In the wake of intensified digital transformations across industries, logistics remains among the sectors where introducing innovation is as challenging as rewarding. The challenge lies in finding the most efficient and flexible solution to bypass friction points and deliver results. The reward lies in optimized operational efficiency, time-effective processes, and increased productivity.
How does AI in logistics fare when it comes to tackling challenges and rewarding adopters? How has artificial intelligence evolved since its introduction to the industry? How will it affect the future of supply chains?
This article will address top-of-mind questions related to the application of artificial intelligence in logistics to outline the most realistic image of current and potential adoption benefits.
What is AI in logistics?
Artificial Intelligence (AI) in Logisticsis the practice of overseeing implementation of the technology in different areas of logistics and transportation to address industry pain points and secure long-term efficiency.
Having continuously faced disruptions caused by global-scale events, the landscape of logistics and supply chain remains uneven and unpredictable, motivating supply chain managers and logistics executives to seek technologies that can cover vulnerable areas. This intense momentum led to the following 2024 AI in logistics trends:
- Intense consumer demand
According to DHL, the expectations of potential buyers aren’t going down—they’re picking up speed, which means that to win over cooperation with retail leaders, 3PL companies need to become faster, more efficient, and more precise regarding their service delivery. Modern buyers expect to receive their orders as soon as possible—and the company that manages to minimize the waiting time wins more clients. - Persisting skill shortage
As around 76% of supply chains continue to deal with insufficient numbers of skilled workers, there is a demand for precision and foresight that are necessary for efficient resource utilization and workload distribution. The utilization of AI in logistics and transportation allows enterprises to meet such a demand as it injects clarity and enables more controlled logistical frameworks. - Need for transparency
Breaking visibility barriers became a crucial task for logistics organizations in 2023/2024. Following the KPMG statement, 43% of enterprises lack visibility or have limited insight regarding supplier performance. This is the setback companies intend to address within a data-driven approach and decision-making enhanced by AI in supply chain and logistics.
In the context of these changes and ongoing transformation, the use of AI in logistics becomes a cornerstone of new logistical frameworks and models, enabling businesses to equip themselves for smoother delivery journeys.
with digitally-enhanced visibility
How does artificial intelligence impact the logistics industry?
The best way to start the conversation about the benefits of AI for logistics would be to begin by determining why traditional supply chain management solutions need an AI-based upgrade. It's not uncommon for adopters to hesitate or explore alternatives before deciding in favor of AI solutions—much of that uncertainty stems from a lack of knowledge about their outdated tech stack constraints.
- Lack of qualified planners
The shortage of labor force had a negative impact on supply chain planning capacity. Supply chain management requires hundreds of talents to oversee operations, analyze the data, and plan schedules in accordance with the ongoing demand and challenges. However, the 64% talent shortage reported by multiple supply chain organizations indicates that SCM currently lacks the human resources necessary for flexible and insightful planning. - Stale data
Legacy supply chain management solutions provide poor data communication, often failing to refresh their performance data or distribute it timely across managers. As a result, employees end up working with data that doesn't match relevant productivity requirements. Due to this, they end up making costly errors, missing out on revenue opportunities, or failing to make an accurate decision.
What further complicates the issue is that outdated solutions often retain valuable insights locked within local data hubs instead of enriching the data network across the enterprises. This leads to supply chain managers becoming aware of potential issues or possibilities for the entire supply chain. - Out-of-touch supply chain models
Supply chain models address many vital points, such as product manufacturing, selecting the best suppliers, inventory and warehouse management, and distribution. However, most traditional supply chain models turned out to be obsolete and over-simplified compared to the actual dynamic environment.
Therefore, such models could not properly reflect potential developments within supply chain operations or consider the issues affecting the industry.
The application of artificial intelligence in logistics makes it possible to address these issues by taking control of the data across the entire network, eliminating silos, and enabling a more structured organization of human resources.
Use cases: AI in logistics industry
How AI is used in the logistics industry? Since the technology can bring value to nearly every area under the logistics and supply chain management operations umbrella, it makes sense to focus on the most high-demand and well-known AI use cases in logistics, separating the real advantages from the potential improvements.
Logistics planning
One of the strong, if not the strongest, advantages of using AI in supply chain and logistics is the clarity and transparency of logistics operations. By harnessing the ability of AI technology to translate data into predictive insights, organizations are able to power the most important logistics planning components: demand forecasting, route optimization, and supply planning.
- Demand forecasting
Understanding the future demand for the product is the cornerstone of logistics operations. Accurate demand prediction enables companies to timely optimize their routes, schedules, inventory management, and transportation details.
