The Role of Artificial Intelligence in Fleets of the Future

The iconic image of a lone truck traversing vast landscapes embodies the resilience and adaptability of the trucking industry. However, this familiar scene stands at the verge of massive transformation. Artificial intelligence (AI) is poised to change not just the roads traveled, but the very essence of trucking operations. This white paper serves as a strategic roadmap, guiding executives through the transformative potential of AI and its implications forcompetitiveness, efficiency, and ultimately, industry leadership.

1. ARTIFICIAL INTELLIGENCE 101

Artificial Intelligence refers to the simulation of human intelligence in machinesthat are programmed to think and learn like humans. It differs from traditionalcomputer programs that are designed to follow pre-defined instructions andexecute specific tasks based on the programmed algorithms. AI systems,instead, can learn from data, adapt to changing inputs, and make decisions orpredictions without explicit programming for every possible scenario.

Machine learning is a subcategory of artificial intelligence. Its inception lies inclassical statistical methods developed since the 18th century on small data sets.For long, computational power and the absence of large data sets preventedmachine learning from developing its potential. The digitization of our worldthrough the internet and the rapid development of computational power thatcan be housed in a microprocessor were two prime forces that effectively have catapulted machine learning into what it is today.

Machine learning can broadly be split into supervised and unsupervised learning.Supervised learning employs humans to label data sets teaching an algorithm torecognize patterns, classify data, or predict outcomes. Common applications intrucking involve teaching algorithms to read labels or rate confirmations usingoptical character recognition (OCR). Another, more traditional technique insupervised learning is regression analysis, which is commonly taught even inbusiness schools for analytics. Unsupervised learning, on the other hand, doesnot require human intervention to label data. It uses mimicry to build arepresentation of the world from it and then can generate new content.Unsupervised learning includes methods such as clustering to group a set ofobjects by common features or dimensionality reduction to define and selectfeatures.

A method adjacent to machine learning is reinforcement learning which findsapplication in trucking in the form of optimization algorithms. For theseapplications, the goal is not only to mimic human behavior but to exceed humanperformance. This is possible because computers can evaluate the variousoutcomes of a series of decisions faster and pick the best decision sequence(sequential optimization), incorporate probabilities (stochastic optimizations),and avoid a host of behavioral biases that humans are subject to.

Finally, 2023 is often described as the breakthrough year of generative artificialintelligence. Generative AI refers to algorithms that can be used to create newcontent, including text, audio, visual or others. ChatGTP - short for generativepre-trained transformer - for instance is a chatbot that uses a large languagemodel (LLM) to create text answers in response to questions (so-called prompts)

1. ARTIFICIAL INTELLIGENCE 101

Artificial Intelligence refers to the simulation of human intelligence in machinesthat are programmed to think and learn like humans. It differs from traditionalcomputer programs that are designed to follow pre-defined instructions andexecute specific tasks based on the programmed algorithms. AI systems,instead, can learn from data, adapt to changing inputs, and make decisions orpredictions without explicit programming for every possible scenario.

Machine learning is a subcategory of artificial intelligence. Its inception lies inclassical statistical methods developed since the 18th century on small data sets.For long, computational power and the absence of large data sets preventedmachine learning from developing its potential. The digitization of our worldthrough the internet and the rapid development of computational power thatcan be housed in a microprocessor were two prime forces that effectively have catapulted machine learning into what it is today.

Machine learning can broadly be split into supervised and unsupervised learning.Supervised learning employs humans to label data sets teaching an algorithm torecognize patterns, classify data, or predict outcomes. Common applications intrucking involve teaching algorithms to read labels or rate confirmations usingoptical character recognition (OCR). Another, more traditional technique insupervised learning is regression analysis, which is commonly taught even inbusiness schools for analytics. Unsupervised learning, on the other hand, doesnot require human intervention to label data. It uses mimicry to build arepresentation of the world from it and then can generate new content.Unsupervised learning includes methods such as clustering to group a set ofobjects by common features or dimensionality reduction to define and selectfeatures.

A method adjacent to machine learning is reinforcement learning which findsapplication in trucking in the form of optimization algorithms. For theseapplications, the goal is not only to mimic human behavior but to exceed humanperformance. This is possible because computers can evaluate the variousoutcomes of a series of decisions faster and pick the best decision sequence(sequential optimization), incorporate probabilities (stochastic optimizations),and avoid a host of behavioral biases that humans are subject to.

