Tag Archives: autonomous cars

Driverless Cars and the ’90-90 Rule’

In programming circles, there is an aphorism known as the “90-90 rule.” It states that the first 90% of code accounts for the first 90% of the expected development time—and the remaining 10% of code takes another 90% of time. The rule is a tongue-in-cheek acknowledgement that technology projects always take longer than you expect, even when you know that they are going to take longer than you expect.

Sacha Arnoud, director of engineering at Waymo, recently used a variant of the 90-90 rule to characterize Waymo’s self-driving car program. Waymo’s experience, he said, was that the first 90% of the technology took only 10% of the time. To finish the last 10%, however, is requiring 10x the initial effort.

Arnoud’s remarks were given at a guest lecture at Lex Fridman’s MIT class on “Deep Learning for Self-Driving Cars.” He offered technical insights on the history of the Waymo program, how it is applying artificial intelligence and deep learning and how it is moving from demo to industrial-strength product.

The Waymo engineer’s lecture goes beyond most Waymo management presentations and press events. He provides vivid details on the complexity of the effort to date and insight on challenges to come—both for Waymo and for those trying to catch up to its pioneering efforts.

Here are 5 takeaways, though I recommend watching the entire presentation.

1. Industrialization requires 10x the effort.

Arnoud emphasized the large amount of work needed to go from a demo that works in a lab to an industrialized product that is safe to put on the road: “You need to 10x the capabilities of your technology. You need to 10x your team size, including finding effective ways for more engineers and more researchers to collaborate. You need to 10x the capabilities of your sensors. You need to 10x the overall quality of the system, including your testing practices.”

2. Deep learning enabled algorithmic breakthroughs.

Arnoud noted that deep learning techniques were much less advanced in 2010 when Google started its work on self-driving cars. But, in the years since, deep learning has advanced to enable algorithmic breakthroughs in several critical areas for autonomous driving, including mapping, perception and scene understanding.

Arnoud gave numerous examples, such as using deep learning to analyze street imagery to extract street names, house numbers, traffic lights and traffic signs. The ability to precompute such data and store them as maps in the car saves precious onboard computing power for real time tasks.

See also: When Will the Driverless Car Arrive?  

Deep learning is driving breakthroughs in real-time tasks as well, such as analyzing sensor data to identify traffic signals, other vehicles, obstacles, pedestrians, and so on. Deep learning capabilities also help in anticipating possible behavior of other drivers, cyclists and pedestrians, and driving accordingly.

3. Synergy with other Google units is key to Waymo’s progress.

Arnoud acknowledged the importance of Google’s “whole machine learning ecosystem” to Waymo’s progress. This includes the seminal software advances by the Google Brain team and on-going collaboration with other Google teams working on deep learning at scale, such as in vision, speech, natural language processing and maps. The Google ecosystem also provides specialized infrastructure and tools for machine learning. This includes accelerators, data centers, labelled datasets and research that support Google’s TensorFlow programming paradigm.

4. Waymo’s testing program might be its secret sauce.

Arnoud emphasized that however great Waymo’s algorithms, sensors and overall package might be, driverless cars are still complex, embedded, real-time robotic systems that must work safely with imperfect data in an unpredictable world. He highlighted Waymo’s three-prong testing program of real-world driving, simulation and structured testing as key to iterating on and productizing the technology.

Much is made of the millions public-road miles that Waymo’s cars have driven autonomously. Arnoud described this as the equivalent of about 300 years of human driving experience and 160 times around the globe. Real world driving is critical, he said, but what is more important is the ability to simulate.

Simulation is critical because it allows for Waymo to test each new iteration of software against all previously-driven miles. Even more important is the ability to test against “fuzzed” versions of those millions of miles, such as seeing how the software would handle cars going at slightly different speeds, an extra car, pedestrians crossing in front of the car and so on. Arnoud described Waymo’s simulation-based testing capability as the equivalent of 25,000 virtual cars driving 2.5 billion real and modified miles in 2017.

The third component of Waymo’s testing program is its structured testing program. Arnoud said that there is a “long tail” of driving situations that happen very rarely. Rather than trying to encounter every possibility in real-world driving, Waymo set up a 90-acre mock city at the decommissioned Castle Air Force base where it can test its cars against such edge cases. These tests are then fed into the simulation engine and fuzzed to create variations for more testing.

5. Waymo’s next steps are big (and hard) ones.

Arnoud closed with a discussion of the engineering challenges in front of Waymo. He described two big next steps.

