Category: Industry

Who will be to blame when a self‑driving car causes an accident?

The arrival of autonomous vehicles promises to drastically reduce accidents, but it also opens a fascinating debate: what happens when the machine fails? For more than a century, road responsibility has been simple: the driver. But in a vehicle that makes decisions without human intervention, that logic breaks down. We’re no longer talking only about hands on the wheel, but about algorithms, sensors, suppliers, and software updates. The question stops being technical and becomes philosophical, legal, and economic.

Insurance companies are undergoing a major transformation. Traditionally, they calculated risk based on human behavior: driving history, age, area of residence. With autonomous cars, risk shifts toward the quality of the automated driving system. Should someone pay more for a car with a “less reliable” algorithm? Will there be specific policies for each manufacturer, just as today there are for each model? Some even wonder whether insurers will need access to the vehicle’s logs to reconstruct the accident—something that opens a debate about privacy and data ownership.

The role of manufacturers also becomes central. If a software failure causes an accident, is the brand responsible, or the supplier of the computer vision module, or the system integrator? In today’s automotive industry, a car is an ecosystem of components from multiple companies. An error in a LIDAR sensor made by a third party can trigger a chain of incorrect decisions. This forces a rethinking of civil liability: how do you distribute blame when the “driver” is a set of algorithms developed by different companies?

Owners are not left out of the debate. Even if they don’t drive, they remain responsible for maintaining the vehicle. What happens if an accident occurs because the user didn’t install a critical update? Or if they disabled safety features? The line between “use” and “control” becomes blurred. Just as an outdated phone is a risk, an autonomous car without updates could become a danger on the road. This opens the door to new legal obligations: mandatory updates, software audits, and periodic certification of the autonomous system.

Ultimately, responsibility in autonomous vehicles will not be binary but shared. A hybrid model where the owner, manufacturer, software provider, and insurer each have different roles depending on the type of failure. Technology is advancing faster than legislation, and the real challenge will be designing a legal framework that distributes blame fairly without slowing innovation. Because the question is not whether accidents will happen with autonomous cars, but how we will decide who must answer for them when they do.

Exposure to AI by Professional Sectors (EMEA Region)

The adoption of Artificial Intelligence in the EMEA region progresses at very different speeds depending on the sector. It’s not just about technology: it’s a matter of organizational culture, digital maturity, regulation, and competitive pressure. Understanding this map is key to anticipating risks, opportunities, and the redistribution of value in the coming years.

1. Financial Services (FinTech, Banking, Insurance) Exposure: Very High

AI is already part of the core operations: scoring, fraud prevention, regulatory automation, risk analysis, reporting, portfolio management, and customer experience. Regulatory pressure (DORA, AI Act) accelerates the professionalization of AI use and requires integrating auditable and traceable models.

2. Technology, Telecommunications, and Software Exposure: Very High

This sector sets the pace. From development automation (DevOps + AI) and network optimization to internal copilots for support and operations. Here, AI is not a complement: it’s a productivity multiplier.

3. Retail and eCommerce Exposure: High

Demand forecasting, dynamic pricing, smart logistics, personalization, and inventory management. AI is redefining efficiency and customer experience, especially in markets with strong digital competition.

4. Industry and Manufacturing Exposure: Medium-High

AI is integrated into predictive maintenance, quality control, digital twins, and energy optimization. The challenge: plant modernization and integration with legacy systems.

5. Healthcare and Pharmaceuticals Exposure: Medium-High

Assisted diagnosis, image analysis, drug discovery, and hospital management. Regulation and clinical responsibility slow adoption, but the potential is enormous.

6. Public Sector and Government Administration Exposure: Medium

Administrative processes, citizen services, data analysis, and document automation. The challenge is twofold: technological modernization and strict compliance with the European regulatory framework.

7. Education and Training Exposure: Medium

AI for personalized learning, automated assessment, and content generation. Adoption depends on national policies and investment levels in digitalization.

8. Energy and Utilities Exposure: Medium

Grid optimization, consumption forecasting, asset management, and energy transition. AI is key to efficiency, but critical infrastructure requires caution and robustness.

9. Legal, Compliance, and Consulting Exposure: Medium-Low

Document automation, contract analysis, and specialized copilots. The potential is significant, but adoption depends on trust, accuracy, and professional responsibility.

10. Construction, Traditional Logistics, and Transportation Exposure: Low-Medium

AI is used in planning, safety, routing, and maintenance, but digitalization in the sector progresses slowly.

Conclusion: The AI exposure gap is widening

The EMEA region shows a clear pattern: sectors with higher competitive or regulatory pressure adopt AI faster, while sectors with lower digitalization or tighter margins move more cautiously. The question is no longer whether AI will transform each sector, but when—and with what impact on people, processes, and the value generated.

