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15 Hurdles to Scaling for Driverless Cars

Will the future of driverless cars rhyme with the history of the Segway? The Segway personal transporter was also predicted to revolutionize transportation. Steve Jobs gushed that cities would be redesigned around the device. John Doerr said it would be bigger than the internet. The Segway worked technically but never lived up to its backers’ outsized hopes for market impact. Instead, the Segway was relegated to narrow market niches, like ferrying security guards, warehouse workers and sightseeing tours.

One could well imagine such a fate for driverless cars (a.k.a. AVs, for autonomous vehicles). The technology could work brilliantly and yet get relegated to narrow market niches, like predefined shuttle routes and slow-moving delivery drones.  Some narrow applications, like interstate highway portions of long-haul trucking, could be extremely valuable but nowhere near the atmospheric potential imagined by many—include me, as I described, for example, in “Google’s Driverless Car Is Worth Trillions.”

For AVs to revolutionize transportation, they must reach a high level of industrialization and adoption. They must enable, as a first step, robust, relatively inexpensive Uber-like services in urban and suburban areas. (The industry is coalescing around calling these types of services “transportation as a service,” or TaaS.) In the longer term, AVs must be robust enough to allow for personal ownership and challenge the pervasiveness of personally owned, human-driven cars.

See also: Where Are Driverless Cars Taking Industry?  

This disruptive potential (and therefore enormous value) is motivating hundreds of companies around the world, including some of the biggest and wealthiest, such as Alphabet, Apple, General Motors, Ford, Toyota and SoftBank, to invest many billions of dollars into developing AVs. The work is progressing, with some companies (and regulators) believing that their AVs are “good enough” for pilot testing of commercial AV TaaS services with real customers on public roads in multiple markets, including SingaporePhoenix and Quangzhou.

Will AVs turn out to be revolutionary? What factors might cause them to go the way of the Segway—and derail the hopes (and enormous investments) of those chasing after the bigger prize?

Getting AVs to work well enough is, of course, a non-negotiable prerequisite for future success. It is absolutely necessary but far from sufficient.

In this three-part series, I look beyond the questions of technical feasibility to explore other significant hurdles to the industrialization of AVs. These hurdles fall into four categories: scaling, trust, market viability and secondary effects.

Scaling. Building and proving an AV is a big first step. Scaling it into a fleet-based TaaS business operation is an even bigger step. Here are seven giant hurdles to industrialization related to scaling:

  1. Mass production
  2. Electric charging infrastructure
  3. Mapping
  4. Fleet management and operations
  5. Customer service and experience
  6. Security
  7. Rapid localization

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

  1. Independent verification and validation
  2. Standardization and regulation
  3. Public acceptance

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.

  1. Business viability
  2. Stakeholder resistance
  3. Private ownership

See also: Suddenly, Driverless Cars Hit Bumps  

Secondary Effects. We shape our AVs, and afterward our AVs reshape us, to paraphrase Winston Churchill. There will be much to love about the successful industrialization of driverless cars. But, as always is the case with large technology change, there could be huge negative secondary effects. Several possible negative consequences are already foreseeable and raising concern. They represent significant hurdles to industrialization unless successfully anticipated and ameliorated.

  1. Congestion
  2. Job loss

I’ll sketch out these hurdles in two more parts to come.

Innovation Imperatives in the Digital Age

Even a casual look into the history of insurance reveals its rapid evolution over the last decade – from a slow-paced and highly regulated industry to one consumed with technology transformation.

Until recently, insurers have grappled with challenges of engaging millennials, managing investments, simplifying systems, improving combined ratios and driving growth. Today, however, a slew of disruptive forces are changing the playing field. The availability of new user experiences for policy holders, coinciding with the sprouting of insurtech, has created asymmetric competition for established carriers. Legacy products designed decades ago are unable to support the deluge of new data, while millennials are ride-sharing and buying fewer cars. Technologies such as connected health, homes and autonomous vehicles are forcing traditional insurers to re-invent offerings at an unprecedented pace.

See also: 10 Essential Actions for Digital Success  

Market reports reveal that over the past few years technology spending for insurers is higher than the market growth rates, signaling a shift to technology-led-models. The UK FinTech sector alone hopes to create 100,000 jobs and seed $8 billion in investments by 2020. In view of these developments, speed-to-launch will become a real differentiator. Carriers strive to launch products in three to four months to stay abreast of customer demand. They also need to accelerate the integration of enterprise risk management into decision-making for these emerging products. Data monetization and customer-centricity will become key imperatives while lights-on cost continues to be sucked out of legacy platforms.