One next step is expanding the “operational design domains” (ODD) of the cars. This includes expanding into “dense urban cores,” such as San Francisco (in which Waymo recently announced it is expanding its testing program). The other ODD was additional weather conditions, such as hard rain, snow and fog. (Waymo CEO John Krafcik recently told an audience that he was “jumping up and down” recently when it snowed 12 inches near Detroit, because it would enable Waymo’s testing in snow.)

See also: 7 Steps for Inventing the Future

The other area of focus was what Arnoud called “semantic understanding.” As an example, he pointed to the chaotic Place de l’Étoile traffic circle around the Arc de Triomphe in Paris. The circle is a meeting point of 12 roads and notoriously difficult to navigate. Arnoud says he has driven it many times without incident, however, and that such situations require a lot more than perception and vehicle operating skills. They require deep understanding of local rules and expectations. They also require constant communication and coordination with other drivers, including signals, gestures and so on. This kind of deep reasoning is key to numerous edge cases and improving the general abilities of driverless cars.

* * *

While Waymo has clearly made tremendous progress towards the driverless future, Arnoud closed his presentation by emphasizing the engineering infrastructure and the complexities of scaling that have to be addressed in order to turn driverless cars into safe production systems.

How far along is Waymo in the last 90% of that industrialization process? Arnoud never said. But, to put a point on the complexities, he showed a closing video of a Waymo car stopped at an intersection as a gaggle of kids bounced on frogger sticks across the street on all sides of the car. Some things are waiting for, he seemed to imply.

Driverless Vehicles: Brace for Impact

On June 26, Waymo (Google’s autonomous car firm), signed a deal under which Avis Budget Group will provide “fleet support and maintenance services” to Phoenix-area Waymo vehicles. Waymo uses Chrysler Pacifica minivans to autonomously shuttle Phoenix residents around town. Its first fleet of 100 minivans quickly grew into an order for 500 more.

The Waymo/Avis agreement may only be a pilot, but the implications are enormous. Not unlike standard cab companies, Waymo realized that a fleet of autonomous vehicles would need cleaning and maintenance throughout the day and storage throughout the night. When practical matters like auto cleaning and storage become news enough for a press release, something big is going on.

Here are some fun facts:

  • According to USA Today, Avis’ stock rose 14% on the news.
  • The Chrysler Pacifica was chosen, in large part, because it could close its own doors. Waymo usage experts theorized that riders might often hop out and forget to close the door.
  • Within hours of the Waymo announcement, Apple likewise unveiled a deal where Hertz Global would manage its autonomous fleet.

Autonomous vehicles have picked up the pace of disruption over the last two years. What will life be like when the Autonomy of Things takes on many of our everyday behaviors or occupations, like driving? Will we be safer? Will we need insurance? Will auto manufacturers cover accidents via product liability? Who will cover bodily injury or property damage? How will risk products be changed to fit this new model? Is there an insurance right-road to surviving autonomy?

See also: The Evolution in Self-Driving Vehicles  

Is Autonomy Impact Still Underrated?

There has been a lot of talk and certainly a wealth of words written on the impact of auto autonomy, and safety is at the top of the concerns and promises of autonomous vehicles. Insurers are, of course, focused on how autonomous vehicles might cause a decline in the need for auto insurance.

The pace of development, rollout, experimentation and expansion of autonomous vehicles has far exceeded original expectations. In his blog, Peter Diamandis (XPrize Founder) noted that a former Tesla and BMW executive said that self-driving cars would start to kill car ownership in just five years. John Zimmer, the cofounder and president of Lyft, said that car ownership would “all but end” in cities by 2025.

The Wall Street Journal reported in July 2016 that auto insurance represents nearly a third of all premiums for the P&C industry, with projections that 80% could evaporate over the next few decades as autonomous vehicles are introduced, some of them replacing legacy vehicles and some created for shared transportation. At the same time, U.S. government support strengthened in September 2016 when federal auto safety regulators released their first set of guidelines, sending a clear signal to automakers that the door was wide open for driverless cars and betting that the nation’s highways will be safer with more cars driven by machines instead of people.

Those statements, among others, might cause some scrambling. Manufacturers are working frantically to partner with AI providers, cab services and ridesharing services such as Uber, Lyft and Waymo. Naysayers will note that rural areas will be highly unlikely to use autonomous vehicles soon, and it’s true that the largest impact may be in urban areas. But if car ownership were even cut by 5% by 2030, a tremendous number of auto manufacturers and auto insurers would be affected.

Autonomy and its insurance impact isn’t limited to personal autos. Truck company Otto is testing self-driving commercial trucks — a necessary automation that could help alleviate the growing lack of truck drivers. Husqvarna has several models of autonomous lawn mowers on the market. Yara and Rolls Royce are among companies working on autonomous ships. Case, John Deere and Autonomous Tractor Corporation have all been developing driverless tractors.

