How AI Can Help Fleet Managers Build Their Own Solutions

Syneos Health Fleet Manager Kristin Leary is exploring the use of AI-powered development tools to create custom operational solutions tailored to specific fleet needs. By leveraging telematics data and predictive analytics, fleet professionals can address unique challenges without waiting for commercial software providers to update their product roadmaps. This shift toward self-developed applications represents a significant evolution in how the fleet management sector utilizes artificial intelligence to improve efficiency and accountability.

Kristin Leary, the fleet manager for Syneos Health, is investigating how emerging artificial intelligence tools can empower fleet professionals to move beyond standard data reporting. In a recent interview, Leary highlighted that as AI becomes more accessible, it offers the potential for fleets to develop their own software applications. This approach allows organizations to solve immediate operational hurdles using their own telematics data and predictive analytics, rather than remaining dependent on the development timelines of external vendors.

One specific application Leary is exploring involves a custom vehicle inspection and accountability tool. This proposed solution would utilize AI and photography to document a vehicle's physical condition and ensure registration compliance. By automating the verification of these details, the tool aims to increase driver responsibility and provide a more accurate record of fleet assets. Such custom-built tools could bridge the gap between generic commercial software and the highly specific requirements of diverse fleet operations.

The broader implication for the fleet management sector is a transition from being passive consumers of technology to active developers of niche solutions. Leary suggests that AI-powered development tools can help fleets create applications that address specific pain points, such as maintenance tracking or safety compliance, with greater agility. As these technologies continue to mature, the ability to integrate real-time telematics with custom AI models may become a key differentiator for fleets seeking to optimize uptime and operational transparency.