Volkswagen as a Warning Sign: German Industry Enters a Critical Zone

Volkswagen is facing massive layoffs of up to 100,000 workers and the possible closure of four plants in Germany, a move that symbolizes the severity of the structural problems affecting the German economy and industrial sector.

A symptom of a larger issue: Germany’s industry in decline

Volkswagen’s situation is not an isolated case but rather a reflection of a broader deterioration in Germany’s industrial economy. Since 2022, Germany has suffered a combination of factors that have eroded its competitiveness:

  • A drop in industrial profits, especially in automotive, machinery, and chemicals.
  • Excess production capacity in key sectors such as automotive, where Volkswagen acknowledges that its European plants could produce 500,000 more cars than needed.
  • A transformation of the global market, with China pressuring prices and margins, and the United States imposing tariffs that affect European manufacturers.
  • Weak international demand, which has reduced deliveries and profits for giants like Volkswagen, whose profits fell by 28% in 2026.

This set of factors has led to an unprecedented restructuring: model reductions, cuts in production capacity, and workforce adjustments that could exceed 50,000 layoffs in Germany alone before 2030.

The decisive role of energy costs since the start of the conflict in Ukraine The conflict between Russia and Ukraine, which began in February 2022, triggered an energy crisis that hit Germany particularly hard due to its historical dependence on Russian gas.

Direct impact on German industry:

  • A multiplication of gas and electricity costs, critically affecting energy‑intensive sectors such as chemicals, metallurgy, automotive, and advanced manufacturing.
  • Loss of competitiveness compared to the United States, where energy costs are much lower thanks to cheap natural gas.
  • Increased regulatory and environmental costs, which Volkswagen describes as “headwinds worth tens of billions of euros.”
  • Production relocation, with German companies shifting operations to countries with cheaper energy (U.S., China, Eastern Europe).

The automotive industry — which depends on large energy consumption in manufacturing, painting, and logistics — has been especially vulnerable. Rising energy costs have eroded margins and accelerated the need for cuts and restructuring.

How both phenomena are connected The energy crisis is not the only cause, but it is a decisive accelerator of Germany’s industrial deterioration:

  • Soaring energy costs → higher production costs Volkswagen acknowledges that its costs remain 20% higher than those of global competitors.
  • Reduced margins → need for massive cuts Falling profits and deliveries force reductions in workforce, plant closures, and a simplified model lineup.
  • Lower competitiveness → loss of market share to China and the U.S. German industry is losing attractiveness as a production hub.
  • Deep restructuring → layoffs and factory closures Volkswagen’s plan to eliminate up to 100,000 jobs and close four plants is the most visible manifestation of this crisis.

Conclusion The layoffs planned at Volkswagen are the tip of the iceberg of a German industrial crisis driven by:

  • soaring energy costs since the conflict in Ukraine,
  • global competitive pressure,
  • excess production capacity,
  • falling profits and demand,
  • and an industrial model that no longer fits today’s market.

Germany’s economy, traditionally powered by its industrial strength, is facing a forced transformation that will shape the next decade.

Exposure to AI by Professional Sectors (EMEA Region)

The adoption of Artificial Intelligence across the EMEA region is progressing at very different speeds depending on the sector. It’s not just about technology: it’s a matter of organizational culture, digital maturity, regulation, and competitive pressure. Understanding this landscape is essential to anticipate risks, opportunities, and how value will be redistributed in the coming years.

1. Financial Services (FinTech, banking, insurance) Exposure: Very High AI is already part of the core operations: scoring, fraud prevention, regulatory automation, risk analysis, reporting, portfolio management, and customer experience. Regulatory pressure (DORA, AI Act) accelerates the professionalization of AI use and requires integrating auditable and traceable models.

2. Technology, Telecommunications, and Software Exposure: Very High This sector sets the pace. From development automation (DevOps + AI) and network optimization to internal copilots for support and operations. Here, AI is not a complement: it is a productivity multiplier.

3. Retail and eCommerce Exposure: High Demand forecasting, dynamic pricing, smart logistics, personalization, and inventory management. AI is redefining efficiency and customer experience, especially in markets with strong digital competition.

4. Industry and Manufacturing Exposure: Medium-High AI is integrated into predictive maintenance, quality control, digital twins, and energy optimization. The challenge: plant modernization and integration with legacy systems.

5. Healthcare and Pharmaceuticals Exposure: Medium-High Assisted diagnosis, image analysis, drug discovery, and hospital management. Regulation and clinical responsibility slow adoption, but the potential is enormous.

6. Public Sector and Government Administration Exposure: Medium Administrative processes, citizen services, data analysis, and document automation. The challenge is twofold: technological modernization and strict compliance with the European regulatory framework.

