Only a few years ago the thought of driverless cars cruising down our streets seemed crazy; something we’d see only in sci-fi movies. However, with two million autonomous miles already logged and analysts predicting that 10% of all new vehicles will have self-driving capabilities by 2021, autonomous vehicles are closer than we think.
What lessons can businesses learn from autonomous vehicles, as they embark on the era of the Autonomous Enterprise? Here are 10 lessons you need to know:
With autonomous cars, the problem is safety. Humans get tired and distracted behind the wheel, as evidenced by over 90% of traffic fatalities attributed to human error. Remove humans from the driving equation, and cars can be safer.
Similarly, the Autonomous Enterprise, one that can self-drive and self-optimize, addresses several problems including:
Getting to a fully-autonomous car is a journey that takes time. It comprises five levels of automation – from level one, driver assistance, to level five, full autonomy with no human interaction whatsoever.
Businesses can expect a similar journey to the Autonomous Enterprise. Technology – and more importantly the network – is the gateway; it is what connects technology to people. Network automation comprises a similar five-stage journey. The beauty of automation is that enterprises can do so at their own pace; starting and stopping at different stages along the journey.
An autonomous car is not enabled by one single technology or component, but rather by a combination of technologies. These include automatic braking, in-car virtual assistants, acceleration and lane-switching technologies, and more.
Likewise, the Autonomous Enterprise is enabled by many different elements: technologies, products, and software applications. Key attributes include a software-driven infrastructure, analytics, automation, security, and ML/AI.
The most advanced autonomous cars today have Level 3 automation, with advanced driver-assistance systems. Getting to full autonomy – Level 5 automation – requires vehicle-to-everything communication. The cars need to be able to communicate with the ecosystem around them, including other autonomous vehicles and smart infrastructure.
Likewise, the journey to a fully Autonomous Enterprise will stretch beyond the traditional walls and networks of today. It requires an open and standards-based ecosystem, where IT and OT converge, and where information flows freely beyond the network perimeter.
Experts believe fully-autonomous cars will come first to controlled environments, such as retirement homes and private communities, that can be easily and thoroughly mapped. Bringing fully-autonomous cars to complex, urban areas will take longer.
The same logic applies to enterprise environments. Technologies such as ML/AI, critical to enabling autonomy, work best in environments where the construct of the knowledge base is narrow. Enterprises should look to targeted scenarios – such as security anomaly detection or Wi-Fi RF optimization — for the greatest return on initial AI/ML investments.
Humans are creatures of habit. We don’t like change; we want to be in control. Three in four Americans are afraid to ride in a self-driving car. This fear of autonomy is not new; back in the 1950s people were equally afraid of driverless elevators, something we don’t think twice about using today.
Only time will tell how quickly and to what extent enterprises will be ready to give up control within the workplace environment. There is optimism – in IDC’s ‘The Future of Work’ survey, 47% of people believe AI will have a positive effect on their organization’s jobs. Human buy-in is key in the network automation journey.
What if an autonomous car is hacked? What if someone who isn’t the driver takes control of the vehicle? A report on the impact of hacking attacks on autonomous cars in New York City shows security is a concern. Also, driverless cars must collect and store a lot of sensory data to understand what is going on around them. What data is collected and with whom that data is shared has led to privacy concerns.
Similarly, an autonomous network needs to gather a lot of sensory data to run efficiently. Data about where, when, and how long users and devices connect. Data that allows the network to learn, self-drive, and self-optimize. Who does that data get shared with? What happens if the knowledge base of data gets hacked and its integrity compromised? Privacy and network security need to be top of mind for all businesses.
The introduction of autonomous cars has raised ethical and philosophical questions, such as The Trolley Problem. Should a self-driving car be programmed to protect the driver at all costs, or to do the least amount of damage within the overall society? Should a car be programmed to break the law by driving onto a curb to avoid a child on the road? These types of questions need to be considered in the creation and adoption of new technologies.
As enterprises adopt artificial intelligence technologies, similar considerations are called for. Creating an ethical AI is essential. The Amazon recruitment ML engine with its bias against women is an example of what can go wrong when ML/AI is trained on biased data.
As autonomous cars inch closer to reality, the focus has shifted from technology to the experience that car manufacturers want to deliver. What will the driverless experience be like? Humans spending more time looking at their screens? At CES 2019, carmakers showed off vehicles with perfume-puffing headrests, augmented reality video displays, and all manner of in-car entertainment.
In the midst of technology discussions, it is often forgotten that the Autonomous Enterprise is about people – creating superior experiences and changing outcomes for the better. Whether leveraging technology to accelerate medical diagnoses, enhance classroom learning, or personalize retail experiences, it starts and finishes with people, and delivering extraordinary experiences that allow businesses to stand out from the competition.
There is an expression, “Time waits for no man,” meaning the processes of nature continue, no matter how much we might like them to stop. The same can be said for technology in today’s digital era. Whether we like it or not, technology is advancing at a dizzying pace. Autonomous cars will make their way onto our roads regardless of whether we are ready.
The Autonomous Enterprise, and technologies such as ML/AI, promise to disrupt enterprises in ways we have yet to imagine. Businesses that embrace the change, and take the opportunity to reinvent themselves, will endure and thrive in the long-term. As with autonomous cars, enterprises need to fasten their seatbelts and prepare for the journey ahead.
