Tag Archives: ADAS

Self-Driving Vehicles: A Wake-up Call

How close are self-driving vehicles to truly becoming a reality? The answer depends on who is being asked.

Automotive manufacturers may sheepishly respond with “longer than we proclaimed,” as the initial 2020-2022 predictions give way to timing that is now being held closer to the chest, according to the 2019 J.D. Power Mobility Confidence Index Study. However, this critical—albeit possibly humbling—realization brings to light the intersection of the fantasy vehicles played out on-screen in sci-fi movies and TV shows, and the complexities of the technology necessary to safely maneuver real-world vehicles on public roads in all environmental conditions.

Many consumers who had long dreamed of these fantasy vehicles have since pumped the brakes. Why? Tech failures/errors (71%), risk of vehicle being hacked (57%) and legal liability as a result of a collision (55%) are consumers’ top concerns that were uncovered in the J.D. Power study. As consumers begin to experience first-hand the integral technologies that make self-driving vehicles possible, many believe it will likely be more than a decade before they become a mainstay on public roadways.

Ultimately, one thing both groups agree on is this: Turning dream cars into real cars isn’t simple.

Effects of Real-World Elements on Self-Driving Vehicles

Automakers are put to the test when introducing safety technology in real-world situations. Sure, a vehicle will stop or swerve when it’s supposed to on a closed course, but what about on the road with other vehicles?

Recently, a Tesla Model S crash occurred when the driver had Autopilot engaged and the car hit the back of a fire truck stopped in a high occupancy vehicle (HOV) lane. The safety system is designed to temporarily ignore stationary objects in the roadway to reduce “false alarms,” but, according to one of the many findings of the resulting National Transportation Safety Board (NTSB) report, the fire truck was no false alarm. Even though the name of the technology may imply the vehicle will handle itself in any situation, it’s still imperative for the driver (or operator) to pay attention and take control if necessary, regardless of successful experience with the system’s performance in more ideal situations.

See also: How to Prepare for Self-Driving Cars  

Consumer Trust and Acceptance Needed for Adoption of Self-Driving Vehicles

No manufacturer has a ready-for-purchase, self-driving vehicle available today. The safety technology in 2019/2020 model-year vehicles is what the industry calls Advanced Driver Assistance Systems (ADAS). These include features like adaptive cruise control, forward collision warning, lane-keeping assistance systems and automatic parking, to name a few. Although truly automated features are not yet available, the driver must remain engaged regardless of what safety system is activated, even if his or her feet are off the pedals and hands are off the steering wheel. Unfortunately, consumers don’t seem to fully understand this, which will hurt future acceptance of self-driving vehicles, as crashes occur that are caused by misunderstanding of systems.

ADAS features—the building blocks for full vehicle automation—are designed to notify the driver of situations that may lead to a collision and step in if the driver fails to act. Roughly 60% of new vehicles sold today are equipped with some or all of these technologies, which the National Highway Traffic Safety Administration (NHTSA) says could reduce crashes and save thousands of lives.

However, many drivers have deemed some ADAS alerts so annoying or bothersome that they disable them. Nearly one-fourth (23%) of customers with lane-keeping and centering systems—one of the most prevalent safety technologies on the road today—fall into this category, with 61% sometimes disabling the system and possibly trying to avoid them on future vehicle purchases, according to the 2019 J.D. Power Tech Experience Index Study.

Consumers who are concerned about cars being able to drive themselves want more information about these complex systems, as well as more channels to learn how to use them or how and why they kick in. Dealers remain one of the main partners to educate consumers about what these technologies bring to the table and help consumers trust that systems are going to kick in when they’re supposed to, as well as understand when they’re working properly.

The Cost of Repairing Safety Technology

Automakers have developed incredibly rigorous standards of research and development, testing and manufacturing to ensure these technologies work reliably. However, the same cannot be said of the automotive service and repair shops we depend upon to safely fix the 13 million vehicles involved in a collision each year.

There is no clear way for consumers to know the ADAS features in their vehicle have been properly repaired following a collision even though they may receive a report or invoice stating this to be the case. This is another area where trust will help garner consumer adoption of self-driving vehicles.

The repair industry is still trying to understand and operationalize these very complicated and delicate technologies. For example, many ADAS features rely on cameras to help determine a vehicle’s position in relation to the road, stationary objects and moving vehicles or people. These cameras may be mounted in different areas depending on the vehicle’s make and model. Something as seemingly simple as replacing a cracked windshield could mean the difference on whether a particular safety system continues to properly engage, if the new windshield isn’t designed or calibrated for the correct model’s specifications.

