Adding more features to vehicles, especially in the area of safety, is often seen as a natural step toward autonomous driving. However, despite rapid advances in technology, fully autonomous vehicles are unlikely to become mainstream within the next decade. There are multiple obstacles: technical, economic, and social that still need to be addressed.
The Cost Challenge
One of the most immediate barriers is cost. Machine-operated systems that control vehicles are expensive, requiring significant computational resources and energy as they take on increasingly complex responsibilities. In comparison, human drivers remain highly efficient and cost-effective. While costs for AI systems may decrease over the next three to five years, making them more competitive with human labor, this is only part of the equation. Other, deeper challenges remain.
Responsibility: The Human Factor
A fundamental concern lies in responsibility. Humans have an innate understanding of risk and consequence - we value our own survival, plan for the future, and make decisions with a sense of accountability. Computers, by contrast, lack awareness or genuine concern. While AI can be programmed to simulate caution, it cannot truly understand responsibility. Entrusting AI with control over vehicles, where mistakes can have catastrophic consequences, raises critical ethical and safety questions. Until AI can demonstrate true responsibility, autonomous driving will remain limited to controlled environments and pilot programs.
Employment and Societal Implications
Autonomous vehicles also pose societal challenges, particularly in employment. Millions of people worldwide rely on driving as their profession. Widespread adoption of self-driving vehicles will require governments and companies to manage workforce transitions, creating opportunities for retraining and alternative employment. As a result, fully autonomous fleets are likely several generations away from broad-scale implementation.
Shifting the Focus: AI in Operations
While fully autonomous vehicles remain a long-term vision, AI is already transforming transportation companies in other ways. Every transportation business has two groups of processes, for example:
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Core operational processes: logistics, driver management, vehicle maintenance.
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General business processes: HR, accounting, office management, etc.
AI can automate both types, either through third-party software or internally developed solutions. For core operations, companies benefit most by building their own AI-powered tools, tailored to their specific processes.
Building an AI Integration Framework
Successful AI adoption requires a structured approach. In Gurtam we succeeded with the following framework:
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An AI integration team: Led by a respected, motivated leader with deep experience. This team should combine business understanding and technical expertise to set realistic goals and guide implementation.
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Access to company knowledge: Documentation, CRM data, and operational processes must be cleaned, updated, digitized, and structured before AI systems can use them effectively. This preparatory work is often the most time-intensive part of the process.
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Executive oversight: Leadership must provide ongoing guidance and feedback to align AI solutions with business realities.
If a company relies on third-party providers, access to internal data becomes critical. Without it, AI tools cannot fully adapt to a company’s unique operations, which is why building internal AI capabilities is often the more strategic and safe approach.
The Opportunity for Transformation
Creating in-house AI tools has never been more accessible. Modern AI can function as a digital software engineer, developing applications 24/7 without requiring deep technical training. Even small-scale implementations, like data classification, image recognition, or document analysis, can quickly deliver measurable value.
The key is to start experimenting. Begin with automating existing processes, gain experience, and gradually develop solutions for the industry. Over time, transportation companies can evolve from technology users into technology creators, potentially offering solutions to those who have not yet embraced AI.
Currently the transportation industry is on the edge of disruption. Large Silicon Valley companies are entering traditional sectors like transportation, and the “old”, “established” companies that adapt early will define the future. By embracing AI strategically, transportation businesses can improve operations, create value, and position themselves as leaders in an increasingly technology-driven world.
The question is clear: will you wait to be disrupted, or will you take the lead in transforming your industry?
This article is based on a presentation by Gurtam’s founder and CEO, Aliaksei Shchurko, delivered at the Transport Innovation Forum on October 15, 2025, in Vilnius.