Finally, 2023 is often described as the breakthrough year of generative artificialintelligence. Generative AI refers to algorithms that can be used to create newcontent, including text, audio, visual or others. ChatGTP - short for generativepre-trained transformer - for instance is a chatbot that uses a large languagemodel (LLM) to create text answers in response to questions (so-called prompts)

2. IMPACT OF ARTIFICIAL INTELLIGENCE ON OUR ECONOMY

The adoption of artificial intelligence across industries is expected to generate significant economic benefits. Goldman Sachs estimates that the adoption of large language models can increase global GDP by 7% or almost $7 trillion, and increase productivity growth by 1.5% over ten years.

A 2022 survey by McKinsey found that companies investing more than 20% of their digital budgets on AI report that artificial intelligence is responsible for 20%of their EBIT. While cost reductions see many applications, these firms frequently also use them to drive revenue and create new businesses or products.

Workforce effects are the most substantial impacts of artificial intelligence across economies. The adoption of new tools will fundamentally affect what humans do, how humans do it, and what skills organizations need to hire for. While most of the automation efforts in the past century have targeted blue-collar manual labor, the wave of automation leveraging artificial intelligence is likely to affect most fundamentally white-collar positions in the so-called knowledge economy, in particular the operations of services.

2. IMPACT OF ARTIFICIAL INTELLIGENCE ON OUR ECONOMY

The adoption of artificial intelligence across industries is expected to generate significant economic benefits. Goldman Sachs estimates that the adoption of large language models can increase global GDP by 7% or almost $7 trillion, and increase productivity growth by 1.5% over ten years.

A 2022 survey by McKinsey found that companies investing more than 20% of their digital budgets on AI report that artificial intelligence is responsible for 20%of their EBIT. While cost reductions see many applications, these firms frequently also use them to drive revenue and create new businesses or products.

Workforce effects are the most substantial impacts of artificial intelligence across economies. The adoption of new tools will fundamentally affect what humans do, how humans do it, and what skills organizations need to hire for. While most of the automation efforts in the past century have targeted blue-collar manual labor, the wave of automation leveraging artificial intelligence is likely to affect most fundamentally white-collar positions in the so-called knowledge economy, in particular the operations of services.

3. AI TRANSFORMING THE TRUCKING INDUSTRY

The trucking industry, long known for its resilience and adaptability, stands at the precipice of a transformative era. Artificial intelligence (AI) is poised to change many aspects of operations today, from the vehicles themselves to driver management, dispatching, and back-office processes. This section explores the potential of AI to unlock new levels of efficiency, safety, and profitability for trucking companies.

Many fleet owners struggle with the amount of work and the amount of workarounds traditional transportation management systems create for them. The Nobel-prize-winning economist Robert Solow described this phenomenon famously by saying "You can see the computer age everywhere but in the productivity statistics." His student Erik Brynjolfsson, professor and senior fellow at the Stanford Institute for Human-Centered AI, projects that generative AI is different. He expects it to generate at least a 3% productivity gain per year and likens it to the impact of electricity on human economic activities. The opportunity for productivity improvements with modern TMSs can be expected to be similar.

Traditional transportation management systems (TMS), often siloed and on- premise, struggle to keep pace with the demands of a data-driven world. AI, on the other hand, thrives on data. It can analyze vast amounts of information on weather, traffic patterns, driver behavior, and vehicle performance, gleaning insights that were previously hidden. This enables AI-powered systems to:

Optimize routes and schedules: AI can dynamically adjust routes in real time, considering factors like traffic congestion, weather conditions, and driver preferences. This leads to reduced fuel consumption, improved on-time delivery rates, and better driver utilization.
Enhance predictive maintenance: AI can analyze sensor data from vehicles to predict potential failures before they occur, minimizing downtime and maintenance costs.
Improve driver safety: AI-powered driver assistance systems can detect drowsiness, distracted driving, and potential hazards, reducing accidents and improving overall safety.

However, the full potential of AI can only be realized cost-efficiently when coupled with a modern, cloud-based technology stack. Legacy systems lack the scalability, computational power, and data accessibility needed for advanced AI applications. By transitioning to multi-tenant cloud platforms, trucking companies can unlock the true power of AI and gain a competitive edge in the market.

Workforce effects are the most substantial impacts of artificial intelligence across economies. The adoption of new tools will fundamentally affect what humans do, how humans do it, and what skills organizations need to hire for. While most of the automation efforts in the past century have targeted blue-collar manual labor, the wave of automation leveraging artificial intelligence is likely to affect most fundamentally white-collar positions in the so-called knowledge economy, in particular the operations of services.

