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ATL
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Atlanta
Digital

 

ATL
Digi

Atlanta
Digital

 

Organizations today face many challenges, including escalating operational costs, stringent safety regulations, environmental sustainability, and the pressing demand for digital transformation. As the industry works to rapidly digitize, traditional methods are proving inadequate, underlining the need for innovative solutions.

Against this backdrop, Info-Tech Research Group published its latest research, “Practical Use Cases for AI in Fleet Management.” This research will equip fleet managers with the knowledge to effectively harness AI and turn the current hurdles into opportunities for efficiency, sustainability, and competitive advantage. The firm’s assembled use cases will guide industry IT leaders through the intricacies of digital evolution in their sector.

fleet management
Info-Tech Research Group’s “Practical Use Cases for AI in Fleet Management” blueprint outlines five factors that IT leaders must consider when developing their approach to AI use across the industry. (CNW Group/Info-Tech Research Group)

“Data is being generated at an exponential rate due to technological advancements, making it nearly impossible for humans to perceive and analyze all the data in a timely manner,” said Kevin Tucker, principal research director at Info-Tech Research Group. “Fleet managers will increasingly need to use AI if they want to keep up with the exponential growth in demand for scalable transportation solutions and infrastructure.”

The research suggests that while implementing AI in fleet management can be complex and challenging, particularly with legacy equipment and unsupported systems, it should not deter organizations from adopting these advances. The firm highlights the importance of moving beyond complacency with outdated tools, advocating for a strategic adoption of AI. This approach involves crafting robust business cases that articulate the potential benefits and long-term value that AI integration brings to fleet management processes, encouraging organizations to overcome operational and technical hurdles for improved efficiency and innovation.

“In fleet management, machine learning, a subset of AI, plays a pivotal role in analyzing and reporting on the vast data sets generated by the telematics system. Its primary capabilities lie in data-driven pattern recognition and offering insightful recommendations,” explained Tucker. “These systems are evolving, with the prospect of providing prescriptive instructions soon becoming a reality. In contrast to older methods such as business intelligence, which relies on knowledge of what questions to ask, AI relies on data to determine the most important factors influencing outcomes and suggest changes that will deliver results.”

In its latest blueprint, Info-Tech highlights the importance for fleet managers to identify AI use cases that meet their specific organizational needs in order to improve operations and offer immediate benefits. Building collaborative partnerships with industry peers, tech companies, and experts is also encouraged to foster mutual learning and guidance.

Five Factors To Consider With AI In Fleet Management

With AI becoming a pivotal contributor to the fleet management solutions market, understanding its impactful delivery is imperative. Info-Tech outlines in the resource the following five factors that IT leaders must consider when developing their approach to AI use across their organization:

Be Intentional: Emphasize clarity about important AI adoption factors, such as policy, transparency, ethics, and accuracy requirements, ensuring these critical factors are at the forefront of decision-making processes.

Identify Embedded AI: Recognize and assess AI integration in products, even when it’s not immediately apparent, directly interactive, or configurable, to ensure informed implementation decisions.

Unmask Invisible AI: Diligently identify AI functionalities hidden within products, ensuring they align with the organization’s strategic goals and risk management framework.

Fix Hallucinations: Develop strategies to manage and mitigate the risks of AI-generated misinformation, prioritizing the establishment of safeguards against deceptive practices.

Enforce Responsible AI: Advocate for the development and deployment of trustworthy AI solutions, fostering an organizational culture educated in and committed to responsible AI use.

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