In nearly every one of these cases, there are safety benefits and disruptive insurance implications, but there are also revenue growth opportunities for those that think more broadly and “outside the box.” From developing partnerships with automotive companies to leveraging the autonomous vehicle data for new services, each offers alternative revenue streams to counter the decline of traditional auto insurance. The key is experimenting with these technologies to find alternative “products and services” and develop an ecosystem of partners to support this, before the competition does.

Share and Transportation as a Service — Insurers May Like

In our report, A New Age of Insurance:  Growth Opportunity for Commercial and Specialty Insurance in a Time of Market Disruption, we cite a report from RethinkX, The Disruption of Transportation and the Collapse of the Internal-Combustion Vehicle and Oil Industries, which says that by 2030 (within 10 years of regulatory approval of autonomous vehicles), 95% of U.S. passenger miles traveled will be served by on-demand autonomous electric vehicles owned by fleets, not individuals, in a new business model called “transport-as-a-service” (TaaS). The report says the approval of autonomous vehicles will unleash a highly competitive market-share grab among existing and new pre-TaaS (ride-hailing) companies in expectation of the outsized rewards of trillions of dollars of market opportunities and network effects.

Welcome to the adolescence of the sharing economy and transportation as a service. Autonomy isn’t the only road for vehicle progress. Vehicle sharing is growing and will remain in vogue for some time. Just as Airbnb and HomeAway have given rise to new insurance products, Zipcar and Getaround and Uber have given rise to new P&C products.

At the same time, a merging of public and private transportation and a pathway to free transportation is in the early stages of being created in the TaaS model. This will shift risk from individuals to commercial entities, governments or other businesses that provide the public transportation, creating commercial lines product opportunities beyond traditional “public transportation.”

Vehicle users, whether they are riders, borrowers, sharers or public entities, are going to need innovative coverage options. Tesla and Volvo may be promising some level of auto coverage for owners of autonomous vehicles, but that kind of blanket coverage is likely to mimic an airline’s coverage of passengers and cargo — it will be limited. Those who lend their vehicle, through a software-based consolidator, such as Getaround, will need coverage that goes beyond their auto policy.

In the past few weeks, we’ve also seen how cyber attacks can undermine freight and shipping, not to mention systems. Nearly all of these service-oriented options will require new types of service-level coverage. Autonomous freight may be safer in transit, but in some ways it may also be less secure.

The lessons appear to be found in brainstorming. Technology is breeding diversity in service use and ownership. There will be new coverage types and new insurance products needed.

See also: Will You Own a Self-Driving Vehicle?  

Up Next … Flying Vehicles

Remember the movie “Back to the Future” and the Jetsons flying cars that were so cool? Well, they are quickly becoming a cool reality. A June 2017 Forbes article says flying cars are moving rapidly from fiction to reality, with the first applications of flying vehicles for recreational activities in the next five years. The article says that, in the past five years, at least eight companies have conducted their first flight tests, and several more are expected to follow suit, indicative of the frenzied activity in this space.

Companies such as PAL-VTerrafugia, AeromobilEhangE-VoloUrban AeronauticsKitty Hawk and Lilium Aviation completed test flights of their flying car prototypes, with PAL-V going further by initiating pre-sales of its Liberty Pioneer model flying car, which the company aims to deliver by the end 2018. This sounds like Tesla and its pre-sales move!

Not to be left behind … ride-sharing companies are aggressively entering the space. Uber launched the Uber Elevate program, with a focus on making flying vehicles transport a reality by bringing together government agencies, vehicle manufacturers and regulators. Google and Skype are entering the space by investing in start-ups: Google in Kitty Hawk and Skype in Lilium Aviation. Not to be left behind, Airbus has unveiled a number of flying car concepts, with plans to launch a personal flying car by 2018. Airbus also plans to build a mass transit flying vehicle…the potential next TaaS option.

So, it pays for insurers to keep their attention on autonomous vehicle trends … because it is more than the personal autonomous vehicle … it is the transformation of the entire transportation industry and will have a significant impact on premium and growth for auto insurers. As we recently found in our commercial and specialty insurance report, the transportation industry is rapidly changing and new technologies may be lending themselves to safety, but the world itself isn’t necessarily growing any safer.

Risk doesn’t end. Insurers will always be helping individuals and companies manage risk. The key will be using the trends to rapidly adapt to a shift to the new digital age. Insurers will need to understand and value new risks and offer innovative products and services that meet the changing needs in this shift during the digital age.

