AI research dominates every traditional industry. The times of doubt and neglect are past us at this point. The future is fascinating for every business. Heavy machinery manufacturing included. In this article, we tap into the realm of Artificial Intelligence research for autonomous transportation and more importantly the reasons why it matters for every modern digital entrepreneur.

This year Apple made a historic leap becoming the first American public company to cross $1 Trillion in value. This achievement has already been unlocked by bigger foreign companies but who cares. It’s not the fact of being worth a trillion that is sexy. It’s the fact that Apple is not a hardware engineering company in a traditional sense. Of course, it produces devices but, it is not bound to its production facilities. Apple sources everything from everywhere to the point where it becomes trivial where the products are made. It’s the place where they are designed that matters.

Apple’s thought leadership, iconic design, and exceptional marketing approach all powered by enormous financial infusions and complete independence, make the Cupertino giant unbeatable.

If there is nothing limiting your supply chain which is global and modular, what can possibly stop you from delivering more and better products every (fill in the blank)?

This puts traditional industries into a different perspective. How can they bolster innovation without the ability to fly off their brick-and-mortar production facilities? Are there ways to optimize production while staying in the manufacturing business that produces less but with more meaning? And what is the role of AI technologies in traditional industries of traditional countries?

Masterful engineering is endangered

If the reputation of the entire country can be affected by the companies it gives birth to, those are some exceptional companies. If we think about German engineering, we mean excellence. German industrial growth is rooted in the deconstruction of processes, precise engineering, and human resource management. Something gave a tremendous push to the development of the country’s finest manufacturing brands. And that something was innovation.

The automotive industry was pioneered by German design and production. Other heavy machinery manufacturers kept up. Add to that a tremendous resilience with which German production companies endured through multiple crises and you’ll get the chronicle of strenuous development that hinges on discipline and dedication.

Image credit: Fabian Oefner

This workflow became a tradition. Over time it acquired new abilities, morphed along with the development of modern technologies in what relates to hardware production methods and materials but nothing disruptive, unorthodox, and too opportunistic. German manufacturing industry remained very traditional and resistant to change. It took a governmental initiative called Industrie 4.0 to jump-start the much-needed motion towards the technologies of the future.

German automotive industry focused on excelling in engineering and production. Today you can hardly make an argument about the faultiness of German cars in that aspect. However, the next big thing is not a reliable car that operates smoothly for 10+ years.

The rapid pace of technological innovation in the allied spheres projects the same kind of expectation on the automotive industry.

While the pure engineering advantage is being systematically and effectively tackled by the Japanese and Korean auto groups, there is a realm which presents the most opportunities and is the most challenging. Self-driving cars and other autonomous vehicles require a somewhat conflicting look in a totally different direction — software.

Digital disruption is inevitable and needed

Software is something you can’t build off of discipline and meticulousness alone. A good piece of machinery nowadays requires interactive features and the ability to operate by itself. Artificial intelligence being the core of this operations relies on machine learning and data management. These categories are specifically confusing for hardware manufacturers as they showcase no tangible value. This made German car corporations reluctant to financing AI application research.

All the great (perhaps, the greatest) German car engineering in the world may potentially follow the path of being a cog in the wheel of a client-based company. Apple’s supply chain is a complicated network of great companies that you’ll never hear of.

Same way, AI-infused car startups owned by the American companies might steamroll European manufacturers and turn them into suppliers from what was once competition.

It’s not like there is no progress, but a large portion of it is being slowed down by legislation (Uber ban), obsolete company leadership models, bureaucratic hurdles and lack of coordination. On top of that, innovative culture is pop culture and 2015’s VW controversy added no public trust to the brand. As the result, the company has to double up on their efforts to regain trust and introduce new value. Think of all the resources for it that could otherwise have gone on research and innovation.

Legacy leadership model is obsolete

One of the ways to build a solid and trusted management is by growing it within the business. By gradually promoting employees, you get a traditional decision-making process and inherited operation. However, startup culture which is the driver of innovation of today does not rely on legacy leadership.

When it’s all about aggressive hiring, the employees in American tech startups are focused on vision and value more than on performance history and experience. At the same time, German labor laws are high-standard and were meant to be a societal wonder but in the current context are sluggish. It’s harder to fire people which opens an opportunity for underperformance (issues with migrant labor ethics) and lack of urgency.

AI radiates evolution

In the core of artificial intelligence lies the ability of potent enough computers to constantly learn without humans instigating every context. The level of automation Germany is at now is high enough to take care of the mechanical labor and completely replace humans and empower them for creative and analytical work. However, with AI there is a chance to make such advancements that used to be rendered impossible just a couple years ago.

We’re talking natural language processing, visual object recognition, smart spatial reasoning and many more.

The German economy being a driver for half the continent, harbors enormous potential for AI development. In fact, there aren’t a lot of other options at hand capable of beating the stigma of a quickly aging population and a potential trade warfare with the US. Automation beyond mechanical labor is the key to Germany’s dominance in the future.