Using technology to re-invent insurance

In the face of myriad transformation alternatives and confusing consultant-speak, choosing the right path can be tedious. To address this challenge, I suggest a three-dimensional approach that maps outcomes to technologies. I strongly believe that this framework will empower insurance organizations in making informed decisions on how technology can drive future growth.

A2C: Artificial intelligence (AI), Automation and Cloud — This category comprises new and emerging technologies that help insurers improve efficiency, reduce cost and scale easily. For instance, the adoption of cloud platforms and agile infrastructure continues to be a hot trend among insurance providers given the radical performance advantages. Automation is helping organizations achieve huge cost benefits and efficiency improvements by automating repetitive processes and eliminating the risk of human error. I find that robotic process automation (RPA) or software robots are best-suited for back-office insurance processes such as claims processing, billing reconciliation and subrogation. While adoption of machine learning is still nascent in the industry, companies are beginning to deploy chatbots for front-end processes. For instance, ICICI Lombard has developed a chatbot, MyRA, that engages with customers to sell policies and execute transactions without human intervention.

D3C: Design, Digitization, Data and Consulting — This category comprises mature technologies that help insurance companies accelerate revenue growth. In my opinion, as demand for intuitive policies rises, product innovation will become a key differentiator for insurers. Carriers must listen closely to what their customers are saying and develop products that meet their needs. Here, Design thinking can be a vital tool for product rethink that meets the key criteria of desirability, feasibility and viability. When design thinking is coupled with digitization, companies can access advanced ways of improving efficiency and tracking customer sentiment. Analyzing such customer feedback provides valuable insights into how insurers should revamp user interfaces to deliver delightful customer and user experiences. Digitization also supports insurers in providing self-service dashboards and omni-channel capabilities for customers to interact with their providers, thereby increasing customer stickiness. However, any initiative involving digital or design thinking must be reinforced with a strong data strategy. This is why I highlight the importance of investing in intelligent systems that collate unstructured and structured data to gain a holistic customer view. Such solutions enable extreme product and service personalization such as usage-based policies, customized pricing and claims validation across auto, life and home insurance. Consider how Ford is partnering with IVOX to develop a technology that gives insurers insights into driver performance, to lower premiums. Finally, such innovation requires robust partner ecosystems, underscoring the need for strong consulting services. Seamless collaboration across partners is critical if design, digital, data and consulting are to generate tangible value.

CoLT: Core systems, Legacy systems and Total outsourcing — Over the years, while some insurers have built robust albeit monolithic enterprise applications, others have grown through mergers and acquisitions. Both now have an intricate web of IT infrastructure and legacy systems. Managing these inherited systems is a cost that insurers are forced to bear. McKinsey estimates that handling this complexity accounts for 75% of the operational and IT costs when it comes to servicing policies. Not surprisingly, many insurers choose outsourcing as a solution because it makes the bloat appear low. Third-party service providers are better equipped with the skills and infrastructure as well as the agility to adopt innovative technologies. Further, insurers will need to reinvent existing systems to meet increasing customer demand for better services and products. This can be a heavy burden on organizational budgets, particularly when dealing with legacy core systems. This is a key concern as stricter data security laws increase the liability for penalties. I strongly feel this is where leading technology service providers can demonstrate their expertise. Best-in-class technology solutions can help insurers modernize their legacy systems at lower cost to improve efficiency and performance. Additionally, intuitive solutions allow insurers to on-board new technologies and enjoy sophisticated digital capabilities while reducing total cost of ownership (TCO).

See also: Seeing Through Digital Glasses  

Thus, technology service providers seeking to provide real business value to insurance organizations must design solutions that deliver innovation in the above three categories. Application modernization, cloud computing, automation and other new technologies will help insurers optimize their core systems, develop customer-centric insurance products and streamline underwriting and risk management. Such capabilities will empower insurance companies to build and sustain competitive edge in the digital age.

Car Makers, Insurers: Becoming Partners?

When “Car and Driver” magazine debuted more than 60 years ago (originally titled Sports Cars Illustrated), nobody could have envisioned the approaching changes that would transform life as we knew it – including all things automotive and consumer. Today, the expression “car and driver” suggests a completely different meaning as automobiles are becoming “driven” by software and technology and their owners are becoming passengers – and increasingly we are riding in vehicles we don’t even own but rather share or rent.

