is it faster and better than the person doing the job now? Put the tool against your best person on the same task, price up the difference, and evaluate whether the investment is worth the potential return.
How We Use Data, and Where the Analysts Come In
We run proprietary software and use data to point our analysts in the right direction. The software maximizes our analysts’ time to spot findings. They watch people and analyze behavior, then decide what a leadership team needs to see.
Human oversight still matters in loss prevention with data for direction and humans for judgment, just as it does in Trend-Based Monitoring as well. The industry term is “human in the loop”, but we prefer it the other way around: the analyst leads and the software is in the loop.
Invest to Operate Better, Not Just to Be Current
The businesses getting real value from data and AI are the ones that invested to improve how they operate. They named the job the tool would do, the person who would act on its output, and the number that would prove it worked. If the job you are pricing is watching your locations and turning camera footage into decisions, that is the work our analysts do every day. See how our loss prevention service works, or book a call to discuss how it can benefit your operation.
What Businesses Ask Us About AI and Data
What is AI washing?
AI washing is the practice of marketing a product as AI when little or none is involved: relabeling existing software as “AI-powered,” wrapping a third-party chatbot around an old tool, or claiming machine intelligence for work that people still do. The label matters to buyers because it now carries a price premium. If a vendor cannot explain what their AI does that the previous product did not, the label is doing the work.
How can we tell if a vendor’s AI claims are real?
Ask what the product did before the AI label appeared and what it can do now that it could not do then. Then ask which decisions the system makes on its own and which a person makes. Vendors with real capability answer in specifics and will demonstrate the difference on your data. Vendors selling a label steer the conversation back to the roadmap.
Is it a mistake to invest in AI right now?
No. The shift is happening and sitting it out entirely carries its own cost. The mistake is investing for the boardroom slide rather than the operation. Invest where a tool removes real work or delivers operational efficiency you can measure, and where someone in the business is resourced to act on what it produces.
Does Pembroke use AI?
Pembroke runs proprietary software, built in-house, that helps its analysts work faster by surfacing the right footage and saving review time. Every judgment in a Pembroke report is made by a trained analyst. In footage review specifically, Pembroke’s testing keeps reaching the same result: a person remains faster and more accurate than the models.