New Era of Commercial Insurance

Despite a generally soft market for traditional P&C products, the fact that so many industries and the businesses within them are being reshaped by technology is creating opportunities (and more challenges). Consider insurers with personal and commercial auto. Pundits are predicting a rapid decline in personal auto premiums and questioning the viability of both personal and commercial auto due to the emergence of autonomous technologies and driverless vehicles, as well as the increasing use of alternative options (ride-sharing, public transportation, etc.).

Finding alternative growth strategies is “top of mind” for CEOs.  Opportunities can be captured from the change within commercial and specialty insurance. New risks, new markets, new customers and the demand for new products and services may fill the gaps for those who are prepared.

Our new research, A New Age of Insurance: Growth Opportunities for Commercial and Specialty Insurance at a Time of Market Disruption, highlights how changing trends in demographics, customer behaviors, technology, data and market boundaries are creating a dramatic shift from traditional commercial and specialty products to the new, post-digital age products redefining the market of the future.

See also: Insurtechs Are Pushing for Transparency

Growth Opportunities

New technologies, demographics, behaviors and more will fuel the growth of new businesses and industries over the next 10 years. Commercial and specialty insurance provides a critical role to these businesses and the economy — protecting them from failure by assuming the risks inherent in their transformation.

Industry statistics for the “traditional” commercial marketplace don’t yet reflect the potential growth from these new markets. The Insurance Information Institute expects overall personal and commercial exposures to increase between 4% and 4.5% in 2017 but cautioned that continued soft rates in commercial lines could cause overall P&C premium growth to lag behind economic growth.

But a diverse group of customers will increasingly create narrow segments that will demand niche, personalized products and services. Many do not fit neatly within pre-defined categories of risk and products for insur­ance, creating opportunities for new products and services.

Small and medium businesses are at the forefront of this change and at the center of business creation, business transformation and growth in the economy.

  • By 2020, more than 60% of small businesses in the U.S. will be owned by millennials and Gen Xers — two groups that prefer to do as much as possible digitally. Furthermore, their views, behaviors and expectations are different than those of previous generations and will be influenced by their personal digital experiences.
  • The sharing/gig/on-demand economy is an example of the significant digitally enabled changes in people’s behaviors and expectations that are redefining the nature of work, business models and risk profiles.
  • The rapid emergence of technologies and the explosion of data are combining to create a magnified impact. Technology and data are making it easier and more profitable to reach, underwrite and service commercial and specialty market segments. In particular, insurers can narrow and specialize various segments into new niches. In addition, the combination of technology and data is disrupting other industries, changing existing business models and creating businesses and risks that need new types of insurance.
  • New products can be deployed on demand, and industry boundaries are blurring. Traditional insurance or new forms of insurance may be embedded in the purchase of products and services.

Insurtech is re-shaping this new digital world and disrupting the traditional insurance value chain for commercial and specialty insurance, leading to specialty protection for a new era of business. Consider insurtech startups like Embroker, Next Insurance, Ask Kodiak, CoverWallet, Splice and others. Not being left behind, traditional insurers are creating innovative business models for commercial and specialty insurance, like Berkshire Hathaway with biBERK for direct to small business owners; Hiscox, which offers small business insurance (SBI) products directly from its website; or American Family, which invested in AssureStart, now part of Homesite, a direct writer of SBI.

The Domino Effect

We all likely played with dominoes in our childhood, setting them up in a row and seeing how we could orchestrate a chain reaction. Now, as adults, we are seeing and playing with dominoes at a much higher level. Every business has been or likely will be affected by a domino effect.

What is different in today’s business era, as opposed to even a decade ago, is that disruption in one industry has a much broader ripple effect that disrupts the risk landscape of multiple other industries and creates additional risks. We are compelled to watch the chains created from inside and outside of insurance. Recognizing that this domino effect occurs is critical to developing appropriate new product plans that align to these shifts.

Just consider the following disrupted industries and then think about the disrupters and their casualties: taxis and ridesharing (Lyft, Uber), movie rentals (Blockbuster) and streaming video (NetFlix), traditional retail (Sears and Macy’s) and online retail, enterprise systems (Siebel, Oracle) and cloud platforms (Salesforce and Workday), and book stores (Borders) and Amazon. Consider the continuing impact of Amazon, with the announcement about acquiring Whole Foods and the significant drop in stock prices for traditional grocers. Many analysts noted that this is a game changer with massive innovative opportunities.