But while we await our future, current innovations in vehicle and consumer technologies have already emerged to create a transition period full of complex challenges and issues accompanied by potentially significant opportunities for all participants. While much attention is being paid to the emergence of telematics and the connected car, and seemingly endless amounts of investment capital are flowing to the many innovative and promising startups sprouting in this fertile global environment, something even more consequential is also beginning to evolve. Auto insurers and auto makers – once basically adversaries – are beginning to cooperate around many of the related opportunities.  

See also: 3 Technology Trends Worth Watching  

These two industries, which serve and share a common customer base, have traditionally been wary of one another because they had so many conflicting interests. Carriers insure the people who drive the cars that OEMs make, and, when accidents inevitably occur, liability is frequently brought into question to protect the interests of one from the other. In addition, franchised new car dealers, upon whose success OEMs depend for sales and vehicle distribution, earn significant revenues from selling a variety of related products and services – including warranties and insurance, another area of potential conflict. Finally, when insured vehicles end up in collision repair shops as a result of accidents (which happens more than 20 million times a year), insurance carriers do their best to manage repair costs by encouraging these shops to find and use less expensive parts, which costs OEMs and their franchised new car dealers significant parts sales revenues. And, at a higher level, insurers and OEMs value and fiercely protect their customer relationships and have no interest in sharing them with others.   

However, these dynamics are quickly changing as new mobile technologies are rapidly transforming consumer behavior and expectations and as new connected car and automated driver assist technologies begin to present significant new challenges as well as exciting opportunities to both auto insurers and OEMs. It is far from a given that today’s auto market share leaders will enjoy similar shares of future autonomous vehicle sales, and it is equally uncertain as to by whom and how these vehicles will be insured.

Tesla is positioning itself to do both. And so the ancient proverb that “the enemy of my enemy is my friend” seems to apply very well here. Evidence of insurer/OEM partnerships, both direct and indirect, is plentiful and growing daily.

Insurer/OEM connected car partnerships date back to as early as 2012 and include State Farm/Ford, Progressive/GM OnStar, Allstate/GM OnStar and Nissan/Liberty Mutual. In 2015, Ford conducted a “Data Driven Insurance” pilot program that provided participating drivers with their driver history for use in obtaining auto insurance. In 2017, GM OnStar began offering its subscribers 10% discounts on auto insurance from participating carriers including National General, 21st Century, Liberty Mutual, State Farm and Plymouth Rock.  

And data and analytics information providers Verisk and LexisNexis Risk Solutions, which collect data and analytics solutions for use by the insurance industry, have both recently launched telematics data exchanges with OEM participants including GM and Mitsubishi. Consenting connected-car owners have the option to contribute their driving data and seamlessly take advantage of insurers’ usage-based insurance (UBI) programs designed to reward them for how they drive.

Other innovative telematics data models include BMW CarData, which allows owners to share customized data with pre-approved third-parties such as insurers, auto repair shops and other automotive service providers. Drivers can obtain custom insurance coverage based on their exact number of miles driven while repair shops could automatically order parts in advance of service appointments.

For carriers, existing data pools and analytics tools will become less useful than real-time data streaming from connected cars coupled with increased proficiency in predictive modeling and machine learning. OEM/insurer partnerships can enable both parties to share the costs and co-develop big data mining technologies and advanced analytics methodologies to benefit their respective businesses. Insurers can improve underwriting and claims processes while OEMs can improve vehicle safety, design and performance.

Data provided by connected-car devices could be used to initiate claims processing, order damaged parts, triage required collision repair and manage other third-party services (e.g. towing, rental, appraisal) and record accident dynamics as well as occupant placement. OEM/insurer partnerships sharing this data could lead to better claims service and satisfaction and more reliable injury claim evaluation. OEMs could use this data to improve vehicle and occupant safety and could ensure that repairs are performed at properly certified collision repairers and that appropriate parts are used in the repair.

OEMs and insurers can partner to offer customers innovative customer experiences, becoming primary points of contact for risk prevention and new hybrid insurance products as well as dealer parts, service and sales opportunities. New revenue sources for both parties could include Intelligent GPS for theft recovery, real-time notifications of traffic and other travel inconveniences, intelligent parking, location-based services, safety and remote maintenance services. Cost duplication from currently overlapping services such as roadside assistance and towing could be eliminated by single-sourcing such services.