Even though most consumers leave the repair shop trusting that their vehicles are functioning properly, given the wide disparity between manufacturers’ product offerings, the complexity of calibration that is required for these technologies and the repair facility’s capabilities, that trust is possibly misplaced.

See also: The Evolution in Self-Driving Vehicles  

It would be beneficial for the service and repair industry, car buyers and the insurance industry as a whole for automakers to develop a uniform process and governance that all repair facilities can use to verify that any repairs for vehicles equipped with ADAS features are calibrated correctly. This would help ensure the accuracy and consistency of driver assistance technology repairs through a vehicle’s lifecycle. Unfortunately, there’s no clear indication of when something like this might be put into place, which further limits the potential for fully automated vehicles to grow beyond a niche in the automotive marketplace.

The main factor in making self-driving vehicles a reality is transparency. Keeping consumers informed about all aspects of the technology they’re investing in—why they need it, how it works, when it will activate and how to tell if it’s still functioning as intended—will go a long way to keep this journey marching forward with fewer roadblocks.

New Entrants Flood Into Insurance

New entrants seem to be coming out of the woodwork in insurance. The insurtech movement, the advance of emerging technologies and the appetite of the global tech titans are all contributing to new entrants, new partnerships and new business models. A few recent examples illustrate the new interest in insurance from those both inside and outside of the insurance industry.

  • WeWork partners with Lemonade. In what seems like a very natural partnership, WeWork plans to offer its WeLive members renters’ insurance through Lemonade. WeLive members rent fully furnished apartments from WeWork for short-term situations.
  • Credit Karma enters insurance. This fintech intends to build on customer relationships to expand into auto insurance. While the initial focus will be education – helping Credit Karma customers understand how credit and adverse driving affects insurance rates – the longer-term goal is to provide yet another shopping/comparison site.
  • BMW and Swiss Re partner for ADAS scores. BMW Group and Swiss Re will collect telematics data from vehicles related to the use of ADAS (Automated Driver Assistance Systems) and build scores that can be used by primary insurance companies.
  • Lending Tree buys QuoteWizard for $370 million. Fintech Lending Tree, which has been on a buying spree, moves into insurance with the acquisition of insurance comparison shopping site QuoteWizard.
  • Travelers partners with Amazon for the smart home. Travelers will set up a digital storefront on Amazon featuring smart home devices for a discount (especially security-related devices) as well as discounts on homeowners’ insurance.
  • JetBlue invests in insurtech Slice. This appears to be a pure investment play, but it is still interesting that an airline would be following insurtech and seeking investment opportunities.

Something is going on here. It is not as if there have never been new entrants or that companies from other industries have ignored insurance. But the flurry of activity and innovative partnerships, investments and market approaches may represent a bigger trend. Insurance is transforming, and, despite some of the doom and gloom warnings, a case can be made that there is more opportunity than ever for the industry. Even in the examples provided above, the emphasis is more on new opportunities than displacing incumbent insurance players. Indeed, in the Swiss Re and Travelers cases, the incumbents are part of the new partnerships – and these are just two of many examples.

See also: 5 Cs of Transformation in Insurance  

One of the main themes of the examples highlighted above is the attention on distribution and customer relationships. While insurtechs are working with insurers on many opportunities to improve underwriting, claims, and other areas, so far the new entrants from outside the industry don’t appear to have the appetite to underwrite risk and handle claims. This may change, but it is likely that there will be even more interest from outside insurance in capitalizing on customer relationships. Above all, these new entrants and innovative partnerships serve to accelerate the transformation of insurance.

Suddenly, Driverless Cars Hit Bumps

Recent tests by The Insurance Institute for Highway Safety on two key ADAS capabilities cast doubt on the efficacy of these technologies and thus on how soon full autonomy is likely to affect auto insurance premium.

Anyone insuring automobiles is paying a lot of attention to the development of ADAS (advanced driver assistance systems) and of fully autonomous vehicles.

Many of the underlying technolgies used in ADAS (e.g. cameras, radar, lidar, AI) will also be used in fully autonomous vehicles. However, the demands that a fully autonomous vehicle places on these technologies are quite different than the demands of an ADAS-equipped vehicle. ADAS-equipped vehicles will pass control to and from human drivers (or send warnings to human drivers) in various circumstances. Fully autonomous vehicles will have no hand-offs and no warnings because there are no human drivers to receive them.

The Insurance Institute for Highway Safety (IIHS) recently ran a series of tests of two key ADAS capabilities: adaptive cruise control (ACC) and active lane keeping. ACC maintains a set speed and a specified distance from a car in front of the car with ACC. Active lane keeping automatically maintains the car within its current lane.

See also: Autonomous Vehicles: Truly Imminent?  

Vehicles with ACC and active lane keeping are at Level 2 on the SAE International scale. This is a widely recognized framework demarcating degrees of autonomy — ranging from Level 0 (no automation) to Level 5 (fully autonomous).

Source: NHTSA https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety

Notice that Level 2 is a long way from Level 5.

The IIHS tested five well-regarded vehicles:

  • A 2017 BMW 5-series with “Driving Assistant Plus”
  • A 2017 Mercedes-Benz E Class with “Drive Pilot”
  • A 2018 Tesla Model 3 and a 2016 Model S with “Autopilot” (software versions 8.1 and 7.1, respectively)
  • A 2018 Volvo S90 with “Pilot Assist.”

The results of these tests were reported in IIHS and HLDI publication, Status Report (Aug. 7, 2018).

The results were not pretty.

  • In one test on a public roadway, the Mercedes was aware of a stationary vehicle in front of it but continued without reducing speed, until the human driver applied the brakes.
  • In a 180-mile test drive, the Tesla Model 3 slowed without an appropriate cause 12 times (including seven instances of tree shadows on the road).
  • In testing active lane keeping on curves; the BMW, the Mercedes and the Volvo were unable to stay in their lane without the driver providing steering assistance.
  • The vehicles’ active lane keeping capability was also tested when they reached the top of hills. At the top, some cars’ technologies essentially lost sight of the lane markings on the road. The BMW failed to stay in its proper lane (without driver intervention) in all 14 tests. The Volvo stayed in the lane in nine of 16 tests. The Tesla Model S swerved right and left as it attempted to locate the appropriate lane. Sometimes it also entered an adjacent lane or drove onto the shoulder.

There is evidence that ADAS technologies do reduce accidents and insured losses—here and here.

See also: Autonomous Vehicles: ‘The Trolley Problem’  

However, the real world test results of Level 2 technology in these five highly regarded models were certainly disappointing. Level 2 autonomy requires the driver to remain engaged and constantly monitor the environment. The key words are “remain engaged.” People, while driving, often do many things other than remaining engaged.


The shared responsibility between less-than-perfect humans and less-than-perfect technologies of Level 2 implies that either the technologies have to become intrinsically better — or they must find ways to compensate for imperfect humans.

As mentioned, you cannot make a straight-line projection of elapsed time from the current state of Level 2 ADAS technology to the arrival of ready-for-prime-time Level 5 fully autonomous technology.

Why AI IS All It’s Cracked Up to Be

A lot of people are talking about the promise of artificial intelligence (AI), and some say it’s too early to evaluate its long-term impact. I disagree. I believe we need to evaluate AI’s value now, because it’s already beginning to fundamentally change the way auto insurers do business.

A sweeping statement, perhaps, but there’s a lead-up to this discussion that is creating the perfect storm for P&C insurers.

First, insurer performance is challenging, and most every insurer I speak with is racing to identify ways to reduce expenses while continuing to offer desirable products to savvy consumers — consumers who expect insurance to be delivered and serviced just as seamlessly as their interactions with their favorite online retailer.

Next, vehicle complexity is making it extremely difficult to price risk, predict frequency (largely due to advanced driver assistance systems, or ADAS) and understand increasing repair costs, thanks to enhanced electronic content, such as the sensors in newer vehicles.

In this environment, AI can play a critical role, helping insurers bring expenses back in line while creating opportunities to deliver a better insurance experience for consumers. And, as vehicles become more connected, streaming more data, the role of AI will only grow.

AI Now

If you’re still not clear on what exactly AI is, it refers to programs that are capable of learning to make decisions more like humans. AI is at work all around us – when robots control other robots on the manufacturing line, intelligently automating the management and optimization of financial portfolios, detecting cancer using MRIs and machine vision and powering self-driving cars. In fact, AI is becoming so prevalent it’s expected to create $1.2 trillion in business value by the end of this year and $3.9 trillion by 2022.

AI in Insurance

It’s now our industry’s turn to put AI to work. What we’re seeing in other industries is now happening in claims. AI is being injected into key points in the claim process, helping to create value that can be seen (and felt) inside and outside the organization. Meaning, AI done right can yield improvements designed to enhance the experience of all stakeholders.

From an internal efficiencies perspective, consider AI’s impact on workflow challenges. As just one example, let’s look at the value of mobility and IoT, telematics in particular, because this is foundational to AI-driven improvements in processes. As you read on, think about all the existing processes and labor currently linked to your own auto claims area, because even the workflow that initiates a claim–in place for a hundred years–is now being changed, thanks to AI.

See also: Strategist’s Guide to Artificial Intelligence  

The New Claims Workflow

There is a new claims workflow taking hold right now, not some point in the future.

First, policyholders won’t call the insurer when they experience an accident—the insurer will contact him/her. This is because the insurer will apply AI to telematics data, setting an alert tagged to view the rate of change of the vehicle and determine in real time that there has been an accident.

Now apply AI-driven conversations via chatbots with customers at scale, in real time, to guide them through the claims process after that accident occurs. In our example, the chatbot asks the claimant for a photograph—an automated, back-end review determines suitability of the photograph, enabling the insurer to determine with high accuracy and in real time whether the vehicle is likely to be a total loss or repairable and advises the policyholder accordingly. Fast, transparent communication.

If the damage is repairable, the chatbot asks for additional facts and photos. The insurer detects location and severity of the damage by automatically comparing it against millions of collision variables and applying predictive, model-driven AI. Heat maps are used as visible illustrations of the damage, building credibility with your policyholder.

The internal workflow changes further when virtual inspections are powered by AI. Remote appraisers can be given photos, heat maps and even a guided estimating tool, reducing time and effort in the field and yielding higher accuracy and productivity, processing 15 or more estimates per day versus four to five estimates in a pre-AI, field inspection world. Once the estimate is written, information gleaned from the photographs is fused with insights gleaned from CCC’s wealth of estimating experience to determine if the estimate is in line with insurer guidelines. The appraiser views the pictures and, applying AI, builds out the estimate with interactive prompts to improve it.

Thanks to AI, the policyholder is given an estimate in significantly less time than is possible today. AI also fuels communication that is more transparent and consistent with consumer expectations. When a vehicle is repairable, the policyholder doesn’t need to wait impatiently for days while the claims and repair process slowly unfolds in ways they don’t know about or understand. Instead, a consumer has access to a host of chatbot and SMS technology, where messages communicate the necessary steps to resolve the claim. Similar to how we book a restaurant reservation, the policyholder can schedule a repair shop appointment; and like an airline that can notify us of flight status, repair status updates have become standard practice for shops.

Through the use of AI, services can be dispatched and intelligently routed to the repair shop of choice—or the salvage yard in the case of a total loss, saving time and money on additional tows and storage fees. From the policyholders’ perspective, the insurer continues to prove that it has their best interests at heart, building trust and loyalty at a pivotal time in the relationship.

In other words, an experience is built around AI, putting it to work to benefit the consumer. And, the same thing is happening for the estimator.

On the casualty side, insurers handling first- and third-party claims can leverage AI to help inform investigations and increase loss cost management accuracy. For example, AI can detect the principal direction of force and the delta V to predict the likely physical injuries and outcomes of the vehicle’s occupants. There are early indications that integrating this data for analytics and intelligence purposes can improve claims outcomes, both by qualifying injury causation and revealing whether certain injuries are consistent with the facts of the accident.

See also: Why AI-Assisted Selling Is the Future  

AI Next

What I’ve just described is the tip of the iceberg. We are at the tipping point. Connected cars will drive another wave of claims innovation. According to IHS Markit, worldwide sales of connected cars will reach 72.5 million units in 2023, up from 24 million units in 2015. That means, in just over eight years, almost 69% of passenger vehicles sold will be exchanging data with external sources.

What does that mean for us?

If I’m looking into my crystal ball, here’s what I see:

When there’s an accident, the amount of instantaneous information available to us will probably be 10 times what it is today. We won’t need policyholders to take the photographs I mentioned earlier. With telematics data, we will have all of the information that is knowable about an accident event, which makes the AI even faster and more accurate and claims management and related outcomes even that much better.

If the car isn’t that safe, it will be picked up by a self-driving tow truck and taken to the shop while another self-driving car will come pick up the policyholder. By the way, at the shop, no one’s going to have to order any parts; the parts will be ordered within minutes, maybe even seconds after the accident.

From an internal and external perspective, there is no downside to embracing AI’s promise: reduced claims costs, increased customer satisfaction and improved business outcomes – today and into the future. The value is there; the time is now.

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.