In light of these ongoing challenges, could artificial intelligence be the solution? By automating repetitive tasks—such as invoice validation, exception alerts, and document processing—AI has the power to streamline workflow, reduce errors, and free teams to focus on strategic decision-making.
A recent report from research firm Deep Analysis, sponsored by document automation company Hyperscience, highlights the current state of AI readiness in transportation and service back-office functions. The report, titled “Market Momentum Index: AI Readiness in Transportation and Logistics Back-Office Operations,” is based on the results of a survey conducted among transportation and logistics professionals to uncover the challenges and opportunities related to automation and AI adoption. (For more information about the research and methodology, see the sidebar, “About the Research.”)
This article summarizes some key findings and provides some actionable recommendations for supply chain professionals looking to harness the power of AI to enhance efficiency and competitiveness.
The critical role of the back office
Back office operations are the administrative core of supply chain operations, and include tasks such as order processing, inventory management, invoicing, compliance documentation, and communications with vendors and carriers. Although these tasks are not visible to end consumers, they are vital to maintaining the smooth flow of goods and ensuring on-time delivery. Furthermore, these processes are typically complex, involve many transactions and partners, and as a result, often suffer from fragmented processes, duplicate efforts, and inconsistent data. However, most transportation and logistics companies still rely on manual processes and paper-based systems for their office operations, which often lead to errors, delays, and inefficiencies.
For example, the industry relies heavily on documents such as invoices, bills of lading, shipment tracking forms, and compliance records. However, many organizations use manual or semi-automated processes to manage these documents. Survey respondents indicated that manual handling of supply chain documents is a major challenge that can have a significant impact on overall supply chain efficiency (see Figure 1). For example, missing or incorrect paperwork can cause customs delays, fines, or disrupt critical supply chain timelines. In addition, document processing involves multiple touchpoints, which increases the risk of errors and operational delays. Furthermore, the lack of standardized document formats complicates data exchange and collaboration.
The survey found that many companies have implemented digital tools such as enterprise resource planning (ERP) systems, supply chain management (SCM) systems, transportation management systems (TMS), and warehouse management systems (WMS). These systems were initially marketed as comprehensive solutions capable of automating business processes, improving efficiency, and providing real-time data visibility. However, its effectiveness has been limited by several key challenges. First, high implementation costs and complex integrations often lead to partial deployments, where critical functions remain unautomated. Second, rigid system architectures struggle to adapt to dynamic business needs, forcing employees to rely on manual solutions — especially in Excel — to fill functional gaps. This reliance on spreadsheets results in high data entry error rates, inconsistent reporting, and limited data visualization capabilities. Additionally, ineffective user training and resistance to change hinder the adoption of these systems, leaving many organizations unable to take full advantage of these systems. As a result, despite their potential, ERP, WMS, and similar tools often fail to deliver the operational transformation they promise.
Growing interest in artificial intelligence
Given the lack of success with other technology tools, there is a perception that supply chain organizations in general – and transportation and service companies in particular – may be resistant to or uninterested in AI. It was therefore a bit surprising that the survey results indicated a growing interest in automation and artificial intelligence in the transportation and services sector. More than 70% of respondents expressed a desire to invest in improved AI systems, recognizing the potential of these technologies to transform back-office operations.
Among those respondents whose organizations were already using AI, 98% said they viewed the technology as useful, important, or vital. As Figure 2 shows, these participants are currently using AI to achieve a wide range of goals. The report highlights several key areas where AI adds value, such as:
1. Improving the decision-making process (31%): AI can analyze large amounts of complex data — such as real-time traffic patterns, weather conditions, shipment tracking, and historical trends — to improve supply chain decisions.
2. mistake discount (28%): For back-office tasks such as data entry, invoice processing, and document management, AI can automate repetitive processes, significantly reducing human errors.
3. Improve data quality (37%): AI improves data quality by ensuring consistency, standardization and accuracy, making data more reliable for decision-making purposes.
Going forward, automation and artificial intelligence have the potential to reshape the industry, enabling companies to reimagine workflows, prioritize sustainability, and enhance collaboration.
Barriers to the adoption of artificial intelligence
Despite the clear potential of AI, significant barriers to its adoption remain. Survey respondents expressed several concerns about applying AI in back-office operations (see Figure 3). The most common concerns include:
1. Data security and privacy (54%): Transportation and logistics companies handle a large volume of sensitive data, including customer information, shipping details, and payment records. Ensuring strong security protocols and compliance with privacy regulations is critical for any AI application.
2. Implementation cost (51%): AI technologies require significant upfront investments in both hardware and software, and many small logistics companies or those with limited margins may find it difficult to justify these expenses.
3. Integration with existing systems (47%): Many logistics companies still rely on traditional TMS and ERP systems that were not designed with AI in mind, requiring large-scale, large-scale investments in infrastructure modernization.
Basic steps
No matter how powerful technology is, its effectiveness in the real business world is only as good as planning and executing a transformation project. As companies look to implement AI, they must ensure they take key steps such as standardizing data formats, investing in workforce training, and fostering industry-wide collaboration. The report concludes with several recommendations for companies looking to adopt AI and automation into their back-office operations, including:
1. Invest in AI trainingProviding training to employees on artificial intelligence tools and systems will help bridge the knowledge gap and increase adoption rates.
2. Focus on gradual implementation: Starting with pilot projects allows companies to evaluate technology return on investment (ROI) and build confidence in AI technologies before deploying them at scale.
3. Develop industry standards: Collaborate with industry groups to create standardized document formats and processing protocols, reducing inefficiencies and errors.
4. Determine integration priorities: Select AI solutions that integrate seamlessly with existing systems, minimizing disruptions during the transition process.
5. Monitor emerging technologies: Stay up to date on developments in AI, such as Intelligent Document Processing (IDP) and Robotic Process Automation (RPA), to stay competitive.
The time is now
The transportation and logistics sector is at a pivotal moment, with significant opportunities to leverage artificial intelligence and automation to address long-standing inefficiencies in back-office operations. While challenges such as integration, cost and training remain, the industry is steadily moving towards broader adoption of digital and AI-based solutions. By addressing these barriers and focusing on increasingly strategic execution, companies can unleash the full potential of automation and AI, driving efficiency and competitiveness in an already increasingly complex and volatile market.
Some may be understandably skeptical of AI’s ability to truly transform back-office operations, especially in light of past failures to digitize paper-based processes. Of course, no technology is perfect, and its effectiveness depends on how well an organization plans and implements it. However, it is important to note that tremendous progress has been made in AI’s ability to read, understand, and process document-based processes. As a result, AI has the ability to do relatively light work on anything from invoices to bills of lading, typically providing much higher levels of accuracy than a human can do manually.
For supply chain professionals, the message is clear: the future of T&L lies in embracing digital transformation, investing in artificial intelligence, and enhancing cross-industry collaboration. It’s time to act.
About search
In 2024, document automation company Hyperscience and the Council of Supply Chain Management Professionals (CSCMP) collaborated with research and consulting firm Deep Analysis on a research project exploring the current state of back-office operations in transportation and logistics, and the potential impact of artificial intelligence. The report, titled “Market Momentum Index: AI Readiness in Transportation and Logistics Back-Office Operations,” is based on the results of a survey conducted by senior managers and executives from 300 companies located in the United States. All of these organizations have annual revenues of more than $10 million and more than 1,000 employees. The survey was conducted in November and December 2024. The full 21-page report can be downloaded for free at https://explore.hyperscience.ai/report-ai-readiness-in-transportation-logics.