See also: The Evolution in Self-Driving Vehicles  

To be sure, other telematics data business models have emerged that could threaten OEM/insurer partnerships.  In June 2017, BMW and IBM announced the integration of the BMW CarData network with an IBM cloud computing platform that could help as many as 8.5 million German drivers who grant permission to diagnose and repair problems save on car insurance, and take advantage of other third-party services. IBM can also collect data from other OEMs over time, and BMW plans to expand the program to other markets. And technology companies, including Automatics Labs and Otonomo, are seeking consumer consent to sell data through their exchange platforms.

While we await the day that self-driving vehicles dominate our roadways – which will no doubt make many of these driver data initiatives basically irrelevant – we have the most pragmatic of all reasons why OEM/insurer partnerships make sense. Participants can mitigate their risk and reduce their investments in these costly but still relatively short-term opportunities as they position their companies for the as-yet-undefined future of transportation and insurance.

When Will the Driverless Car Arrive?

When Chris Urmson talks about driverless cars, everyone should listen. This has been true throughout his career, but it is especially true now.

Few have had better vantage points on the state of the art and the practical business and engineering challenges of building driverless cars. Urmson has been at the forefront for more than a decade, first as a leading researcher at CMU, then as longtime director of Google’s self-driving car (SDC) program and now as CEO of a driverless car dream team at Aurora Innovation.

Urmson’s recent “Perspectives on Self-Driving Cars” lecture at Carnegie Mellon was particularly interesting because he has had time to absorb the lessons from his long tenure at Google and translate those into his next moves at Aurora. He was also in a thoughtful space at his alma mater, surrounded by mentors, colleagues and students. And, it is early enough in his new startup’s journey that he seemed truly in “perspective” rather than “pitch” mode.

The entire presentation is worth watching. Here are six takeaways:

1. There is a lot more chaos on the road than most recognize.

Much of the carnage due to vehicle accidents is easy to measure. In 2015, in just the U.S., there were 35,092 killed and 2.4 million injured in 6.3 million police-reported vehicle accidents. Urmson estimates, however, that the real accident rate is really between two and 10 times greater.
Over more than two million test miles during his Google tenure, Google’s SDCs were involved in about 25 accidents. Most were not severe enough to warrant a regular police report (they were reported to the California DMV). The accidents mostly looked like this: “Self-driving car does something reasonable. Comes to a stop. Human crashes into it.” Fender bender results.
While we talk a lot about fatalities or police-reported accidents, Urmson said, “there is a lot of property damage and loss that can be cleaned up relatively easily” with driverless technology.
2. Human intent is the fundamental challenge for driverless cars.
The choices made by driverless cars are critically dependent on understanding and matching the expectations of human drivers. This includes both humans in operational control of the cars themselves and human drivers of other cars. For Urmson, the difficulty in doing this is “the heart of the problem” going forward.
To illustrate the “human factors” challenge, Urmson dissected three high-profile accidents. (He cautioned that, in the case of the Uber and Tesla crashes, he had no inside information and was piecing together what probably happened based on public information.)

Google Car Crashes With Bus; Santa Clara Transportation Authority

In the only accident where Google’s SDC was partially at fault, Google’s car was partially blocking the lane of a bus behind it (due to sand bags in its own lane). The car had to decide whether to wait for the bus to pass or merge fully into the lane. The car predicted that the remaining space in the bus’s lane was too narrow and that the bus driver would have to stop. The bus driver looked at the situation and thought “I can make it,” and didn’t stop. The car went. The bus did, too. Crunch.

Uber’s Arizona Rollover

Uber Driverless Car Crashes In Tempe, AZ

The Uber SDC was in the leftmost lane of three lanes. The traffic in the two lanes to its right were stopped due to congested traffic. The Uber car’s lane was clear, so it continued to move at a good pace.

A human driver wanted to turn left across the three lanes. The turning car pulled out in front of the cars in the two stopped lanes. The driver probably could not see across the blocked lanes to the Uber car’s lane and, given the stopped traffic, expected that whatever might be driving down that lane would be moving slower. It pulled into the Uber car’s lane to make the turn, and the result was a sideways parked car.

See also: Who Is Leading in Driverless Cars?  

Tesla’s Deadly Florida Crash

Tesla Car After Fatal Crash in Florida

The driver had been using Tesla’s Autopilot for a long time, and he trusted it—despite Tesla saying, “Don’t trust it.” Tesla user manuals told drivers to keep their hands on the wheel, eyes in front, etc. The vehicle was expecting that the driver was paying attention and would act as the safety check. The driver thought that Autopilot worked well enough on its own. A big truck pulled in front of the car. Autopilot did not see it. The driver did not intervene. Fatal crash.

Tesla, to its credit, has made modifications to improve the car’s understanding about whether the driver is paying attention. To Urmson, however, the crash highlights the fundamental limitation of relying on human attentiveness as the safety mechanism against car inadequacies.

3. Incremental driver assistance systems will not evolve into driverless cars.

Urmson characterized “one of the big open debates” in the driverless car world as between Tesla’s (and other automakers’) vs. Google’s approach. The former’s approach is “let’s just keep on making incremental systems and, one day, we’ll turn around and have a self-driving car.” The latter is “No, no, these are two distinct problems. We need to apply different technologies.”

Urmson is still “fundamentally in the Google camp.” He believes there is a discrete step in the design space when you have to turn your back on human intervention and trust the car will not have anyone to take control. The incremental approach, he argues, will guide developers down a selection of technologies that will limit the ability to bridge over to fully driverless capabilities.

4. Don’t let the “Trolley Car Problem” make the perfect into the enemy of the great.

The “trolley car problem” is a thought experiment that asks how driverless cars should handle no-win, life-threatening scenarios—such as when the only possible choices are between killing the car’s passenger or an innocent bystander. Some argue that driverless cars should not be allowed to make such decisions.

Urmson, on the other hand, described this as an interesting philosophical problem that should not be driving the question of whether to bring the technology to market. To let it do so would be “to let the perfect be the enemy of the great.”

Urmson offered a two-fold pragmatic approach to this ethical dilemma. First, cars should never get into such situations. “If you got there, you’ve screwed up.”  Driverless cars should be conservative, safety-first drivers that can anticipate and avoid such situations. “If you’re paying attention, they don’t just surprise and pop out at you,” he said. Second, if the eventuality arose, a car’s response should be predetermined and explicit. Tell consumers what to expect and let them make the choice. For example, tell consumers that the car will prefer the safety of pedestrians and will put passengers at risk to protect pedestrians. Such an explicit choice is better than what occurs with human drivers, Urmson argues, who react instinctually because there is not enough time to make any judgment at all.

5. The “mad rush” is justified.

Urmson reminisced about the early days when he would talk to automakers and tier 1 suppliers about the Google program and he “literally got laughed at.”  A lot has changed in the last five years, and many of those skeptics have since invested billions in competing approaches.

Urmson points to the interaction between automation, environmental standards, electric vehicles and ride sharing as the driving forces behind the rush toward driverless. (Read more about this virtuous cycle.) Is it justified? He thinks so, and points to one simple equation to support his position:

3 Trillion VMT * $0.10 per mile = $300B per year

In 2016, vehicles in the U.S. traveled about 3.2 trillion miles. If you could bring technology to bear to reduce the cost or increase the quality of those miles and charge 10 cents per mile, that would add up to $300 billion in annual revenue—just in the U.S.

This equation, he points out, is driving the market infatuation with Transportation as a Service (TaaS) business models. The leading contenders in the emerging space, Uber, Lyft and Didi, have a combined market valuation of about $110 billion—roughly equal to the market value of GM, Ford and Chrysler. Urmson predicts that one of these clusters will see its market value double in the next four years. The race is to see who reaps this increased value.

See also: 10 Questions That Reveal AI’s Limits  

6. Deployment will happen “relatively quickly.”

To the inevitable question of “when,” Urmson is very optimistic.  He predicts that self-driving car services will be available in certain communities within the next five years.

You won’t get them everywhere. You certainly are not going to get them in incredibly challenging weather or incredibly challenging cultural regions. But, you’ll see neighborhoods and communities where you’ll be able to call a car, get in it, and it will take you where you want to go.

(Based on recent Waymo announcements, Phoenix seems a likely candidate.)

Then, over the next 20 years, Urmson believes we’ll see a large portion of the transportation infrastructure move over to automation.

Urmson concluded his presentation by calling it an exciting time for roboticists. “It’s a pretty damn good time to be alive. We’re seeing fundamental transformations to the structure of labor and the structure transportation. To be a part of that and have a chance to be involved in it is exciting.”

The Uberization of Insurance

Our nomination for word of the year is, by far, “uberization.”

This term is used to describe the growing deluge of companies that offer on-demand services from cars to homes to labor, and much more. Many commentators view this economic transformation as a revolution that will see our entire economy shift from one of consumption, to one of access.

And we think they’re correct.

The Rise of On-Demand

The key to an “uberized” economy is where on-demand services meet crowdsourced labor solutions. You see it everywhere. Even traditional businesses are learning new tricks from an avalanche of high-profile acquisitions. Whether it’s Expedia’s purchase of Homeaway, GM’s buyout of Sidecar or Ford’s investment in Lyft, this shift is becoming more undeniable.

On-Demand for Insurance

Now, on-demand services are coming to the insurance industry, the most risk-averse industry, by its very nature. The insurance industry has become more nimble–mostly out of necessity, but that’s a story for another day.

See also: How On-Demand Economy Can Prosper  

Insurance carriers are learning quickly that they need to adapt to the demand of, well, on-demand services. And the integration of the gig economy is the next step in the business evolution of the traditional insurance sector.

Tough Questions for the Insurance Industry

What does the “uber of insurance” mean? What opportunities and challenges does it bring to the industry? The gig economy, sharing economy, 1099 economy, on-demand economy or whatever you want to call it isn’t going away, and consumer participation continues to grow.

Earners, consumers and the old guard of the supply chain are eager to find ways to diversify and optimize business solutions.

How do you satisfy the demand for on-demand data gathering? Claims handling and processing? How does the insurance industry gather the data it needs effectively, efficiently and accurately?

Uber, Lyft, and Airbnb have not only demonstrated that they fill a need in the marketplace, but often they do it better than the traditional options – as uncomfortable a thought as that may be for the old guard in the supply chain.

Can this model work for the insurance industry? It can, and this is how.

Hug Your Smartphone, Save a Tree

Mobile technology is your new best friend when it comes to data gathering for claims handling and processing. The insurance industry is traditionally paper-intensive. Paper is no longer a security blanket, but a wet blanket weighing down processes and impeding efficiency.

Candy Crush and Capturing Data

It’s easy to marvel at the innovation of smartphones from the most addictive apps to the most useful. I won’t get into my Candy Crush addiction; I’m seeking professional help.

The point is to make smartphones work for you and your business processes. Today, smartphones are essential to the daily lives of most of us, providing communication, connectivity, schedules, entertainment and even our wallets. Think about how you can leverage people’s familiarity and affinity for their smartphones by merging it with your smart application development and deployment.

Capturing data has never been easier than point and click…Oops, I mean a finger swipe.

Now more than ever data can be captured, optimized and automatically entered into your data systems and processes. This new process can facilitate the seamless flow of data into business processes without risking it getting stuck to the bottom of someone’s shoe, misfiled, misplaced or eaten by the proverbial dog.

For the notepad next to your computer: seamless data integration at the point of data capture.

It sounds like a dream, doesn’t it?

Sharing Is Caring

First referred to as the sharing economy or the gig economy, the “uberization” of the workforce didn’t originate with Uber. But I’m still voting for “uberization” for word of the year. Merriam-Webster is next on my contact list.

People have always done odd jobs that fit their skill set, hobby, or need. Uber, Turo, Airbnb and WeGoLook through mobile technology have taken this tried-and-true individual entrepreneurship spirit not only to the next level, but to a measurable impact on the economy. Just consider recent sharing economy industry projections made by PwC. I won’t spoil it for you, but you’ll soon be acquainted with the word “mega trend.”

See also: Uber’s Thinking Can Reinvent the Agent  

Crowdsourced labor solutions not only provide diversified earning opportunities, but they also provide options to workers, consumers and businesses alike. Remember our talk about being nimble?

All parties can scale up or down as they choose. They can also select where and how they participate in the gig economy and leverage it to provide for their financial or business goals.

As these on-demand solutions grow, expand and diversify, companies and consumers will have the opportunity to test and identify the best solutions for them, all with a swipe of their smartphone.

Free Market for Solutions

Some will argue the gig economy is the free market at its best, others will argue it’s at its worst. Like anything, it comes back to how individuals and companies strategically apply these solutions to their business challenges.

In the insurance industry, data gathering and claims processing will always resolve around how you can do it faster and better and with fewer mistakes. As the saying goes, “time is money.”

With the help of technology, the reach of smartphones and crowd labor — insurance companies can standardize and streamline data gathering, claims processing and other simple tasks while controlling costs.

For instance, why dispatch an employee across the metro, county, state or even country, incurring all the related expenses, time delays to gather data and take pictures when you can dispatch someone who’s already there?

Not only do you save time travel, and employee productivity, but thanks to the near-universal familiarity with smartphones and standardized mobile apps, you don’t have to train workers.

What if there was an Uber of Insurance? It’s not really a matter of “if” anymore, but of “when” and “how.” The when is now, and the how is through the growing relevance of the insurtech disruption.