3. AI TRANSFORMING THE TRUCKING INDUSTRY

The trucking industry, long known for its resilience and adaptability, stands at the precipice of a transformative era. Artificial intelligence (AI) is poised to change many aspects of operations today, from the vehicles themselves to driver management, dispatching, and back-office processes. This section explores the potential of AI to unlock new levels of efficiency, safety, and profitability for trucking companies.

Many fleet owners struggle with the amount of work and the amount of workarounds traditional transportation management systems create for them. The Nobel-prize-winning economist Robert Solow described this phenomenon famously by saying "You can see the computer age everywhere but in the productivity statistics." His student Erik Brynjolfsson, professor and senior fellow at the Stanford Institute for Human-Centered AI, projects that generative AI is different. He expects it to generate at least a 3% productivity gain per year and likens it to the impact of electricity on human economic activities. The opportunity for productivity improvements with modern TMSs can be expected to be similar.

Traditional transportation management systems (TMS), often siloed and on- premise, struggle to keep pace with the demands of a data-driven world. AI, on the other hand, thrives on data. It can analyze vast amounts of information on weather, traffic patterns, driver behavior, and vehicle performance, gleaning insights that were previously hidden. This enables AI-powered systems to:

Optimize routes and schedules: AI can dynamically adjust routes in real time, considering factors like traffic congestion, weather conditions, and driver preferences. This leads to reduced fuel consumption, improved on-time delivery rates, and better driver utilization.
Enhance predictive maintenance: AI can analyze sensor data from vehicles to predict potential failures before they occur, minimizing downtime and maintenance costs.
Improve driver safety: AI-powered driver assistance systems can detect drowsiness, distracted driving, and potential hazards, reducing accidents and improving overall safety.

However, the full potential of AI can only be realized cost-efficiently when coupled with a modern, cloud-based technology stack. Legacy systems lack the scalability, computational power, and data accessibility needed for advanced AI applications. By transitioning to multi-tenant cloud platforms, trucking companies can unlock the true power of AI and gain a competitive edge in the market.

Workforce effects are the most substantial impacts of artificial intelligence across economies. The adoption of new tools will fundamentally affect what humans do, how humans do it, and what skills organizations need to hire for. While most of the automation efforts in the past century have targeted blue-collar manual labor, the wave of automation leveraging artificial intelligence is likely to affect most fundamentally white-collar positions in the so-called knowledge economy, in particular the operations of services.

4. Identifying Value: Specific Applications and Benefits of AI

The potential of AI extends far beyond theoretical possibilities. It is already being implemented in various aspects of trucking operations, delivering tangible benefits:

Trucks: AI-powered autonomous driving systems, though still in development, hold immense promise for improving safety and efficiency. Moreover, AI can optimize fuel consumption by adjusting engine performance based on real-time data.
Drivers: AI-powered driver coaching systems analyze driving patterns and provide personalized feedback, improving safety and reducing fuel costs. Additionally, AI can automate tedious tasks like paperwork and compliance checks, freeing up drivers' time and reducing stress.
Dispatching: AI can optimize load assignments, matching drivers with the most suitable routes and loads based on their preferences, skills, and vehicle capabilities. This leads to increased driver satisfaction, improved operational efficiency, and higher margins.
Customer service: AI-powered chatbots can provide real-time shipment tracking information and answer customer inquiries, improving customer satisfaction and reducing workload for customer service personnel.
Backoffice: AI-powered tools can automate tasks like data entry, invoice processing, and expense management, freeing up staff time and improving accuracy.

However, it is crucial to acknowledge potential risks. AI models can generate errors, and relying solely on AI for critical tasks can be risky. Therefore, a human-in- the-loop approach is essential, where AI and humans work together, leveraging each other's strengths and mitigating weaknesses.

These are just a few examples of how AI is transforming the trucking industry with a clear value proposition: revenue growth and cost savings through automation and optimization, better decision-making, and improved risk management through proactive maintenance and safety measures.

