Tag Archives: industrialization

15 Hurdles to Scaling for Driverless (Part 3)

This is the third part of a three-part series. You can finds part 1 here and part 2 here.

Successful industrialization of driverless cars will depend on getting over many significant hurdles. Failure only requires getting tripped up by a few of them. In part two of this series, I outlined seven key hurdles to industrial-size scaling of driverless cars. Overcoming hurdles to scaling is not enough, however.

In this concluding article, I explore the challenges to broader market acceptance. I outline eight additional hurdles related to trust, market viability and managing secondary effects. All must be overcome for driverless cars to truly revolutionize transportation.

Trust. It is not enough for developers and manufacturers to believe their AVs are good enough for widespread use, they must convince others, too. To do so, they must overcome three huge hurdles:

8. Independent verification and validation. To date, developers have kept their development processes rather opaque. They’ve shared little detail about their requirements, specifications, design or testing. An independent, systematic process is needed to verify and validate developers’ claims of their AVs’ efficacy. Many are likely to demand this, including policy makers, regulators, insurers, investors, the public at large and, of course, customers. The best developers should embrace this—it would limit liability and distinguish them from laggards and lower-quality copycats.

9. Standardization and regulation. Industry standards and government regulation cover almost every aspect of cars today. Industrialization of driverless cars will require significant doses of both, too. Standards, especially those enforced by government regulation, ensure reliability, compatibility, interoperability and economies of scale. They also increase public safety and reduce provider liability.

10. Public acceptance. Most new products take hold by attracting early adopters. The lessons and resources from that initial success help developers “cross the chasm” to mainstream success. The industrialization of AVs will depend on much earlier and broader public acceptance. AVs affect not only the early-adopting customers inside them, but also every non-customer on and near the roads those AVs travel. Without widespread acceptance—including by those who would not choose to ride in the AVs—industrialization is not likely to be allowed.

See also: Where Are Driverless Cars Taking Industry?  

Market Viability. The next three hurdles deal with whether AV-enabled business models work in the short term and the long term, both in beating the competition and other opponents.

11. Business viability. Analyses of AV TaaS business models are generally optimistic about the possibility of providing service for much less than the cost of human-driven services or personal car ownership. Current cost-per-mile estimates are nowhere near long-term targets, however. Most players are also underestimating the cost to scale. It remains to be seen whether rosy market plans will survive contact with the marketplace.

12. Stakeholder resistance. As the old saying goes, one person’s savings is another’s lost revenue. The industrialization of driverless cars will require overcoming the resistance of a large host of potential losers, including regulators, car dealers, insurers, personal injury lawyers, oil companies, truck drivers and transit unions. This will not be easy, as the potential losers include some of the most influential policy shapers at federal, state and local levels.

13. Private ownership. AV TaaS services are only a waypoint on the path to transformation of the private ownership market. If AVs are to revolutionize transportation, they will have to appeal to consumers who have long preferred to own their own cars. Privately owned cars account for the vast majority of all cars and all miles driven.

Secondary Effects. Technology always bites back. The industrialization of AVs could induce huge negative secondary effects. Most will unfold slowly, but two consequences are already concerning and must be addressed as part of the industrialization process.

14. Congestion. Faster, cheaper and better transportation will deliver greater economic opportunity and quality of life—especially for those who might otherwise not have access to it, like the poor, handicapped and elderly. But, it might also cause a surge in congestion by driving up the number of vehicles and vehicle miles traveled. This happened with ride-hail services, including Uber and Lyft. According to a recent study by the San Francisco County Transportation Authority, for example, congestion in the densest parts of San Francisco increased by as much as 73% between 2010 and 2016. The ride-hail services collectively accounted for more than half of the increase in daily vehicle hours of delay.

15. Job loss. Some argue that the history of technology, including transportation technology, shows that new services will create more jobs, not less. Few argue, however, that the new jobs go to those who lost the old ones. There’s no getting around the fact that every AV Uber means one less human Uber driver—even if other jobs are created for engineers, maintainers, dispatchers, customer service reps, etc. The same holds true for AV shuttles, buses, trucks and so on. Early AV TaaS providers will operate under an intense spotlight on this issue. Providers will have to anticipate and ameliorate potential public and regulator backlash on job loss.