However, demand forecasting requires processing large volumes of real-time data and comparing it to past performance data to detect trends and determine demand. AI algorithms capable of pattern recognition, data analysis, and scenario simulation take this massive task off the executives' hands, providing them with actionable predictive insights that pinpoint future demand and facilitate decision-making. - Supply planning
Understanding the future demand has a direct effect on stock and replenishment planning. The same AI algorithms pinpointing the demand for a certain product within a certain season or time of year let companies make more informed decisions about their inventory management.
AI in logistics improves supply planning by analyzing production schedules, sales data, cost constraints, and lead time, which allows businesses to calculate safety stock levels, reorder points, and replenishment routines. In addition, the application of artificial intelligence in logistics planning helps companies prevent sudden stockouts or instances of excess inventory, thus considerably minimizing costs and giving more control over expenses. - Route optimization
Route planning and scheduling can reduce costs and time or do the exact opposite, depending on how dynamic it is. AI logistics equips companies with solutions that identify the most efficient transportation routes by analyzing a wide range of factors—from traffic and weather conditions to customer preferences and vehicle capacities. Additionally, AI-powered solutions reinforce logistics with flexibility by tackling unforeseen disruptions (traffic jams, roadblocks due to an accident) and swiftly rerouting vehicles to available routes. This enhanced accuracy, and agility enables companies to minimize transportation costs and fuel consumption while increasing their ROI from every operation.
Logistics planning also covers personnel planning that further benefits from AI-based enhancements. For instance, AI algorithms assist companies with calculating the exact amount of personnel needed for peak seasons such as Thanksgiving or Christmas, preventing scenarios where employees work overtime in an extremely stressful environment (due to personnel shortage).
Sales and marketing
Operators, dispatchers, warehouse managers, and other logistics professionals aren't the only ones who benefit from AI use in logistics. AI solutions can streamline processes from the customer's side, providing marketing analysts, sales managers, and company representatives with valuable assets for increasing conversion rates and amplifying customer experience.
- Point-of-sale forecasting
Even when a company's demand forecasting is top-notch, there is always a way to boost decision-making accuracy within a few steps from the end user. Point of sale forecasting does that by analyzing transaction data (POS data) to identify consumer-buying patterns at a store level. Enriching POS forecasting with logistics AI allows enterprises to add more depth and precision to this analysis, optimizing operations, inventory management, and product distribution for each store. - AI chatbots
Regardless of the industry, customer expectations remain dynamic and growing. Logistics clientsand partners expect fast responses, information transparency, and friction-free onboarding. Addressing those needs within human-managed flows can result in stretched schedules and dangerously increase the number of errors and miscommunications.
Therefore, chatbots powered by logistics AI are now becoming an integral part of superior customer experience. Capable of responding to customer queries, processing data, and connecting clients with relevant reps within seconds, virtual assistants take the bulk of customer communication. Meanwhile, sales reps can focus on addressing the most urgent questions.
In addition to processing customer queries, AI chatbots fuel marketing campaigns by supplying teams with valuable customer data (from general service feedback to geographical location and preferences) allowing for more personalized offers. Virtual helpers also give marketing teams a more in-depth perspective on their campaign performance, which helps them identify and halt underperforming campaigns, liberating more resources to develop productive ones.
It is also worth mentioning that AI in transportation and logistics has a powerful effect on intracompany communication, letting departments share valuable data and updates. As all that data is filtered and sorted out by a fast-thinking AI solution, it becomes considerably easier for sales reps and marketing teams to explore enriched databases and discover valid bits of information that can be used for brand promotion.
Data-driven analytics
Where there’s data, there’s work for AI. With its ability to swiftly process, analyze, manage, and organize data at all levels, AI in logistics and supply chain offers an eye-opening journey across company workflows, enabling adopters to strengthen every supporting pillar of their performance.
- Risk management
Leveraging AI for logistics allows enterprises to minimize potential risks and hazards that can affect productivity or even employee safety. From advanced analytical tools to ML-based modeling that simulates force majeure scenarios to analyze supply chains' readiness for accidents, logistics businesses have the means for timely issue identification and troubleshooting.
Managing risks with AI-powered platforms also ensures that enterprises don’t compromise their productivity or endanger employees as they work their way through disruptions. Using AI in logistics for risk prevention provides a long-term benefit as it lets enterprises build up resistance and shift towards flexibility necessary for overcoming future challenges. - Anomaly detection and fault analysis
Any error, whether a master data error or a hiccup in the logistics processes, can lead to a financial loss or sabotage an entire operation. This is where AI in transportation and logistics backs employees up by enhancing the accuracy of data monitoring and supply chain management. In the latter case, AI solutions are often paired with IoT sensors attached to vehicles, machines, or forklifts, responding to unnatural noises or tracking alarming performance patterns.
