AI in Logistics: A 360-View on Challenges, Use Cases, Best Practices

Dmytro Ivanov
Alina Ampilogova

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?

In this article, we'll address these and other top-of-mind questions related to the AI application in logistics to outline the most realistic image of current and potential adoption benefits.

AI in logistics and supply chains: the journey so far

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 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 the 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.

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How is artificial intelligence impacting the logistics industry?

The best way to start the conversation about the benefits of AI in supply chains and 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 made 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.
    Use of AI 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.

AI in logistics industry: key benefits and use cases

AI technologies can bring value to nearly every area under the logistics and supply chain management operations umbrella.


Due to this, we will 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, AI application 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 in transportation and 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).

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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.

    Such precision lets logistics businesses optimize operations, inventory management, and product distribution for each store they deliver their products. In other words, use of AI in logistics POS forecasting helps businesses create more personalized management, replenishment, and reordering strategies, gleaning max profit and ROI from every operation.

  • 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, AI-powered chatbots 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.

Logistics analytics dashboards and how they enable smarter supply chain management

  • Risk management
    AI solutions for logistics allow for minimizing 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 identifying and solving emerging issues.

    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 and machine learning in supply chains and logistics as its market value is expected to reach $64 billion by 2030.

Given such promising numbers, we’ll witness further diversification 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.

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.

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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 a number of 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 development of AI 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 actually implemented artificial intelligence in logistics and supply chain by 2024. To be more specific, there are several reasons for slow adoption of AI in transportation and logistics.

  • 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.

  • 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 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 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.

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.

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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 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. For that reason, it’s always recommended to consult with trusted technology partners possessing deep domain knowledge and experience with pioneering AI solutions across the niche. A 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 learning how to use AI in 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.

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AI and machine learning have a wide range of logistics management applications, allowing organizations to build dynamic supply chain models, enhance their demand planning, analyze operational data, and make more informed decisions.
Using AI in transport logistics and supply chain management provides companies with better control over their data, removing data silos, refreshing databases, and letting planners take account of a wide range of factors such as vehicle conditions, weather, traffic, and route status.
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