Sensor and Location Data Used by Under 10% of Companies – How Growth Companies Open Device Data to AI Agents

Smart device and sensor data served as a data source for only 9% of companies that run data analytics with their own staff. Location data from vehicles or portable devices was used by even fewer, 7% (Statistics Finland, ICT Usage in Enterprises 2025, published 27 Nov 2025). Device data use in AI is barely getting started — even though devices generate data constantly, it rarely reaches analytics or an AI agent.

Your growth company's production line, vehicles, or warehouse likely already has sensors, GPS tracking, or smart devices. Data flows from them constantly, but it goes to waste: it stays in the device's own memory, a separate monitoring dashboard, or a vendor's cloud service nobody checks regularly. That same data could feed an AI agent that spots anomalies, predicts maintenance needs, or optimizes routes — but no one has built the bridge between the device and the AI.

Why does device data go unused in growth companies?

The problem isn't a lack of devices — it's a lack of integration. Sensors and vehicles often use different protocols, vendor-specific interfaces, or fully closed systems. The data exists, but it's locked into the device manufacturer's own format, and no one has taken ownership of moving it into a shared format that AI can use.

How does a growth company open its device data to AI agents?

The solution is a data foundation layer that connects devices centrally. Master Layer is a data foundation layer that connects your company's existing systems — CRM, ERP, documents, and also sensor and location data from devices — securely for AI use. Its job isn't to replace devices or their vendor-specific systems, but to build one secure path along which data flows from the device to the AI agent.

In practice this means three steps: mapping devices and their data formats, establishing a secure connection to the chosen sources, and building an interface the AI agent can read in real time. Development moves in 3-day sprints — the first device group is made available to AI quickly, not through a months-long integration project.

What's the benefit of combining sensor and location data with an AI agent?

Once device data is available to an AI agent, the agent can act proactively instead of only reporting after the fact. Concrete use cases include:

• Sensor data on a production machine reveals an anomaly before the machine stops — the agent flags maintenance in advance
• A vehicle's location data combined with order data optimizes routes and delivers real-time ETAs to customers
• Temperature and humidity sensors in a warehouse trigger an automatic alert before a batch of goods spoils
• Device usage data supports capacity planning as order volume grows

Where should a growth company start with device data?

Start with a mapping exercise that shows, in euros, where device data creates the most value for your company. Master Plan is an AI strategy sprint that maps out where AI creates the most value for your company — measured in euros. It's also a solid starting point for device data: before you integrate every sensor, map out which device group delivers the biggest benefit first.

The EU Data Act also changed the legal picture: companies now have a clearer right to the data generated by their own devices, even when that device was purchased or leased from another supplier. We covered this in more detail in The EU Data Act Is in Force. Having the legal right to data and the technical ability to use it are two different things — you need both.

What does an AI agent cost when it uses device data?

Cost depends on the number of device groups and the diversity of the data. The sprint model makes cost predictable: development moves in 3-day cycles, and billing happens per completed sprint. The first step is mapping which device group creates the most value — that determines the budget, not the other way around.

Is using device data in AI secure?

Yes, when data is routed through a centralized data foundation layer with controlled access rights. The risk isn't using device data in AI — the risk is data traveling through unmonitored paths with no clear ownership. A centralized interface makes access rights and logging a manageable whole.

Is using device data in AI realistic for a smaller growth company?

Yes, because integration doesn't require connecting every device at once. One device group at a time, in 3-day sprints, produces results quickly without a months-long planning phase. Most of the benefit often comes from one or two device groups, not from integrating the entire device fleet at once.

Device data already exists in your company — the question is whether AI can see it. Book a free Master Mind analysis and find out which device group in your business creates the biggest benefit for an AI agent first.