Generative artificial intelligence (GenAI) is establishing itself as a strategic ally in areas that require agility, precision, and scalability. In this scenario, companies from various sectors can deploy this tool to transform their logistics and information technology (IT) operations with a focus on tangible benefits such as cost reduction, increased productivity, and improved performance metrics.
According to data from Mordor Intelligence, the global logistics automation market is expected to reach USD 120.63 billion in the next five years due to the digitalization of this sector.
GenAI is moving from being just a text assistant to taking on the role of co-pilot for business operations, said Linika Aoyagui, project manager at Samsung SDS, the technology arm of Samsung Group.
“This tool can elevate IT and logistics processes from reactive to predictive to autonomous mode over time,” he said. The executive said the use of technology enables faster and more proactive decision-making, reduces rework, increases end-to-end visibility and frees up teams to focus on higher-value activities.
For example, among the solutions developed by Samsung SDS are practical applications of GenAI, which have already been deployed in Brazil and other countries. In the IT field, technology is used to automate early-level technical support, incident analysis, documentation, and runbook creation to help developers securely resolve issues.
In the logistics space, GenAI can also be used to generate an intelligent overview of shipment status, account for delays, suggest alternative routes, integrate quotes and deadlines from carriers, and respond to customers based on real data from TMS and WMS systems, Aoyagui explains.
“This vision takes shape on three fronts: accelerating analysis and planning, automating operations, and improving customer experience. In practice, this means applying GenAI to tasks such as demand forecasting, capacity simulation, automated services, data auditing, estimating, and scheduling,” he said.
For managers, technology also helps provide contextual, real-time information to customers, increasing predictability across the chain.
GenAI applications have also proven effective in critical processes such as traceability and demand planning. For example, in inventory management, AI can correlate variables such as sales, promotions, and seasonality to suggest ideal levels by SKU and location, as well as suggest transfers between distribution centers and stores, Aoyagi explains.
“For traceability, the technology integrates data from multiple sources to create a single, easy-to-understand timeline detailing events and risks, which enables proactive actions such as rescheduling delivery windows and accurately notifying customers. For demand planning, GenAI accounts for variation, tests scenarios, suggests adjustments, and accelerates the S&OP cycle by combining statistical models and contextual knowledge,” he added.
The move, which the executive highlighted, reflects a broader trend. According to a McKinsey study, companies around the world are redesigning their workflows and involving senior executives in AI governance.
A study by the Enterprise Strategy Group (ESG) with Hitachi Vantara published on the InforChannel portal shows that 44% of organizations have a clearly defined policy for GenAI, but only 37% believe they are ready in terms of infrastructure and data.
Aoyagui highlighted that some global Samsung SDS platforms, such as Cello (focused on supply chain) and Brity Automation (focused on automation and conversations), already have GenAI capabilities to understand natural language, generate insights, and execute tasks.
“In a regulated environment, this technology operates using secure information retrieval mechanisms (RAGs), audit trails, and access controls, while complying with laws such as the General Data Protection Act (LGPD).”
Despite the progress, the executive notes that the implementation of GenAI in the logistics sector still faces technical and regulatory challenges. “Key hurdles include ensuring data quality and governance, mitigating illusions through validation, controlling costs and delays under variable loads, and securely integrating technology and legacy systems. Implementing model observability mechanisms, rapid versioning, and fallback policies are also essential.”
Samsung SDS believes that by 2030, generative AI will become the engine of “effortless hyper-innovation” in logistics and enterprise technology operations, evolving from a conversational co-pilot to an AI agent that can orchestrate end-to-end processes with security, governance, and clear ROI. The company already has a full-stack approach to AI, including infrastructure, consulting, platforms, and solutions, with a focus on partnerships with Brity Copilot, Samsung Cloud Platform, and cloud ERP providers.
The company is also investing in features such as multilingual automatic interpretation, GPU as a Service (GPUaaS), and enterprise security. “We are working with clear goals in mind, metrics monitored in real time, and fluid integration with existing systems. The trend is for GenAI to become the ubiquitous co-pilot of IT and logistics, increasing productivity and the quality of services provided,” concludes Aoyagui.
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