The transportation industry is at the front end of a massive domino-toppling event. A report from RethinkX, The Disruption of Transportation and the Collapse of the Internal-Combustion Vehicle and Oil Industries, says that by 2030 (within 10 years of regulatory approval of autonomous vehicles (AVs)), 95% of U.S. passenger miles traveled will be served by on-demand autonomous electric vehicles owned by fleets, not individuals, in a new business model called “transportation-as-a-service” (TaaS). The TaaS disruption will have enormous implications across the automotive industry, but also many other industries, including public transportation, oil, auto repair shops and gas stations. The result is that not just one industry could be disrupted … many could be affected by just one domino … autonomous vehicles. Auto insurance is in this chain of disruption.

See also: Leveraging AI in Commercial Insurance  

And commercial insurance, because it is used by all businesses to provide risk protection, is also in the chain of all those businesses affected – a decline in number of businesses, decline in risk products needed and decline in revenue. The domino effect will decimate traditional business, product and revenue models, while creating growth opportunities for those bold enough to begin preparing for it today with different risk products.

Transformation + Creativity = Opportunity

Opportunity in insurance starts with transformation. New technologies will be enablers on the path to innovative ideas. As the new age of insurance unfolds, insurers must recommit to their business transformation journey and avoid falling into an operational trap or resorting to traditional thinking. In this changing insurance market, new competitors don’t play by the rules of the past. Insurers need to be a part of rewriting the rules for the future, because there is less risk when you write the new rules. One of those rules is diversification. Diversification is about building new products, exploring new markets and taking new risks. The cost of ignoring this can be brutal. Insurers that can see the change and opportunity for commercial and specialty lines will set themselves apart from those that do not.

For a greater in-depth look at the implications of commercial insurance shifts, be sure to downloadA New Age of Insurance: Growth Opportunities for Commercial and Specialty Insurance at a Time of Market Disruption.

A New World Full of Opportunity

The primary drivers of disruption in insurance – notably, fintech (and more specifically, insurtech) – are coming from outside the industry. However, while the pace of change and market disruption has been daunting for most incumbents, the growing presence of insurtech companies is not a threat, but rather is creating real opportunities for the industry.

We see a combination of market and organizational priorities that open the door for these new opportunities.

  • External opportunities primarily relate to social and technological trends and pertain to the shift in customer needs and expectations (which digital technology has facilitated). Insurers have been taking action in these areas to stay relevant in the market and at least maintain their market position. For many companies, focusing on these opportunities remains critical, but this is not enough for them to gain a truly competitive advantage.
  • Internal opportunities relate to using technology to enhance operations and business function execution. For example, some insurers have used artificial intelligence (AI) technology to enhance internal operations, which has improved efficiencies and automated existing customer-facing, underwriting and claims processes.

To take full advantage of these opportunities, insurers need to determine their innovation needs and make meaningful connections with innovators. Doing so will help them balance their innovation mix – in other words, where they can make incremental innovations (the ones that keep them in the game) and where they can strive for real breakthroughs with disruptive and radical innovation (the ones that position them as market leaders).

An effective enterprise innovation model (EIM) will take into account the different ways to meet an organization’s various needs and help it make innovative breakthroughs. The model or combination of models that is most suitable for an organization will depend on its innovation appetite, the type of partnerships it desires and the capabilities it needs. EIMs feature three primary approaches to support corporate strategy:

  • Partner – Innovation centers (also known as hubs or labs) are the most common of the three EIM approaches. Their main purpose is to connect insurers to the InsurTech ecosystem and create new channels for bringing an outside-in view of innovation to the business units.
  • Build – Incubators are a common and effective way to build innovative capabilities and accelerate change. They can be internal, but most companies have preferred to establish them externally and then bring their ideas back into the company.
  • Buy – In this case, an insurer typically will establish a strategic corporate ventures division that sources ideas from outside the company. The company provides funding and support for equity, while the venture explores, identifies and evaluates solutions, and participates in new ventures.

Companies can select elements from each of the above models based on their need for external innovation, the availability of talent, their ability to execute and the amount of investment the organization is willing to commit.

See also: Innovation — or Just Innovative Thinking?  

Insurance leaders’ innovation agenda should include:

  • Scenario planning – What are potential future scenarios and their implications?
  • Real-time monitoring and analysis of the insurtech landscape – What’s out there that can help us now, and what do we want that may not exist yet?
  • Determining how to promote enterprise innovation, including which combination of approaches will most effectively accelerate and enable execution – What’s the best approach for us to stimulate and take advantage of innovation?
  • Augmenting the organization with new and different types of talent – Where are the innovators we need, and how can we best attract and employ them?
  • Cyber security and regulation – Are we prepared for the operational challenges that new technology can present, and have we and our real and potential partners considered the compliance ramifications of what we’re doing or considering?

