Gen AI

Trend Overview

Impact:

Significant

Adoption:

< 5 Years

Focus Areas:

Automation & Efficiency Customer Experience Health, Safety & Future of Work

Trend Clusters:

Artificial Intelligence Digital Backbone

Sectors:

The trend of Generative Artificial Intelligence (Gen AI) refers to AI techniques that can autonomously produce new content, such as images, text, audio or videos, based on patterns learned from existing data. It utilizes algorithms and neural networks to generate original outputs that mimic the style or characteristics of the input data.

Gen AI grew from advances in machine learning (ML), particularly in the development of currently the two most widely used generative AI models. One type is generative adversarial network (GAN) models, which enable technologies to create visual and multimedia artifacts from both imagery and text input data; the other type is transformer-based models including technologies such as generative pre-trained (GPT) language models that can use information gathered on the internet to create text content.

These models have introduced new methods for training neural networks to generate realistic and novel data by learning underlying patterns from large datasets. The field has since expanded to encompass various techniques and applications, leading to widespread use in creative tasks, data augmentation, and more.

The gen AI market size was valued at US$ 29.00 billion in 2022 and is projected to grow from $43.87 billion in 2023 to $667.96 billion by 2030, with a compound annual growth rate (CAGR) of 47.5% during the forecast period (2023-2030). North America accounted for a market value of $14.49 billion in 2022, representing approximately 49.93% of the global market.

The trend of Gen AI is set to have a relatively high impact on logistics , with widespread adoption in logistics anticipated within the next three to five years.

Here at DHL, we mostly expect gen AI developments to impact use cases involving image, video, or text generation. We also anticipate use cases within logistics that will impact both white-collar and blue-collar employees. Applications that will automate certain back-office processes will continue to expand, as well as supporting operational processes. These can range from streamlining IT helpdesk requests, automating simple HR enquiries to generating translations, to name a few.

In addition, we believe there will be a steep increase in adopting sandboxed environments in which companies can use gen AI tools to try new ideas and initiatives. For example, within DHL we have developed our own GenAI Hub to test out automating certain processes, developing content, and generating information.

Gen AI Models: Types & Use Cases

Source: Zendesk (2024): A beginner's guide to generative AI for business

Relevance to the Future of Logistics

Optimized Route Planning

AI algorithms can be used in logistics to optimize route planning for deliveries. By considering various factors such as traffic patterns, weather conditions, and delivery priorities, AI-powered systems can generate efficient routes while providing transparent explanations for the decisions made. This ensures logistics providers can trust the accuracy of the suggested routes and understand the rationale behind them.

In logistics, which relies heavily on location services, gen AI may also be used to accurately convert satellite imagery to map views, enabling the exploration of yet uninvestigated locations.

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Content Creation

Gen AI offers the creation of text, images, and even coding. For logistics, this means the potential for acceleration across a number of functions.

The technology can for example automate the creation of text for product descriptions, inventory reports, and customer service responses, streamlining communication within logistics operations and improving efficiency. It can also generate visual representations of inventory items or warehouse layouts to help with inventory management. Other image generation applications include creating designs for packaging that maximize space efficiency and protection of goods. These AI generated images can help in prototyping new packaging solutions quickly.

Gen AI can also automate the creation of scripts for data analysis, optimize algorithms for route planning, and build predictive models for demand forecasting. This enhances data-driven decision-making processes and operational planning in logistics companies.

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Customer Experience Automation

Chatbot interfaces offered by gen AI can be used in many ways to enhance customer-centric supply chain networks.

This technology can also help customers get immediate and satisfactory responses to their enquiries, with their input processed through commonly used communication channels. For example, each customer can receive a prompt and customized email response or automated phone call or text message.

Gen AI can also swiftly analyze various unstructured customer feedback sources such as online reviews and social media sentiment. So, for example, when multiple customers make comments about a particular product, gen AI can help rapidly integrate these insights into product development workflows.

In e-commerce, gen AI helps to improve the shopping experience and increase customer satisfaction. A good example of this is personalized product recommendations generated by AI-driven algorithms based on customer preferences and past purchase history.

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AI Assistants

Currently, several different types of AI assistants are being developed at scale but not all are suitable for deployment in the logistics industry.

Here at DHL, we see logistics operations making use of AI-based predictive analytics assistants, which can forecast demand, optimize inventory levels, and anticipate potential supply chain disruptions, enabling proactive decision making and resource allocation.

We also expect the industry to make use of warehouse management assistants, which can optimize inventory placement, automate picking and packing processes, monitor equipment maintenance schedules, and enhance overall operational efficiency within warehouses and distribution centers.

Another useful type of AI assistant in logistics is a supply chain visibility assistant. These provide real-time visibility of the entire supply chain, tracking the movement of goods from suppliers to end customers, helpfully identifying bottlenecks, mitigating risks, and optimizing logistics processes for enhanced transparency and responsiveness.

In the logistics back office, administration, legal documentation, finance, or HR department, we see AI assistants providing support such as screening lengthy texts and summarizing the key points. This could accelerate tasks, ensuring faster turnaround times for non-disclosure agreement (NDA) processing, contract processing, candidate curriculum vitae (CV) screenings, and financial report summarizing and forecasting.

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Challenges

Implementing and integrating gen AI into existing logistics systems and workflows is likely to be resource intensive and requires specialist AI and data science expertise not commonly available in the industry today.

Logistics companies may face challenges in adapting their infrastructure, training personnel, and ensuring seamless integration with existing software platforms.

It can be difficult for stakeholders to assess the reliability of output from gen AI models (which typically operate as "black boxes"); this lack of interpretability can lower trust in a solution, even preventing acceptance. This trustworthiness of AI is a challenge that can be addressed through AI Ethics.

Trust in AI is a challenge that can be addressed through AI ethics.

Logistics companies may face challenges in adapting their infrastructure, training personnel, and ensuring seamless integration with existing software platforms.

Trust in AI is a challenge that can be addressed through AI ethics.

Challenge 1 Challenge 2 Challenge 3 Challenge 4

Outlook

Here at DHL, we have witnessed developmental leaps in AI over recent years. This explains why AI continues to generate interest and investment and is predicted to grow in the long term, with an ever-expanding range of gen AI applications capable of driving significant business value.

The three key building blocks of more data, continuous advancements in algorithms, and stronger computing power indicate these use cases are likely to materialize and scale.

Logistics and the supply chain are integral to soaring AI adoption levels every year. By adopting gen AI, the logistics industry will be able to better respond effectively to the operational challenges of growing business-to-business (B2B) and business-to-consumer (B2C) demand, and more recently emerging consumer-to-consumer (C2C) demand.

This trend should be ACTIVELY monitored,with implementations available for many use cases today.