* * *

There’s an old saying in Silicon Valley that one should never mistake a clear view for a short distance. The revolutionary potential of AVs is clear. Yet, we are still far from the widespread adoption needed to realize their benefits.

Don’t mistake a long distance for an unattainable goal, though. As a close observer, I am enthusiastic (and pleasantly surprised) by the progress that has been made on AV technology. Leading developers like Waymo, GM Cruise, nuTonomy and their diaspora have raced to build AVs and progressed faster than many, just a few years ago, thought possible.

See also: Driverless Cars and the ’90-90 Rule’  

Industrialization is a marathon, not a sprint. It depends on overcoming many hurdles, including the 15 I’ve laid out. The challenges of doing so are great—likely greater than many current players (and their investors) perceive and are positioned to address. New strategies are needed. A shakeout is likely.

That’s how innovation and market disruption work. That is why most contenders fail and why outsized rewards go to those who succeed. Whoever thought that a phone maker or a search engine company could be worth a trillion dollars? Is it outlandish to believe, as I still do, that driverless cars would be worth multiple trillions?

How Machine Learning Changes the Game

Insurance executives can be excused for having ignored the potential of machine learning until today. Truth be told, the idea almost seems like something out of a 1980s sci-fi movie: Computers learn from mankind’s mistakes and adapt to become smarter, more efficient and more predictable than their human creators.

But this is no Isaac Asimov yarn; machine learning is a reality. And many organizations around the world are already taking full advantage of their machines to create new business models, reduce risk, dramatically improve efficiency and drive new competitive advantages. The big question is why insurers have been so slow to start collaborating with the machines.

Smart machines

Essentially, machine learning refers to a set of algorithms that use historical data to predict outcomes. Most of us use machine learning processes every day. Spam filters, for example, use historical data to decide whether emails should be delivered or quarantined. Banks use machine learning algorithms to monitor for fraud or irregular activity on credit cards. Netflix uses machine learning to serve recommendations to users based on their viewing history and recommendations.

In fact, organizations and academics have been working away at defining, designing and improving machine learning models and approaches for decades. The concept was originally floated back in the 1950s, but – with no access to digitized historical data and few commercial applications immediately evident – much of the development of machine learning was largely left to academics and technology geeks. For decades, few business leaders gave the idea much thought.

Machine learning brings with it a whole new vocabulary. Terms such as “feature engineering,” “dimensionality reduction,” “supervised and unsupervised learning,” to name a few. As with all new movements, an organization must be able to bridge the two worlds of data science and business to generate value.

Driven by data

Much has changed. Today, machine learning has become a hot topic in many business sectors, fueled, in large part, by the increasing availability of data and low-cost, scalable, cloud computing. For the past decade or so, businesses and organizations have been feverishly digitizing their data and records – building mountains of historical data on customers, transactions, products and channels. And now they are setting their minds toward putting it to good use.

The emergence of big data has also done much to propel machine learning up the business agenda. Indeed, the availability of masses of unstructured data – everything from weather readings through to social media posts – has not only provided new data for organizations to comb through, it has also allowed businesses to start asking different questions from different data sets to achieve differentiated insights.

The continuing drive for operational efficiency and improved cost management has also catalyzed renewed interest in machine learning. Organizations of all stripes are looking for opportunities to be more productive, more innovative and more efficient than their competitors. Many now wonder whether machine learning can do for information-intensive industries what automation did for manual-intensive ones.

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A new playing field

For the insurance sector, we see machine learning as a game-changer. The reality is that most insurance organizations today are focused on three main objectives: improving compliance, improving cost structures and improving competitiveness. It is not difficult to envision how machine learning will form (at least part of) the answer to all three.

Improving compliance: Today’s machine learning algorithms, techniques and technologies can be used on much more than just hard data like facts and figures. They can also be used to  analyze information in pictures, videos and voice conversations. Insurers could, for example, use machine learning algorithms to better monitor and understand interactions between customers and sales agents to improve their controls over the mis-selling of products.

Improving cost structures: With a significant portion of an insurer’s cost structure devoted to human resources, any shift toward automation should deliver significant cost savings. Our experience working with insurers suggests that – by using machines instead of humans – insurers could cut their claims processing time down from a number of months to a matter of minutes. What is more, machine learning is often more accurate than humans, meaning that insurers could also cut down the number of denials that result in appeals they may ultimately need to pay out.