Typical Exploration Topics

Some of the typical exploration topics across lines of business are:

  • Personal lines: usage-based insurance, shared and on-demand economies, peer-to-peer, direct-to-consumer, ADAS & autonomous cars.
  • Commercial lines: direct to small business, drones and satellite imagery, internet of things, alternative risk transfer, emerging risks.
  • Life and retirement: robo-advice, personalized insurance, medical advances, automated underwriting, decreasing morbidity and mortality risk.

Opportunities for insurers

As part of PwC’s Future of Insurance initiative, we have interviewed many industry executives and identified six key insurtech business opportunities. We see a combination of market and organizational priorities, which open the door for both external and internal opportunities.

External opportunities primarily relate to social and technological trends and pertain to the shift in customer needs and expectations (which digital technology has facilitated). Insurers have been taking action in these areas to stay relevant in the market and at least maintain their market position. For many companies, focusing on these opportunities remains critical, but this is not enough for them to gain a truly competitive advantage.

External opportunities:

  • Are mainly driven by customer expectations and needs and enabled by technology.
  • Offer front runners the opportunity to gain market relevance and position themselves.
  • Also offer fast followers opportunity because value propositions can be quickly replicated.

Internal opportunities relate to using technology to enhance operations and business function execution. For example, some insurers have used artificial intelligence (AI) technology to enhance internal operations, which has improved efficiencies and automated existing customer-facing, underwriting and claims processes.

Internal opportunities are:

  • Mainly driven by technological advancements.
  • A source of competitive advantage but demand deeper change.
  • An opportunity to set the foundation for how the company understands and manages risk.

To take full advantage of these opportunities, insurers need to determine their innovation needs and make meaningful connections with innovators. Doing so will help them balance their innovation mix – in other words, where they can make incremental innovations (the ones that keep them in the game) and where they can strive for real breakthroughs with disruptive and radical innovation (the ones that position them as market leaders).

See also: 10 Predictions for Insurtech in 2017  

Some examples of change are:

  • Incremental: Omni-channel integration, leveraging mobile and social media solutions and experiences to follow existing trends in customer and partner interaction;
  • Disruptive: Usage-based and personalized insurance that leverages technology and data to develop new risk models based on behavioral factors. This also has the potential to drive radical change.
  • Radical: Crop insurance, where data from different sources (such as weather and soil sensors) is leveraged to optimize and predict yield. As a result of this deterministic model, claims are paid up-front at harvest time.

There is no single perfect innovation mix. It depends on a company’s strategic goals and willingness to invest. Insurers should take into account current insurtech trends and determine long-term potential market scenarios based in part on current indicators and emerging trends. A short-term view will not foster the change that leads to breakthrough innovation.

As a starting point, the following questions can help you evaluate how prepared your organization is to drive innovation.

  • Corporate structure
    • Which parts of your organization drive innovation? Does the push for innovation occur at the corporate or business unit level (or both)?
    • How is the board engaged on decisions about the organization’s innovation mix?
    • What is the organization doing to make innovation a part of its culture?
    • What are the main challenges your organization faces when driving innovation?

  • Strategy, ideation and design
    • How does your organization become familiar with new trends and their implications?
    • To what extent has your organization used an “outside-in” view to inform your innovation model?
    • Which potential future scenarios have you identified and shared across the organization?
    • To what extent have you aligned your innovation portfolio strategy with potential future scenarios?
    • How does your organization approach ideation through execution?
    • Which capabilities are you leveraging to enable and accelerate the execution of new ideas?
  • External participation
    • What investments has your organization made in innovation?
    • In which areas is your organization participating (e.g., autonomous cars, connected economies, shared economies)
    • What structures (potentially in specific locations) has your organization created to support external participation?
    • To what extent has your organization managed to attract talent and partners?

Fast prototyping is key to quickly creating minimally viable products/solutions (MVP) and bringing ideas to life. Early stage start-ups develop and deploy full functioning prototypes in near-real time and go-to-market with solutions that are designed to evolve with market feedback. In this scenario, the development cycle is shortened, which allows startups to quickly deliver solutions and tailor future releases based on usage trends and feedback and to accommodate more diverse needs.

Incumbents can follow the same approach and align appropriate capabilities and resources to develop their own prototypes. They also can partner with existing startups that have a minimally viable product (MVP) to help them to move to the next stage, scaling. For this, they have to take into consideration several factors, including operational capacity, cyber risk and regulation (among others) to deploy the MVP in an “open” market. As opposed to controlled pilots or proofs of concept that are controlled environments, this “open” market is driven by demand. Lack of proper resources and the inability to scale the startup will severely compromise or actually prevent successful innovation.

