In early 2015, South Korean technology giant, Samsung, made headlines with the launch of the world’s first ‘transparent’ semi-trailer combination. Developed in collaboration with Argentinian ad agency, Leo Burnett, the vehicle featured a front-facing video camera that live-streamed the driver’s view onto the trailer’s tailgate – allowing the following traffic to see what was ahead and overtake safely.
In the wake of the surprise unveiling – consumer electronics and automotive rarely mix – Samsung’s intelligent truck not only garnered waves of media attention, but also sparked a debate on how Artificial Intelligence (AI) may be applied to modern trailer design.
After a period of lacklustre sales and slow innovation, engineers suddenly felt empowered to dream big again: Inspired by the concept’s success outside the trucking community (see breakout box), the discussion turned from showing what’s in front of the vehicle to ‘upgrading’ the information sent to the rear by adding live updates on speed and road conditions – a risk assessment tool administered by the vehicle itself. All that was needed was an intelligent operating system, they argued.
Even though regulatory and liability issues have since stifled that idea, the notion of bringing futuristic technology to the tradition-rich transport equipment industry continues to reverberate – most recently fuelled by the launch of the Tesla Semi, which will supposedly come with an enhanced version of the driver assistance system already found in the company’s electric passenger vehicles.
While it’s still unsure whether or not the Semi will be able to “transform into a robot, fight aliens and make one hell of a latte”, as Musk teased in the lead-up to the launch on 16 November 2017, it’s safe to say Tesla paved the way for the return of AI into the collective consciousness of not just the trucking community, but the general public. It did, however, forget to pick up where Samsung left off two years ago and take the trailer into the equation, too.
Planned or not, German OEM Schmitz Cargobull picked up on the oversight and steered the discussion back to trailing equipment shortly after curtain fell on the Tesla. With the launch of a hip new start-up dedicated to digitalising fleet management, transport and logistic processes, it positioned itself at the forefront of the digitisation movement, cleverly riding the wave of Tesla’s bold foray into the world of commercial trucking.
“Digital solutions for managing and monitoring global supply chains are becoming increasingly important,” CEO Andreas Schmitz (pictured) commented less than a week after the Tesla outing, noticeably proud to finally bring a taste of Silicon Valley to European trailer building.
Backing Schmitz’ optimism is a new piece of research into digitisation published by McKinsey*. Less than three years after Samsung and Leo Burnett teased the transport industry by showcasing just how impactful the integration of smart technology with traditional trucking could be – and conveniently coinciding with the Tesla launch – the consultancy found that AI has finally reached the “commercialisation tipping point”.
“As a result of several technology advancements that are now converging, major investments by technology companies and start-ups, and demand from businesses, AI is starting to have a major impact across markets,” the company’s new report says, much in line with the media echo from the Tesla/ Schmitz showcase. Underestimating the potential of AI, it adds, could prove a costly mistake.
“Fundamentally different from the current digital revolution, the coming AI surge will likely redefine 21st-century business practices,” it warns. “Company leaders … need to invest now to turn AI into value that can be captured. While some may choose to wait and see at this point, such a posture will make later attempts to harness AI even more difficult in the face of disrupting competitors.”
The risk of sleeping away a crucial technological milestone is especially high for businesses in the manufacturing and automotive space, McKinsey continues, which generally don’t seem to recognise the potential of AI in the same way Schmitz Cargobull or Tesla have.
“Advanced industries such as automotive … and industrial manufacturing could harness AI … to discover entirely new ways of making things better, cheaper and faster,” McKinsey’s Wouter Baanz, Christopher Thomas and Joshua Chang argue. “Instead, they take ad-hoc approaches that might not scale, cannot prove out new technologies, and fail to build systematic capabilities. Ultimately, such efforts make little impact.”
While projections vary, forecasters seem to align on an annual AI revenue-growth rate of roughly 50 per cent, according to Baanz et al. Analysts are equally bullish about the technology, telling McKinsey it has the potential to drive “significant productivity improvements” in the decade to come– especially in vehicle manufacturing.
And yet, AI is still not a strategic priority for 43 per cent of C-level executives worldwide. The problem, according to McKinsey, lies in applying big-picture concepts to concrete, specific applications where AI can create real life value – an art form Elon Musk is renowned for mastering. The Tesla Semi, for instance, is build around a fully integrated equipment leasing package that could revolutionise the very notion of truck ownership as we know it – applying the same, smart technology across multiple business units.
“[Beyond Tesla’s approach,] operational enhancements could cover research, product development, manufacturing, supply-chain management, or marketing and sales,” Baanz et al. theorise. The problem, they add, is to bring them underway in the first place. “This might include introducing AI-enhanced predictive maintenance in manufacturing or developing R&D applications that could recommend modification areas to drive productivity,” they explain, stressing AI could not only drive product innovation, but lead to the birth of whole new business models like in the case of Tesla.
Questions like ‘could [an AI application] enable product substitution or speed up product commoditisation’ should be more prevalent in everyday planning, they argue, adding that alleged technological hurdles often limit a company’s ability to respond o change, and with it the ability to take action.
The solution may lie in collaborating with trailblazers like Elon Musk and Andreas Schmitz to create intellectual economies of scale: “Experience shows that many times, AI combined with other business concepts creates the real disruption,” it says. “For instance, think about self-driving robo-taxis enhanced with AI.”
Another suggestion is the resurrection of corporate role-playing. “For example, have teams role play as start-ups, Internet companies, and direct competitors, and then play out how to invest in and use AI in the industry. This can enhance brainstorming and quickly separate reality from theory,” the consultancy recommends – an approach Schmitz Cargobull is following by leaving its new subsidiary on a long leash and allowing it to explore freely how digitisation may apply to the trucking industry – even if it involves a competing OEM.
The ultimate goal of such an exercise, according to McKinsey, is to emancipate companies from the notion that AI could make their respective core businesses obsolete. “Instead expect it to inject that core with a powerful transformative juice that turns it into a learning organization,” the company’s new report says. “Exploiting this ability will be the key to AI success for most companies.”
While potentially overestimated in the near term – even Elon Musk is not able to say when exactly the Tesla Semi will hit the road – the long-term impact of Artificial Intelligence on the transport equipment space is expected to be profound. As such, McKinsey is urging leaders to follow Musk and Schmitz’s example and “get some skin in the game” now.
*Wouter Baan, Joshua Chang and Christopher Thomas: How advanced industrial companies should approach artificial-intelligence strategy. Beijing, Taipei, 2017