In addition to audio-based anomaly detection, AI in logistics can also be based on visuals by combining ML models with computer vision. This enables AI to evaluate manufactured products in accordance with the references it was given, identify faults, and send corresponding notifications. - Yield loss analysis
The role of AI in logistics goes beyond improving already well-performing processes—it also includes investigating the causes of underperforming operations and then making the most out of that knowledge.
For example, AI-powered platforms let companies enhance and optimize their yields by analyzing production data and pinpointing the underlying patterns or factors that affect overall product quality and yield rates. Using such solutions also allows for identifying problematic elements in product manufacturing or operating conditions, necessary for streamlining production and reducing costs.
Aside from the productivity-boosting AI solutions for logistics, there are also types of AI platforms dedicated specifically to employee welfare. By regularly interacting with operators, dispatchers, and managers, these platforms gather information on their stress level and mental state, and use this data to deliver individual recommendations and generate break suggestions for HR managers.
While such solutions aren't directly related to the logistics industry, they help address workforce needs, increasing employee engagement and keeping up with their expectations.
The future of AI in logistics: what lies ahead?
The future looks bright for the adoption of AI in logistics and transportation as its market value is expected to reach $64 billion by 2030.
Given such promising numbers, we’ll witness more diverse use of AI in logistics. The technology will likely continue filling in various supply chain gaps and gaining more flexibility to tackle complex tasks. However, what is more important and what we look forward to is the increase of accessibility of AI technology, when exclusive AI projects will evolve into services available to logistics businesses of varying sizes and budgets.
Large companies and startups implementing AI in logistics
When exploring the growing role of artificial intelligence in logistics, it’s important to pay close attention to the giants that are the first to adopt innovative trends — and the startups that tailor disruptive technology to a specific niche. In the case of logistics and artificial intelligence, it’s noteworthy that the most impactful adopters are companies that don’t operate in the sector.
For instance, retail giants such as Amazon and Walmart are the leading players when it comes to AI implementation, with Amazon leveraging its vast resources to try out even the most experimental directions of AI logistics. Electrocar manufacturer Tesla Motors is another particular example as it continuously explores applications of artificial intelligence in logistics.
Automated warehousing
Automated warehouses with self-moving equipment and IoT sensors are often mentioned as part of AI solutions for logistics and supply chains—to the point it may seem that automated warehouses have become a regular occurrence. However, the truth is that around 80% of warehouses across the US remain non-automated, with only 5% being equipped with sophisticated AI-powered solutions.
This is a logical development since implementing such advanced technology requires a personalized approach and an in-depth knowledge of the company's workflow, manufacturing processes, and distribution routines.
To create fully automated warehouses, companies need a center of excellence dedicated to creating capabilities and software for specific tasks and operations. Currently, this task is affordable to massive and resource-rich enterprises like Amazon. But in the future automated warehouses are expected to become a lot more common, with automated warehousing shifting from exclusive and costly projects to accessible service.
Automated vehicles
Many materials and conversations have been dedicated to AI-controlled vehicles, forklifts, and their role in improving workplace productivity and potentially reducing operational costs by 45%. However, autonomous vehicles and AI-managed equipment currently remain individual, tailored projects rather than massively available solutions.
Explored by startups like Outrider, automated vehicles remain a point of curiosity for most logistics businesses that still have concerns and resistances to tackle before introducing this component into their operations. The main reason for such hesitance is the necessity to introduce several measures ensuring the safe, controlled management of autonomous vehicles on the road. Only after gaining full confidence about all the what-ifs surrounding the matter, logistics companies will be able to proceed to more active technology adoption.
This is something we will be observing in the industry's future as companies find ways to align self-driving trucks with safety regulations to glean the benefits without risking their reputation.
Self-learning supply chains
The most significant and valuable application of artificial intelligence in logistics will undoubtedly be its ability to create new types of flexible and resistant supply chains with the capacity for learning and adapting to disruptions.
By quickly analyzing data, sharing it across the network, and working through different scenarios, supply chains are expected to deliver more accurate and optimized strategies amidst uncertain business environments.
Accordingly, businesses will make more informed decisions and calculated steps for mitigating the crises and normalizing routines.
Within self-learning supply chains, logistics companies will be able to continue their activities without losing productivity—and preserving employee engagement.
Challenges in AI adoption
Although plentiful and versatile, the benefits of AI in logistics only become available when companies are familiar with the challenges of adopting the technology. After all, there is a reason why only 25% of organizations surveyed across the US, UK, and Germany had implemented artificial intelligence in logistics and supply chain by 2024. To be more specific, there are several reasons for the slow adoption of AI in transportation and logistics.