AI boosts productivity regardless of the industry and its complicated specifics. The automotive industry is one of the biggest beneficiaries of early AI adoption. High predictability of tasks, fairly slow material technological development, and a vision could not only raise productivity but open new markets to the brands that aren’t traditionally associated with digital disruption.

Intelligent manufacturing on the rise

That being said, disruption doesn’t have to be extreme. We believe that one of the means of becoming a manufacturer of the future is by embracing intelligent manufacturing. Intelligent manufacturing uses connectivity, sensing, real-time collaboration, learning analysis, business data research, and processing, as well as environmental information to enable the integration and optimization of various aspects of a manufacturing.

Intelligent manufacturing stands on three pillars: people, management, and technology.

AI technology development reinvigorates the creation of new models and systems of intelligent manufacturing. New means integrated, digitalized, smart, green, and flexible. If all of these models are integrated into one manufacturing ecosystem, it can qualify as intelligent.

The industrial potential of AI

Since we started off with car industry, let’s stick to the topic and consider the AI’s opportunities to make an impact in product ideation, manufacturing, and business value. Autonomous cars are believed to become the safest commuting means by 2030. Total sales are expected to make it past 15% of global car sales. This will require a huge deal of data gathering, processing, and algorithm implementation. Building an AI and teaching it realms of human life is the long-term goal for the future. In other words,

The future of cars is not in the factory workshops but in computer labs.

The potential for autonomous vehicles reaches way further than just driverless taxis and car-sharing services. AI-powered vehicles will reshape the way logistics operates. The logistical hubs like Hamburg and Dortmund will be on the forefront of these changes. Autonomous and highly-efficient trucks and unpiloted cargo drones will redefine the supply chains, putting more emphasis on the quality and timeliness of delivery.

Autonomous vehicles

The most social impact comes from the application of AI for personal transportation. Autonomous cars are widely considered to be the future of mobility for humans. The efforts of the cutting-edge automotive industry leaders are reconsidering solutions of assisted driving and turning them into autonomous driving.

Most of the existing advanced driver assistance systems operate on rule-oriented programming and it is a double-edged sword. On one hand, the manual scenario input is accurate and disambiguous for the most part, on the other, there is only so much time and effort you can put into documenting the diversity of traffic situations.

Total transportation safety is possible within the complete and definite set of rules of behavior which the given human nature renders impossible.

On top of that, rule-based driving systems do not make use of the processed data received from radars, cameras, and other sensors. So far only humans are possible of processing this type of information which trivializes the sole idea of autonomous transportation.

To finish the transition to self-driving cars, manufacturers will have to embrace the modern AI development techniques like machine learning. For some of the rule-based systems, AI-powered modules have already become a reliable substitute. Apart from Google and Tesla, ahead of the curve are the autonomous driving startups like Argo.ai, Drive.ai, nuTonomy, and others. They are focused on building learning systems powered by AI on four general directions:

• Sensoring
• Data processing
• Planning
• Performing

All of these require extensive resource of both analytical and productive computational power. Due to the fact that there is no clear understanding how many scenarios the AI will have to learn in order to start making 100% right decisions, it’s hard to project the infrastructure of a safe autonomous vehicle system.

Technically, the computational power of every single car can be used to contribute to the common knowledge. Blockchain AF.

In this case, we can completely exclude human judgment which often causes disruption out of the driving process and let the AI systems figure it out in machine-to-machine interaction. There is so much work to be done in research, development, and customer preparation before autonomous vehicles become an accepted reality. This is where major brands and autogroups can find their sweet spot.

Giants of the automotive industry have the name but are too heavy to react to changes as quickly as the market needs. At the same time, autonomous driving startups struggle to generate a reputation in the direction that is majorly distrusted by most of the population. The synergy of the two worlds is where the future lies. Waiting for highly autonomous vehicles to push privately owned cars off the market is not a wise strategy. In contrast, if the boundaries between the private and public cars become blur, you’d want to be on the side of the progress and not defending the legacy workflow.

The predicted annual growth of autonomous vehicles sales is 40% after 2030. This dynamic obliges the market leaders of today to reevaluate their business models, production facilities, and distribution of financial resource for AI research and new tech development.

The change can help avoid stagnation and monopolization of the global market by the American companies dominating the research and experimenting with autonomous driving now.

For most of the tech companies not necessarily involved in AI development, the potential may lie in additional services provided for autonomous vehicles which will definitely be there once mass acceptance turns the heads of digital business owners to AI for transport.

Engineering excellence cannot be an accolade of the past. It is dynamic, moving, and extremely sensitive. Yesterday, Nike announced its partnership with Colin Kaepernick through a commercial that went viral. It ends with “It’s only crazy until you do it. Just do it.”

We at Shakuro are yet to figure out how to start making an AI engine. Some of the greatest manufacturers who can carve a flower out of stone don’t know how to breathe life into it. A little more than 100 years ago cars did not even exist. Today we wonder what each and every of us can do to make the industry better, safer, and cooler. Let’s not just witness the greatest time to explore the boundaries of reality; let’s be a part of it.

This story is published in The Startup, Medium’s largest entrepreneurship publication followed by +367,349 people.