4. Identifying Value: Specific Applications and Benefits of AI

The potential of AI extends far beyond theoretical possibilities. It is already being implemented in various aspects of trucking operations, delivering tangible benefits:

Trucks: AI-powered autonomous driving systems, though still in development, hold immense promise for improving safety and efficiency. Moreover, AI can optimize fuel consumption by adjusting engine performance based on real-time data.
Drivers: AI-powered driver coaching systems analyze driving patterns and provide personalized feedback, improving safety and reducing fuel costs. Additionally, AI can automate tedious tasks like paperwork and compliance checks, freeing up drivers' time and reducing stress.
Dispatching: AI can optimize load assignments, matching drivers with the most suitable routes and loads based on their preferences, skills, and vehicle capabilities. This leads to increased driver satisfaction, improved operational efficiency, and higher margins.
Customer service: AI-powered chatbots can provide real-time shipment tracking information and answer customer inquiries, improving customer satisfaction and reducing workload for customer service personnel.
Backoffice: AI-powered tools can automate tasks like data entry, invoice processing, and expense management, freeing up staff time and improving accuracy.

However, it is crucial to acknowledge potential risks. AI models can generate errors, and relying solely on AI for critical tasks can be risky. Therefore, a human-in- the-loop approach is essential, where AI and humans work together, leveraging each other's strengths and mitigating weaknesses.

These are just a few examples of how AI is transforming the trucking industry with a clear value proposition: revenue growth and cost savings through automation and optimization, better decision-making, and improved risk management through proactive maintenance and safety measures.

5. IMPACT ON SKILL REQUIREMENTS IN TRUCKING COMPANIES

Non-Technical Users

For most end-users, AI applications should be pretty easy to learn and use. The challenge most technology companies will have lies in accurately training models from the enormous amount of data available, and conveying the logic to the end user in a friendly way.

AI is often criticized for being a black box and people are afraid that we only know the answer without getting to know the path to get to that answer - creating anxiety and trust issues when interfacing with AI systems.

Unfortunately, the traditional TMSs that have been used for the last 20 to 40 years in the trucking industry were built primarily to store data, rather than to streamline workflows. As a result, legacy systems are “too much work” and cause users to complete certain tasks outside of the system. This makes implementing otherwise powerful AI technology in the workflow next to impossible. As we mentioned above, this is particularly warranted for tasks with a high cost of errors. One way to address this is to embed AI applications as “co-pilots” in the processes users complete so that they seek guidance from the AI or leverage them selectively to expedite otherwise tedious work.

Generally, for non-technical users, we expect workflows to become easier, more streamlined, and more efficient, but also demand a higher degree of professional experience, judgment, and critical thinking to counterbalance possible errors that models produce from being trained on non-exhaustive data or hallucinations.

Technical Users

The rise of AI will have implications for IT managers in trucking companies. Here are some key areas to consider:

Easier Access to IT Talent: Traditionally, mid-sized trucking companies have struggled to attract and retain IT talent and outsourced customization needs to outsourced service providers. In the future, AI-powered tools will make it easier for non-technical users to customize software, reducing the need for specialized outside developers.
Increased Engineering Productivity: In large trucking companies with in- house development capabilities, AI-powered co-pilots can assist engineers in writing and reviewing code, significantly boosting productivity and efficiency.
More Optimization Work: AI excels at complex optimization tasks. Advanced algorithms can optimize dispatching, load planning, and other areas, leading to better decision-making and improved outcomes. This will increase the need for data scientists, data engineers, and ai-engineers across the industry. Creating a stimulating, attractive environment for this highly sought-after talent pool and promoting the industry early on will be critical.

5. IMPACT ON SKILL REQUIREMENTS IN TRUCKING COMPANIES

Non-Technical Users

For most end-users, AI applications should be pretty easy to learn and use. The challenge most technology companies will have lies in accurately training models from the enormous amount of data available, and conveying the logic to the end user in a friendly way.

AI is often criticized for being a black box and people are afraid that we only know the answer without getting to know the path to get to that answer - creating anxiety and trust issues when interfacing with AI systems.

Unfortunately, the traditional TMSs that have been used for the last 20 to 40 years in the trucking industry were built primarily to store data, rather than to streamline workflows. As a result, legacy systems are “too much work” and cause users to complete certain tasks outside of the system. This makes implementing otherwise powerful AI technology in the workflow next to impossible. As we mentioned above, this is particularly warranted for tasks with a high cost of errors. One way to address this is to embed AI applications as “co-pilots” in the processes users complete so that they seek guidance from the AI or leverage them selectively to expedite otherwise tedious work.

Generally, for non-technical users, we expect workflows to become easier, more streamlined, and more efficient, but also demand a higher degree of professional experience, judgment, and critical thinking to counterbalance possible errors that models produce from being trained on non-exhaustive data or hallucinations.