7. Education and Training Exposure: Medium AI for personalized learning, automated assessment, and content generation. Adoption depends on national policies and investment levels in digitalization.

8. Energy and Utilities Exposure: Medium Grid optimization, consumption forecasting, asset management, and energy transition. AI is key to efficiency, but critical infrastructure requires caution and robustness.

9. Legal, Compliance, and Consulting Exposure: Medium-Low Document automation, contract analysis, and specialized copilots. The potential is significant, but adoption depends on trust, accuracy, and professional responsibility.

10. Construction, Traditional Logistics, and Transportation Exposure: Low-Medium AI is used in planning, safety, routing, and maintenance, but digitalization in the sector progresses slowly.Conclusion: The AI exposure gap is widening The EMEA region shows a clear pattern: sectors with higher competitive or regulatory pressure adopt AI faster, while sectors with lower digitalization or tighter margins move more cautiously. The question is no longer whether AI will transform each sector, but when—and with what impact on people, processes, and the value generated.

Where is AI ​​going?

Artificial Intelligence (AI) is becoming an increasingly ubiquitous technology, and its influence in various fields is increasing rapidly. Experts predict that AI will be combined with various technologies to create seamless workflows, providing operational transparency and automation capabilities. Furthermore, language and vision analysis will be one of the most important applications of AI.

AI is expanding into a wide range of fields, and its impact on human interaction is growing rapidly. AI is expected to have a huge impact on healthcare systems, from diagnosis to epidemic prevention. In addition, AI can also play an important role in agricultural production, helping farmers decide when to plant and harvest.

Interestingly, the concept of AI has been around for centuries, and the idea of ​​automating reasoning and intelligence has been under exploration since ancient times. However, modern AI is done by digital computers and originated in 1956 with the pioneers of AI.

In short, AI is becoming an increasingly ubiquitous technology, and its influence in various fields, from health to agricultural production, is growing rapidly. AI is becoming a critical technology for automating workflows and improving operational transparency.

The reason for the shortage of raw materials and its impact on the industrial sector

The high demand for equipment and installation materials, as a result of the return to normality after the pandemic, has left an uncertain outlook for manufacturers and suppliers in the sector, as the supply begins to decline, prices increase, and all as a result of the shortage of raw materials. A shortage of raw materials, semiconductors, electronic components, etc., which affects the entire value chain, from manufacturers to distribution, installers, as well as developers, maintenance and reform companies, etc.

The lack of electronic components, copper, steel, plastics, etc. It has already caused the partial stoppage of production of several automobile manufacturers, and is beginning to put stress on other auxiliary industries or manufacturers of original machinery -OEM-, by not having all the necessary elements to manufacture.

And the forecasts are not very positive, transferring the tension of the entire supply chain until the year 2022. A problem on a global scale that is already one of the great concerns of the sector. According to data from the Spanish Association of Cable, Electrical Conduit and Fiber Optic Manufacturers, FACEL, the price of PVC has already increased by 20%, copper by 18.5% and aluminum by 15.4%, in the first quarter of 2021.

The origin of all the problem is in the US and its economic incentive policies, and China, where the Asian giant already foresees a 7.5% growth of its economy, being also the largest consumer of raw materials, chips and semiconductors in the world. world. The large stockpiling that they have made of these components has generated shortages in the rest of the countries, and prices have risen since mid-2020. Fortunately, the Chinese government has recently decided to release its strategic reserves of copper, aluminum and zinc to deflate prices to world level. Although the collapse of seaports in Asia is also problematic, where delays in container shipments accumulate, and growing demand has tripled the cost compared to 2020.

The emergence of green technologies, such as the electric vehicle and its infrastructure, also has its influence. The demand forecasts for this green mobility will increase from 2 to 8 million in 2025. The impulse towards this electric mobility and the growing production of these vehicles, also stresses the availability of chips and metals such as copper, silver, platinum, which are vital raw materials for these sustainable vehicles.

Demand for semiconductors and chips will not stop growing

To all this is added teleworking, and the digitization of everything that surrounds us. This new trend as a result of the pandemic has catapulted the demand for consumer electronics, computers and all kinds of devices that facilitate work from home.

What started as a momentary mismatch between supply and demand has turned into a perfect storm, where demand is unstoppable, growing, the forecast was wrong, in contrast to a highly concentrated chip and semiconductor manufacturing industry (83% of the world production is concentrated in Taiwan and South Korea), as is that of metals. There are those who claim that chips have become as scarce and coveted as gold, unable to keep up with demand.

The demand for chips will not stop growing in the present and in the future. The reason? More and more products incorporate a chip. And with the arrival of 5G and the connectivity imposed by digitization, it will be a structural trend. Thermostats, light bulbs, cars, bicycles, refrigerators, etc … everything that surrounds us is / will be backed by an integrated circuit.