As organizations continue investing in artificial intelligence, many are discovering that selecting an AI platform is only part of the challenge.

Geotab believes the larger issue is ensuring AI has access to trusted, real-time operational data.

The company announced June 17 the launch of its new Model Context Protocol (MCP) Connector, which enables organizations to securely access live MyGeotab data directly within approved AI platforms including ChatGPT, Claude, Microsoft Copilot, and other MCP-compatible environments.

The connector allows small fleet managers, operations leaders, and business decision-makers to ask questions, generate reports, automate workflows, and take action using fleet data without switching between multiple software platforms.

The launch reflects a broader shift in enterprise AI adoption as organizations move beyond experimentation and focus on generating measurable business value from their AI investments.

Why Data Quality Matters More Than AI Models

While much of the market's attention has centered on comparing AI models and platforms, Geotab executives argue that businesses are often focused on the wrong variable.

"The biggest mistake is focusing on the AI tool rather than the data that powers it," said Mike Branch, vice president of data and analytics at Geotab.

Businesses frequently evaluate which AI platform offers the best features or the most advanced capabilities. However, Branch said even the most sophisticated AI model can only perform as well as the information available to it.

"There's a lot of attention on which model or platform an organization should use, but the reality is that even the most advanced AI system can only work with the information it has access to," Branch said.

For fleet organizations, operational information often resides across multiple systems, including telematics platforms, maintenance software, fuel management tools, safety applications, and business intelligence systems. That fragmentation can limit the effectiveness of AI initiatives.

"If fleet data is incomplete, of poor quality, outdated, or disconnected from operational workflows, AI will struggle to deliver meaningful business value, and businesses lose trust," Branch said.

The MCP Connector is designed to address that challenge by securely connecting live fleet intelligence with AI environments that organizations have already approved and adopted.

Building a Strong Foundation for AI

According to Branch, organizations that generate measurable returns from AI will be those that prioritize data quality, accessibility, and governance before selecting specific AI tools.

"The organizations that will see measurable results are the ones that start with a strong data foundation," he said. "They understand where their operational data lives, they trust its quality, and they can connect it across systems and workflows."

When AI can access accurate operational information in real time, it becomes significantly more useful for reporting, forecasting, decision-making, and workflow automation.

"AI becomes much more powerful when it's grounded in accurate, real-time operational information," Branch said.

That philosophy aligns with Geotab's approach to AI development. The company processes approximately 37 trillion data points annually from more than 6 million connected vehicles operating across 160 countries. Those data streams include information related to vehicle health, fuel performance, route efficiency, driver behavior, safety, and asset utilization.

"High-quality data and information are essential for AI solutions to have a measurable impact on business operations," Branch said in the company's announcement. "The value of those decisions depends on access to accurate, timely, and relevant operational data."

An Open Approach to Enterprise AI

A key differentiator of the MCP Connector is its use of the open Model Context Protocol standard.

Rather than requiring customers to adopt a proprietary AI ecosystem, the connector allows organizations to work within their preferred AI environments while maintaining control over security requirements, privacy policies, governance standards, and broader AI strategies.

"Our customers are looking for ways to bring trusted fleet intelligence into the workflows they already use, and not just to answer questions, but to get things done," Branch said. "MCP gives them that flexibility while allowing them to maintain control over their data, AI strategy, and operational processes."

The platform can support actions such as generating reports, creating alerts, scheduling maintenance activities, and automating operational workflows.

For Central Transport, the benefits have already become apparent.

"By integrating the Geotab MCP connector with Claude, we transformed complex fleet data into real-time, actionable intelligence, replacing weeks of manual analysis with instant, high-depth reporting," said Jon Hanvey, director of tractor maintenance at Central Transport.

As enterprise AI adoption matures, Geotab is betting that organizations with the strongest operational data foundations will be best positioned to move beyond AI experimentation and achieve measurable business outcomes.