The AI Tools Are Ready. Is Your Data?

For the past two years, the conversation around artificial intelligence has focused on the tools themselves.

Organizations have been evaluating models, experimenting with copilots and testing new ways of working. The debate has largely centered on which platform is best, which model is most capable and how quickly AI can be integrated into everyday workflows.

As businesses move from experimentation to execution, they're starting to realize they've been focused on the wrong question.

The challenge is no longer whether AI is capable enough. The challenge is whether organizations have the trusted data required to make AI useful.

Today's leading AI platforms are remarkably capable. They can summarize information, generate content, identify patterns and help people make decisions faster than ever before. Yet their effectiveness is ultimately limited by the quality of the information they can access.

If the underlying data is fragmented, outdated, incomplete or disconnected from real-world operations, even the most advanced AI system will struggle to produce reliable outcomes. In many cases, AI will produce an answer quickly and confidently, but speed is not the same thing as accuracy and confidence is not the same thing as truth.

This challenge is becoming increasingly visible as businesses move beyond pilots and proof-of-concepts toward operational deployment. The organizations that will generate the most value from AI won't necessarily be the ones with access to the newest models. They will be the ones that have invested in collecting, organizing and connecting their operational intelligence over time.

At Geotab, we see this dynamic playing out every day.

Few industries generate as much real-world operational data as transportation. Every vehicle produces a constant stream of information related to performance, utilization, maintenance, safety, fuel consumption and driver behavior. Turning that information into meaningful operational insight requires not only scale, but trust.

Today, Geotab processes approximately 37 trillion data points annually from more than six million connected vehicles operating across 160 countries. Over more than 25 years, we've built what we believe is one of the world's largest and highest-quality datasets for connected transportation.

As organizations look for ways to bring trusted operational intelligence into the AI tools they already use, the challenge is no longer collecting data. It's making that data accessible, actionable and connected to the workflows where decisions are being made.

That's one of the reasons Geotab recently launched its new Model Context Protocol (MCP) Connector. Built on an open standard, the connector enables organizations to securely access live Geotab data and our Ace agentic platform directly within AI platforms such as ChatGPT, Claude, Microsoft Copilot and other MCP-compatible environments. More importantly, it allows users to move beyond simply retrieving information to taking action — creating alerts, scheduling maintenance, generating reports and executing workflows without leaving the AI tools they already use.

To me, that's where the next phase of AI adoption begins.

The future of AI will not be built around isolated systems competing for attention. It will be built around connected ecosystems where trusted data can move securely between platforms, enabling people to access information and take action within the workflows that make the most sense for their business.

The use and deployment of AI will look different for every industry but there is one principle that remains the same: AI creates the most value when it is connected to trusted, high quality data.