Improving competitiveness: While reduced cost structures and improved efficiency can certainly lead to competitive advantage, there are many other ways that machine learning can give insurers the competitive edge. Many insurance customers, for example, may be willing to pay a premium for a product that guarantees frictionless claim payout without the hassle of having to make a call to the claims team. Others may find that they can enhance customer loyalty by simplifying re-enrollment processes and client on-boarding processes to just a handful of questions.

Overcoming cultural differences

It is surprising, therefore, that insurers are only now recognizing the value of machine learning. Insurance organizations are founded on data, and most have already digitized existing records. Insurance is also a resource-intensive business; legions of claims processors, adjustors and assessors are required to pore over the thousands – sometimes millions – of claims submitted in the course of a year. One would therefore expect the insurance sector to be leading the charge toward machine learning. But it is not.

One of the biggest reasons insurers have been slow to adopt machine learning clearly comes down to culture. Generally speaking, the insurance sector is not widely viewed as being “early adopters” of technologies and approaches, preferring instead to wait until technologies have become mature through adoption in other sectors. However, with everyone from governments through to bankers now using machine learning algorithms, this challenge is quickly falling away.

The risk-averse culture of most insurers also dampens the organization’s willingness to experiment and – if necessary – fail in its quest to uncover new approaches. The challenge is that machine learning is all about experimentation and learning from failure; sometimes organizations need to test dozens of algorithms before they find the most suitable one for their purposes. Until “controlled failure” is no longer seen as a career-limiting move, insurance organizations will shy away from testing new approaches.

Insurance organizations also suffer from a cultural challenge common in information-intensive sectors: data hoarding. Indeed, until recently, common wisdom within the business world suggested that those who held the information also held the power. Today, many organizations are starting to realize that it is actually those who share the information who have the most power. As a result, many organizations are now keenly focused on moving toward a “data-driven” culture that rewards information sharing and collaboration and discourages hoarding.

Starting small and growing up

The first thing insurers should realize is that this is not an arms race. The winners will probably not be the organizations with the most data, nor will they likely be the ones that spent the most money on technology. Rather, they will be the ones that took a measured and scientific approach to building their machine learning capabilities and capacities and – over time – found new ways to incorporate machine learning into ever-more aspects of their business.

Insurers may want to embrace the idea of starting small. Our experience and research suggest that – given the cultural and risk challenges facing the insurance sector – insurers will want to start by developing a “proof of concept” model that can safely be tested and adapted in a risk-free environment. Not only will this allow the organization time to improve and test its algorithms, it will also help the designers to better understand exactly what data is required to generate the desired outcome.

More importantly, perhaps, starting with pilots and “proof of concepts” will also provide management and staff with the time they need to get comfortable with the idea of sharing their work with machines. It will take executive-level support and sponsorship as well as keen focus on key change management requirements.

Take the next steps

Recognizing that machines excel at routine tasks and that algorithms learn over time, insurers will want to focus their early “proof of concept” efforts on those processes or assessments that are widely understood and add low value. The more decisions the machine makes and the more data it analyzes, the more prepared it will be to take on more complex tasks and decisions.

Only once the proof of concept has been thoroughly tested and potential applications are understood should business leaders start to think about developing the business case for industrialization (which, to succeed in the long term, must include appropriate frameworks for the governance, monitoring and management of the system).

While this may – on the surface – seem like just another IT implementation plan, the reality is that it machine learning should be championed not by IT but rather by the business itself. It is the business that must decide how and where machines will deliver the most value, and it is the business that owns the data and processes that machines will take over. Ultimately, the business must also be the one that champions machine learning.

All hail, machines!          

At KPMG, we have worked with a number of insurers to develop their “proof of concept” machine learning strategies over the past year, and we can say with absolute certainty that the Battle of Machines in the insurance sector has already started. The only other certainty is that those that remain on the sidelines will likely suffer the most as their competitors find new ways to harness machines to drive increasing levels of efficiency and value.

The bottom line is that the machines have arrived. Insurance executives should be welcoming them with open arms.