See also: 7 Predictions for IoT Impact on Insurance  

The ways to accomplish all of this vary based on how the organization plans to source new opportunities and ideas, how it plans on executing innovation and how it plans to deploy new products and services. The following graphic provides examples of enterprise innovation operating models by primary function.

Final thoughts

In a fast-paced digital age, insurers are balancing insurtech opportunities with the challenge of altering long-standing business processes. While most insurers have embraced change to support incremental innovation, bigger breakthroughs are necessary to compete with the new technologies and business models that are disrupting the industry.

This article was written by Stephen O’Hearn, Jamie Yoder and Javier Baixas.

Novel Solution for Driverless Risk

The route to a fully autonomous vehicle market seems long and fitful in the eyes of many. But it is likely to become a reality faster than many are prepared to accept. Like IBM, Kodak and many other companies once confronted with a rapidly changing market, we, too, now face disruptions in the auto market, perhaps unlike any since the invention of the auto. As liability increasingly shifts from the human driver to systems and software – a trend highlighted by recent reports of the first autonomous fatality – original equipment manufacturers (OEM) will come to the forefront as primary holders of automobile-related insurance risk. How they manage this risk will help determine the success and acceptance of the autonomous vehicle market in the years to come.

A new age

Skeptics of an early adoption of fully autonomous vehicles have a point. In their short history, autonomous vehicles have faced a wide array of challenges including skittish maneuvering ability in wet weather, gaps in infrastructure, regulatory and legal shortcomings, market acceptance, risk of hacking, consumers’ privacy and ethical choices. The list goes on, but so do advances in technology.

There are dozens of advances such as braking assistance, blind spot detection, pre-collision warning systems, electronic stability control and vehicle-to-vehicle communication that have been adopted over the years or are now making their way into the latest models. These technologies have been largely accepted and often embraced by consumers who have come to view them as something more than just a convenience.

See also: Connected Vehicles Can Improve Claims  

In fact, few dispute the potential safety advantages of fully self-driving cars. Active safety systems that eliminate the human element from the driving equation have already been shown to prevent accidents. According to the Insurance Institute for Highway Safety (IIHS), automatic braking can reduce rear-end crashes by 40%, and front collision warning systems can lower rear-end accidents by 23%.1 But this is just the tip of the iceberg: 94% of auto accidents are caused by human errors such as speeding, driving under the influence and driver inattention, according to a 2015 survey by the National Highway Transportation Safety Administration.2

The U.S. market is expected to see several thousand autonomous vehicles sold in 2020, which will grow to nearly 4.5 million vehicles sold in 2035, according to IHS Automotive forecasts, an industry research firm.3 The slow methodical 11-year turnover in U.S. car ownership is likely to fall by the wayside as convenience or safety features entice consumers to purchase a self-driving car sooner than they would otherwise do. These early purchasers could be setting up a cycle of more rapid adoption as car buyers decide to forgo the thrill or pleasure of driving for the safety of their families and the ability to be more productive (or just catch up on sleep and social media). Further, there may be no need for car ownership at all in a new shared economy including on-demand autonomous shuttles.

Shifting responsibilities

Assessing liability in the near future will admittedly be a tricky matter as a mix of driving modes, ranging from no autonomy to full autonomy, populate the roadways. Accidents that involve human driver to human driver will morph into dozens of combinations of human drivers with various levels of semi-autonomous drivers and eventually fully autonomous cars. Questions of liability will need to sort out not only the comparative negligence of a human operator’s actions but also the capability of software and sensors. As the ever-diminishing role of human drivers gives way to the rise of autonomous vehicles, the importance of personal auto insurance will likewise be replaced by product liability.

Google, Mercedes and Volvo have already said they will accept responsibility for accidents that are caused by malfunctions in the technology in their cars, a move welcomed by federal regulators that see the commitment as a way to smooth the introduction of vehicles with these new technologies. While these carmakers’ pledges may, in fact, be redundant, they are a harbinger of the shift in demand for product liability.

But carmakers’ step up in accountability is only one link in the manufacture of autonomous vehicles, which can involve dozens of suppliers for software, systems and devices which enable the positioning data and predictive response algorithms to be accurate and effective. Enhanced sensing and response time capabilities will drive new demands on hardware and software performance. How will liability be spread among potentially dozens of interlocking but legally separate entities?

See also: Plunging Costs for Autonomous Vehicles  

Currently, as part of the general purchasing conditions, the supplier will indemnify and hold the manufacturer harmless from and against any and all loss, liability, cost and expense arising out of a claim that a defect in the design or manufacture of the product caused personal injury or damage to property. However, suppliers are not always completely responsible for the design or validation of the components they provide, but rather can be directed by the carmaker to either model or test the component according to the carmaker’s predetermined specifications. Thus, the parties may have a shared financial burden of failure and need to negotiate the consequences at project inception. The process of assigning responsibility and managing indemnification often involves a team of resources that do not contribute to the carmakers’ underlying business function of making people mobile.

