The transportation industry finds itself at a pivotal moment where generative artificial intelligence (AI) holds immense promise for operational transformation, yet many logistics companies remain uncertain about how to effectively harness these emerging technologies. Rather than viewing AI as a wholesale replacement for existing infrastructure, the most successful organizations are discovering its true value lies in enhancing and optimizing their current freight technology systems and processes.
Modern logistics operations rely heavily on established transportation management systems, visibility platforms, and carrier onboarding processes that have proven their worth over years of refinement. AI should not be seen as a threat to these foundational systems but rather as a powerful augmentation layer that can address specific operational challenges while preserving existing investments.
“AI is not a standalone replacement—it augments core freight technology platforms,” said Michael Hane, Director, Product Marketing, Transportation Management at Descartes.
This integration philosophy allows companies to maintain their proven workflows while systematically addressing bottlenecks that have long plagued logistics operations.
When implemented thoughtfully, AI can transform how transportation management systems handle routine communications, dramatically reducing the manual data entry that consumes valuable staff time and introduces human error. Visibility tools gain enhanced predictive capabilities, offering more accurate estimated time of arrivals and better exception management, as well as reducing manual tasks like check-calls, and fixing data errors. Carrier onboarding systems can process documentation with unprecedented speed and accuracy, streamlining what has traditionally been a labor-intensive process.
The explosion of AI solutions flooding the logistics market creates both opportunity and confusion. New tools emerge daily, each promising revolutionary improvements, making it increasingly difficult for companies to identify which technologies deserve investment and attention.
Hane advocates for a disciplined approach to this challenge.
“Logistics companies should start with AI applications that solve clear pain points in their current workflows rather than chasing every shiny new AI tool,” he said.
This methodology focuses on identifying manual, repetitive tasks where automation can deliver immediate, measurable benefits.
Communication workflows represent particularly fertile ground for AI implementation. Automating routine exchanges with customers and suppliers immediately frees staff to focus on higher-value activities while reducing the errors that inevitably creep into manual processes. Similarly, tasks like order entry, tracking updates, and basic customer service inquiries can benefit from AI assistance without requiring complete system overhauls.
Companies should also consider leveraging their existing vendor relationships when exploring AI capabilities. Working with current technology providers who are developing AI roadmaps offers several advantages: established support structures, proven implementation methodologies, and deeper understanding of existing workflows. As AI continues to mature and specialize, having tightly integrated roadmaps between management systems and AI capabilities becomes increasingly valuable.
Integrating AI into mature technology stacks presents unique obstacles that require careful navigation. The sheer volume of AI startups and marketing noise makes it challenging to identify vendors with genuine transportation industry expertise and staying power.
“A great place to start is by leveraging your existing relationships with your current technology providers to understand their AI strategy, discuss your pain points, and develop solutions that will work for your business,” Hane said.
Established vendors typically offer mature customer support and battle-tested implementation services that new market entrants cannot match. Moreover, these providers already understand existing systems and operational nuances, reducing implementation risks.
Internal resistance represents another significant hurdle. Team members who have refined manual processes over years may be skeptical of AI-driven changes, particularly if they feel excluded from the implementation process. Early involvement of operational staff ensures workflows are accurately mapped and builds confidence in AI systems. When employees understand how AI tools function and trust their outputs, they are far less likely to second-guess results or perform unnecessary manual verification.
Technical integration challenges also require attention. Legacy systems may need modifications to accommodate AI workflows, and data quality issues that were manageable with manual processes can become magnified when automation is involved. Successful implementations typically involve thorough data auditing and cleanup before AI deployment.
Calculating ROI for AI implementations follows the same fundamental principles as other technology investments, but requires careful selection of appropriate metrics and establishment of clear baselines before deployment begins.
For labor productivity improvements, relevant Key Performance Indicators (KPIs) include loads managed per employee, order entry error rates, loads with perfect tracking, customer satisfaction scores, and freight invoice discrepancies. Freight brokerages often find value in tracking digital freight coverage percentage, measuring the volume of shipments that enter electronically and get automatically covered and completed without human intervention.
The key to accurate ROI measurement lies in establishing comprehensive baselines before AI implementation begins. This preparation enables precise tracking of improvements across labor savings, operational costs, penalty reductions, and customer service enhancements. Without these baselines, companies struggle to quantify AI’s actual impact on their operations.
Companies should also consider qualitative benefits that may be harder to measure but contribute significantly to overall value. Improved employee satisfaction from eliminating tedious tasks, enhanced customer experience through faster response times, and increased operational resilience through reduced dependence on manual processes all contribute to long-term business value.
Avoiding the pitfalls of technological hype cycles requires maintaining a problem-focused rather than technology-focused approach to AI adoption. This discipline begins with an honest assessment of operational bottlenecks and evaluation of solutions that specifically address identified pain points.
“Companies should begin by identifying real operational needs or bottlenecks and then evaluate solutions that specifically address those areas,” noted Hane. “This ensures AI adoption is driven by the value you deliver to your customers, employees, and other stakeholders, not by fear of missing out on the latest trend.”
Strategic adoption also involves careful partner selection. Working with established technology providers who understand transportation industry dynamics provides stability and expertise that startup vendors often cannot match. When AI capabilities are built into or tightly integrated with proven execution systems, they align more naturally with operational needs and benefit from ongoing updates and support from experienced teams.
According to Descartes’ recent transportation management benchmark survey of over 600 companies, an overwhelming 96% of overall respondents indicated they have adopted generative AI and are using it within their operations
Successful AI adoption requires thorough upfront work to map existing workflows and identify optimal integration points. This investment pays dividends by ensuring AI solutions complement rather than disrupt established processes that already deliver value.
As AI continues reshaping the logistics landscape, the most successful implementations will be those that enhance rather than replace existing freight technology infrastructure. By maintaining focus on specific operational challenges, measuring concrete results, and partnering with established technology providers, transportation companies can navigate the AI revolution while delivering tangible benefits to their operations.
The future of freight technology is not about choosing between established systems and AI capabilities but about thoughtfully integrating these powerful new tools into the workflows that drive logistics excellence. Companies that approach AI adoption with strategic discipline, clear metrics, and strong partnerships will be best positioned to capture its transformative potential while avoiding the disruption that comes with chasing every technological trend.