- Need for in-depth digitization
The lack of pre-adoption measures can dramatically impact the outcome of adopting AI in logistics. Most commonly, businesses receive less-than-satisfactory results or see a complete misalignment between their expectations and the final stage, which brings them back to square one, resulting in dissatisfaction with AI applications in logistics. To avoid such a scenario, executives need to digitize their processes across the enterprise and prepare a solid analytical foundation before investing in AI in supply chain and logistics. - Lack of internal expertise
Another obstacle to adopting AI in transportation and logistics is the shortage of employees experienced with operating AI tools. Depending on the onboarding and communication, any innovation can be a benefit or a disadvantage to stakeholders and their area of focus. To prevent the latter, companies need to combine digitization with clear and transparent adoption journeys that can be evangelized across departments. - Cost barriers
Innovation never comes at a low price. Any organizational change will always involve expenses—and the larger the organization, the more it will cost the decision-makers to implement new technology. Accordingly, it’s not uncommon for executives to spend months on risk assessment and discussions, determining the worth of investing millions in innovation. Considering that wrong preparations for AI use in logistics aren't rare, their concern isn’t exactly unfounded—they want to be sure all the massive resources they invest would provide predictable and rewarding returns.
Although the financial and organizational costs of integration of artificial intelligence in transportation and logistics cause executives to hesitate, these challenges can be resolved through detailed preparation and strategic planning. A calculated approach to AI in logistics can result in beneficial outcomes and lasting success—which is why implementation should be focused on delivering long-term value.
Cost of implementation and maintenance
Albeit diverse, the examples of AI in logistics mentioned above have one thing in common—high implementation and maintenance costs. While the exact price depends on the scale, complexity, and purpose of the product, certain components affect the estimate.
Hardware costs
Type of specialized hardware needed to handle the large amounts of data and support AI algorithms: GPUs (Graphics Processing Units), FGPAs (Field-Programmable Gate Arrays), or ASICs (Application-Specific Integrated Circuits)
Software costs
Tools for collecting, labeling, cleansing, organizing, and processing training data — the final cost depends on the volume and complexity of the data set.
Labor costs
Hiring talent skilled in building AI models – data scientists, software developers, and machine learning engineers.
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Consistently utilizing software and hardware resources to keep AI models trained and up-to-date.
In addition to already high hardware, software, and maintenance costs, the price of synergizing logistics and artificial intelligence is further impacted by the following factors:
- Data
AI model training depends on the type and the amount of data used for it. Accordingly, the more complex the data, the more complicated and costly the training process will be. When it comes to logistics AI, there are many different types of data to work with, which will add to the expenses. - Task complexity
The goal of implementing AI for logistics also matters. If an AI model is expected to address a complex issue, it will need larger volumes of training data and ample processing power, increasing software and hardware costs. - Number of devices
If an enterprise needs an AI system to run on numerous apps and devices, the technology implementation costs will grow as it will require more processing power and resources.
On average, when developing a complex AI system the price tag starts from $200,000 million — plus constant maintenance and training costs. While it’s not surprising that executives find such costs overwhelming, it’s important to see the bigger picture regarding the role of AI in logistics.
Despite its considerable cost, the application of artificial intelligence in logistics is also estimated to bring enterprises up to $15 trillion in savings. By reducing logistics costs to 10%, minimizing risks, and enhancing demand forecasting accuracy, AI is expected to assist enterprises with optimizing their resource allocation and maximizing their operational outcomes. So, while AI adoption will inevitably require ample investment, this investment will also impact long-term value and secure greater resilience.
How to use AI in logistics: partnership is the key
While some disruptive technologies don't always fit the company's goals and workflow, AI technology is versatile enough to offer value for almost every component of logistics, transportation, and supply chain—making it a go-to option for logistics enterprise transformation.
Furthermore, by enriching a diverse set of workflows, data, and transparency, AI in supply chain and logistics enables the evolution of industry-related businesses, letting them bypass growing pains and advance to the next level of performance.
However, given the challenges mentioned above, it’s not uncommon for executives to hesitate before making the first step. Therefore, AI in logistics statistics remains lower than expected.
To address their concerns, executives must consult with trusted technology partners possessing deep domain knowledge and experience with pioneering AI solutions across the niche. Collaboration with a team of vetted professionals makes it possible to productize the right solution concept for the enterprise and compensate for the lack of internal expertise with comprehensive onboarding.
If you’re interested in leveraging AI for logistics in a way that would let you unleash the potential of your enterprise, let’s chat.
Our ML engineers, RPA architects, and tech leads will provide a detailed consultation on the opportunities and improvements that can be unlocked by imbuing your logistics planning and supply chain management with artificial intelligence.
