Who is the customer? What is the opportunity?

Itron Idea Labs Projects

We employ lean startup tactics to learn about the challenges our customers face, quickly build minimum viable products, measure success—or failure—and then repeat until we have a full commercial solution ready to address our customers’ needs.

Featured Projects

Because we operate under an agile methodology, Itron Idea Labs is always looking for and pursuing potential projects. Here are a few of our current, featured projects.

Vehicle to Grid, Idea Labs


With the rapid rise in the adoption of electric vehicles, there has been much talk about EVs and vehicle-to-grid (V2G) solutions, but do residential customers want it?

Our social media ads, surveys and interviews found that U.S. customers are primarily interested in V2G for home power backup during outages.

Earth from space at night

NM IoT MVNO Service

Smart devices need a cost effective and streamlined way to connect their sensors for data collection and analysis in the field. With our connectivity expertise, we’ve packaged a plug-and-play NB IoT MVNO service and software development kit (SDK). Using a cloud environment with standardized and interoperable communication protocols, we simplify device registration, accelerate deployment and deliver two-way communications for swift, powerful data aggregation and analysis.

Smartest Sensor

The Smartest Sensor

Smart cities are a hot topic. Expansive IoT networks with flood sensors and air quality sensors have enormous value, but a city's citizens are its smartest sensors!

Interviews with cities and surveys of their citizens have shown us that they don't have the proper tools to communicate with each other. A truly smart city starts with connecting and empowering its citizens.

Consumer Collect

Consumer Collect

Despite advances in AMI, reading the 2.3 billion non-communicating meters worldwide is expensive and error prone. In-depth interviews and iterative field trials are proving the value of automating the collection of meter reads using trained neural network models running on the consumer’s mobile device.