Technical Users

The rise of AI will have implications for IT managers in trucking companies. Here are some key areas to consider:

Easier Access to IT Talent: Traditionally, mid-sized trucking companies have struggled to attract and retain IT talent and outsourced customization needs to outsourced service providers. In the future, AI-powered tools will make it easier for non-technical users to customize software, reducing the need for specialized outside developers.
Increased Engineering Productivity: In large trucking companies with in- house development capabilities, AI-powered co-pilots can assist engineers in writing and reviewing code, significantly boosting productivity and efficiency.
More Optimization Work: AI excels at complex optimization tasks. Advanced algorithms can optimize dispatching, load planning, and other areas, leading to better decision-making and improved outcomes. This will increase the need for data scientists, data engineers, and ai-engineers across the industry. Creating a stimulating, attractive environment for this highly sought-after talent pool and promoting the industry early on will be critical.

6. CHARTING THE FUTURE

The future of AI in trucking is bright, with exciting developments on the horizon:

Multi-tenant Cloud Adoption: The shift to cloud-based TMS platforms will accelerate, providing the scalability and computational power needed for advanced AI applications. This will give early adopters a significant competitive advantage.

Rise of Smart Vehicles: As the level of autonomy vehicles possess increases, they will require sophisticated dispatch systems powered by AI to optimize routes, manage traffic flow, and ensure safety.

Leveraging Advanced Optimization: AI-powered optimization will become commonplace, augmenting human decision-making in areas like dispatching, load planning, and maintenance scheduling. This will lead to significant efficiency gains and cost reductions.

6. CHARTING THE FUTURE

The future of AI in trucking is bright, with exciting developments on the horizon:

Multi-tenant Cloud Adoption: The shift to cloud-based TMS platforms will accelerate, providing the scalability and computational power needed for advanced AI applications. This will give early adopters a significant competitive advantage.

Rise of Smart Vehicles: As the level of autonomy vehicles possess increases, they will require sophisticated dispatch systems powered by AI to optimize routes, manage traffic flow, and ensure safety.

Leveraging Advanced Optimization: AI-powered optimization will become commonplace, augmenting human decision-making in areas like dispatching, load planning, and maintenance scheduling. This will lead to significant efficiency gains and cost reductions.

7. CONCLUDING THOUGHTS

The trucking industry stands at a pivotal moment, poised to be reshaped by the transformative power of Artificial Intelligence (AI). We have outlined the potential of AI to revolutionize operations and unlock new levels of efficiency, safety, and profitability for trucking companies. From optimizing routes and schedules to enhancing predictive maintenance and driver safety, AI is poised to fundamentally alter the landscape of the industry.

However, leveraging AI's full potential necessitates a paradigm shift. Embracing cloud-based solutions to unlock the power of big data is crucial, as are acknowledging the evolving talent landscape and proactively reskilling your workforce. By adopting a human-in-the-loop approach, where AI empowers rather than replaces human expertise, trucking companies can harness the true potential of AI and navigate the road to success.

The future of AI in trucking comes with many possibilities. As multi-tenant cloud adoption accelerates and smart vehicles rise on the horizon, the path towards an AI-powered future has never been clearer. Early adopters who embrace this transformation will enjoy a significant competitive edge, driving operational excellence and reaping the rewards of innovation.

7. CONCLUDING THOUGHTS

The trucking industry stands at a pivotal moment, poised to be reshaped by the transformative power of Artificial Intelligence (AI). We have outlined the potential of AI to revolutionize operations and unlock new levels of efficiency, safety, and profitability for trucking companies. From optimizing routes and schedules to enhancing predictive maintenance and driver safety, AI is poised to fundamentally alter the landscape of the industry.

However, leveraging AI's full potential necessitates a paradigm shift. Embracing cloud-based solutions to unlock the power of big data is crucial, as are acknowledging the evolving talent landscape and proactively reskilling your workforce. By adopting a human-in-the-loop approach, where AI empowers rather than replaces human expertise, trucking companies can harness the true potential of AI and navigate the road to success.

The future of AI in trucking comes with many possibilities. As multi-tenant cloud adoption accelerates and smart vehicles rise on the horizon, the path towards an AI-powered future has never been clearer. Early adopters who embrace this transformation will enjoy a significant competitive edge, driving operational excellence and reaping the rewards of innovation.

Gearing up for the Next Transformation:

The Role of Artificial Intelligence in Fleets of the Future

Gearing up for the Next Transformation:

The Role of Artificial Intelligence in Fleets of the Future

Gearing up for the Next Transformation: