When operations seem steady and teams are settled into their routines, calls to overhaul workflows and embrace automation may not sound appealing, but they signal where the industry is headed.

Generative and agentic AI implementation has cut costs to four-fifths of previous levels, according to a September 2025 report from McKinsey, and the generative AI in logistics market is set to surpass $23.1 billion by 2034, according to July 2025 data from Global Market Insights. Generative AI’s power to simulate scenarios, enrich training data and automate routine tasks is fueling its rapid market growth.

While generative AI recognizes patterns and creates content, agentic AI uses these outputs to reason, plan, and take autonomous actions. Together, logistics leaders can apply one to simulate numerous delivery schedules and have the other pick what’s optimal, adjusting decisions based on parameters such as traffic or weather disruptions.

However, success doesn’t come from the tools alone. Twenty-one percent of respondents to a March 2025 McKinsey survey say they have fundamentally redesigned at least some GenAI workflows, and 44% have reskilled at least 5% of their workers. These new processes are what will set them up for easier agentic AI adoption. 

Generative AI Powers Precision Demand Forecasting

Supply chain forecasting is evolving from a guessing game into a precision instrument. By simulating countless “what if” scenarios and creating synthetic data to fill in the blanks, GenAI helps leaders spot patterns and react faster than traditional methods ever could. MIT reports that companies that have jumped on board are seeing forecast accuracy improve by 15% after rolling out an AI-powered demand planning system. 

Traditional machine learning models rely on patterns in historical data. If certain demand scenarios have never occurred before — like a sudden spike in plant-based milk after a viral campaign — standard models struggle to predict them. However, GenAI can create synthetic scenarios that simulate these rare or unseen conditions, giving models a richer training dataset.

The benefits to logistics teams are that forecasts are more robust, and businesses can anticipate unusual spikes or drops instead of reacting after the fact. With thousands of “what-if” scenarios in the training data, such as extreme weather, supply delays, market trends, promotions, or regulatory changes, companies can stress-test their supply chains and prepare.

Agentic AI Goes Beyond Prediction

In logistics operations, agentic AI enhances efficiency by automating decisions and tasks autonomously across the logistics chain. Already, $85 million is flowing into logistics agentic AI projects powering agents for quoting, dispatch, tracking, scheduling, and billing. This shift promises a leap from heavy manual oversight to near self-sufficient operations.

So far, use cases in autonomous routing and scheduling have led to significant reductions in inventory and logistics costs by more than 20%. Intelligent workflow agents have also been known to streamline documentation processes, reducing transactional cycle times from days to hours or even minutes. 

These advances stem from breakthroughs in large language models, seamless API integration, and increasingly powerful GPU infrastructure capable of handling intensive computations. By automating repetitive tasks and optimizing operations, agentic AI in the supply chain and logistics market was valued at $8.67 billion in 2025, and is projected to reach $16.84 billion by 2030 with a CAGR of 14.2%.

How to Prepare Workflows for Change

Implementing AI agents requires cohesive integration across various systems and platforms. This integration ensures that agents can function seamlessly across different parts of the logistics operation.

Adopting a modular, distributed architecture known as an agentic AI mesh, enables logistics teams to deploy and manage AI agents effectively. This architecture supports scalability and adaptability in AI implementations.

Fleet operators can prepare by consolidating spreadsheets, TMS, WMS and ERP data into a single structured repository. They must fix any missing or inconsistent data and remove duplicates for easier implementation. Good practice would be to start logging operational events such as deliveries and fuel usage as systematically, or automatically as possible.

With clean and consistent data, fleet operators can start working with modular, cloud-friendly systems that allow AI agents to interact across different parts of the operation — for instance, cloud-based TMS/WMS solutions that offer APIs or open integrations. 

Human oversight is essential in agentic AI processes to ensure accountability, maintain trust, and correct errors that automated systems may overlook. Logistics leaders can begin by integrating one or two core systems, such as fleet tracking or order management, and once the team is confident that the new workflows are operating as they should, they can expand.

GenAI enhances forecasting and planning accuracy, while agentic AI executes tasks autonomously, enabling logistics to operate in real time with continuous optimization. To prepare, companies should clean and consolidate data, adopt modular systems, pilot AI on key tasks, and redesign workflows with human oversight and scalable architecture in mind.

Asparuh Koev is the co-founder of Transmetrics