This relationship is likely to evolve as the importance of the car’s electronic control unit (ECU) grows ever more critical as the brain center for programming features that ultimately determine how the car responds. Even now, validating software code – a function paramount in detecting errors – is less defined as compared with hardware. How the validation process will evolve under all possible control scenarios is extremely difficult to imagine. But one change in the process is becoming clear: As the software algorithms become more integral to the success and failure of autonomous vehicles, carmakers have started to keep a tight rein on the integration of software and hardware. As willing as carmakers may be to absolve consumers of the responsibility for accidents that stem from the fault of their technology, they are unlikely to extend a similar courtesy to their suppliers. And why should they if the cause of the accident can be traced to a supplier’s defective sensor or software?

Nevertheless, untangling the web of responsibility can be a distraction from the business focus and could become an impediment to progress. What is a relatively well-established practice in other fields for passing the liability down the supply chain to the source of the failure is likely to become much more complicated and nuanced in the realm of autonomous vehicles as cars become increasingly dependent on an integration of sophisticated technologies.

Likewise, the ways in which risk is shared under product liability are likely to be increasingly difficult to manage. In an autonomous world, the insurance program would ideally be structured such that suppliers not only have skin in the game but also have a more transparent line of sight to the cost they are contributing to the potential liability. The question the industry needs to ask is: Is there a better way to share the cost of risk among the carmaker and its suppliers reflecting the shifted responsibility?

Enter a SPLASh pool

One option is to create an insurance pool for each autonomous carmaker. Under a Supplier Product Liability Autonomous Share (SPLASh) pool, the carmaker would assume all the product liability risk for accidents stemming from the autonomous technology and cede the risk to the SPLASh pool. To be viable, all suppliers – or “swimmers” – along with the carmaker would need to participate in the pool, which would operate as a funding vehicle for the risk. Each year, the pool would be funded commensurate with the expected losses, and losses would be paid directly from the fund, eliminating the manufacturer’s role of managing indemnification from the suppliers.

Like more traditional risk pools used by a range of organizations from public entities that share their law enforcement exposure to a group of hospital systems that manage their professional liability risk, a SPLASh pool would also have a management function, presumably overseen by the manufacturer, as well as various insurance-type functions from actuaries, to calculate the premium and reserves; claims handlers (internal or outsourced) to pay and manage claims; and lawyers to interpret coverage, among others. In this way, autonomous technology may be paving a new road but with the experience and insight of well-traveled insurance professionals who understand the different approaches to managing risk.

Funding would reflect the supplier’s risk profile with low risk suppliers like those that provide cameras for parallel parking – the minnows of the pool – paying less than high risk “whale” suppliers such as a software developer. The pool can be structured according to frequency and severity of risk. Such an arrangement could consist of all pool members participating in a structure where more frequent, low-severity claims are grouped (Fund A) separately from less frequent, high-severity claims (Fund B), both meeting risk transfer.

Each fund would have per occurrence loss limits and require member contributions based on actuarial projections, perhaps at first based on fault rates from engineering systems output, until credible loss data develop. Various features such as aggregate limits, loss ratio caps, overflow between funds and member assessments can be used to tailor the insurance coverage with a clear desired outcome – to motivate innovators to develop quality products.

See also: Here Comes Robotic Process Automation  

The arrangement builds in a high level of transparency as suppliers with bad loss performance would be required to contribute more to Fund B than others. Moreover, consistently poor swimmers could be replaced by suppliers with better performance.

This concept blends well with the current warranty programs offered by car manufacturers. Like those programs offered today, dealers provide details of new and used warranty programs available to the consumer, covering defects in material or workmanship for 48 months or 50,000 miles, whichever comes first, for example. The carmaker would budget a certain amount of costs toward warranty replacement and then track the records and claims to more accurately predict future replacement costs as well as pinpoint components that are failing, assuming that the problem can be isolated. If costs are higher than expected (outside of the normal failure rate), the manufacturer can push further costs to the supplier at the source or remove them from the assembly line altogether.

Buckle up

A SPLASh pool can pave the way to managing carmakers’ risk in the future. The product liability exposure from autonomous vehicles shouldn’t be a roadblock to the increased safety and mobility that self-driving cars can bring to millions of people. The insurance industry will need to demonstrate its creativity and foresight in managing risk to keep innovation on the right track.