The transportation industry finds itself at a crucial moment when generative artificial intelligence (AI) holds immeasurable promises to operational change, but many logistics companies are uncertain about how to effectively utilize these emerging technologies. Rather than viewing AI as a wholesale exchange of existing infrastructure, the most successful organizations have discovered that there is real value in enhancing and optimizing current freight technology systems and processes.
The latest logistics business relies heavily on established transport management systems, visibility platforms, and carrier onboarding processes that have proven sophisticated values over the years. AI should not be seen as a threat to these underlying systems, but as a strong augmentation that can address certain operational challenges while maintaining existing investments.
“AI is not a standalone alternative. It's augmenting our core cargo technology platform,” said Michael Hane, Director of Product Marketing at Descartes.
This integrated philosophy allows businesses to systematically tackle the bottlenecks that have long plagued their logistics business, while still maintaining proven workflows.
When implemented thoughtfully, AI will transform the way transportation management systems handle daily communications, consume valuable staff time and dramatically reduce manual data entry that introduces human error. Visibility tools gain enhanced prediction capabilities, provide more accurate estimated arrival times and better exception management, reduce manual tasks such as check calls, and fix data errors. Career onboarding systems can process documents with unprecedented speed and accuracy, streamlining what was traditionally a labor-intensive process.
The flood of AI solutions explosions in the logistics market creates both opportunities and disruption. New tools emerge every day, each with promising innovative improvements, making it increasingly difficult for businesses to identify which technologies deserve investment and attention.
Hahn advocates a disciplined approach to this challenge.
“Logistics companies should start with AI applications that solve clear problems in their current workflows rather than chasing all the new shiny AI tools,” he said.
This methodology focuses on identifying manual repetitive tasks where automation can provide immediate, measurable benefits.
Communication workflows represent the particularly fertile ground of AI implementations. Automating regular exchanges with customers and suppliers will quickly lead staff to focus on higher value activities, reducing errors that inevitably creep into manual processes. Similarly, tasks like order entries, tracking updates, and basic customer service enquiries can benefit from AI assistance without the need for a complete system overhaul.
In addition, companies should consider leveraging existing vendor relationships when investigating AI capabilities. Working with current technology providers developing AI roadmaps, there are several advantages, including established support structures, proven implementation methodologies, and a deeper understanding of existing workflows. As AI continues to mature and specialize, it becomes increasingly valuable to have a tightly integrated roadmap between management systems and AI capabilities.
Integrating AI into a mature technology stack creates unique failures that require careful navigation. With the vast amount of AI startups and marketing noise, it is difficult to identify vendors with authentic transportation industry expertise and sustainability.
“The best place to get started is to leverage existing relationships with current technology providers to understand AI strategies, discuss issues, and develop solutions that are useful for your business,” says Hane.
Established vendors typically provide mature customer support and implementation services for combat exams where new market entrants cannot match. Additionally, these providers already understand existing systems and operational nuances, reducing implementation risk.
Internal resistance represents another important hurdle. Team members who have improved their manual processes over the years may be skeptical of AI-driven changes, especially if they feel they are excluded from the implementation process. The early involvement of operational staff ensures that workflows are mapped accurately and build confidence in your AI systems. Once employees understand how AI tools work and trust the output, they are much less likely to guess the results of the results second or perform unnecessary manual validation.
The challenges of technical integration must also be noted. Legacy systems may need to be modified to accommodate AI workflows, and data quality issues that are easy to manage with manual processes can be magnified when automation is involved. Successful implementations usually include thorough data auditing and cleanup before AI deployment.
Calculation of ROI in AI implementations follows the same basic principles as other technology investments, but requires careful selection of appropriate metrics and establish a clear baseline before deployment begins.
For increased labor productivity, relevant key performance indicators (KPIs) include controlled load per employee, order entry error rate, perfect tracking, customer satisfaction scores, and freight invoice mismatch. Freight securities companies often track the percentage of digital cargo coverage, enter it electronically, and measure the amount of cargo that is automatically covered and completed without human intervention.
The key to accurate ROI measurements is to establish a comprehensive baseline before AI implementation begins. This preparation will allow for accurate tracking of workforce savings, operational costs, penalties reductions, and overall improvements in customer service. Without these baselines, companies have a hard time quantifying their actual impact on AI operations.
Companies also need to consider qualitative benefits that may be more difficult to measure, but which contribute significantly to overall value. Increased employee satisfaction by eliminating boring tasks, improved customer experience by faster response times, and increased operational resilience by reducing reliance on manual processes all contribute to long-term business value.
To avoid the pitfalls of the technical hype cycle, we need to maintain a problem-focused approach to AI adoption rather than a technology-focused approach. This discipline begins with an honest assessment of operational bottlenecks that specifically address the identified problems and evaluation of solutions.
“Companies must start by identifying actual operational needs or bottlenecks and evaluating solutions that specifically address those areas,” Hane said. “This ensures that AI adoption is not afraid to miss out on the latest trends, with the value it offers to customers, employees and other stakeholders.”
Strategic recruitment also includes careful partner selection. Working with established technology providers who understand the dynamics of the transportation industry, we provide stability and expertise that startup vendors often cannot match. When AI capabilities are built into or tightly integrated into a proven execution system, they will more naturally align with the sales needs and profits of continuous updates and support from experienced teams.
A recent Descartes Transport Management Benchmark Survey of over 600 companies shows that an overwhelming 96% of respondents employ and use it within their operations.
Successful AI adoption requires thorough initial work to map existing workflows and identify the best integration points. This investment pays dividends by ensuring complementary to AI solutions rather than disrupt the established processes that already provide value.
As AI continues to reconstruct the logistics landscape, the most successful implementations are those that enhance the existing freight technology infrastructure, rather than replace it. By focusing on specific operational challenges, measuring concrete outcomes and partnering with established technology providers, transport companies navigate the AI revolution and bringing concrete profits into operation.
The future of cargo technology is not about choosing established systems and AI capabilities, but rather about thinking integration of these powerful new tools into workflows that promote excellence. Companies that approach AI adoption with strategic discipline, clear metrics and strong partnerships are best positioned to capture the potential of their transformation, avoiding the confusion that comes with chasing any technological trend.
For more information about Descartes, click here.
How to use AI workflows in cargo technology first appeared in FreightWaves.
