DB FPX 8415 Assessment 2 Industry Gap in Practice

DB FPX 8415 Assessment 2 Industry Gap in Practice Student Name Capella University DB-FPX8415 Strategic Decision Making Professor Name Submission Date [blur_section id=”postDownload” button=”Free Download” form_id=”e9df100″] Industry Gap in Practice Slide 01 Hello everyone! My name is __________ and today I will talk about the gap in practice in the connected autonomous vehicles (CAV) industry. The main points will be highlighted that could play a pivotal role in the growth of the industry. Slide 02 Part 1 Executive Summary Industry Context The Insurance Industry today is drastically different. Insurance for self-driving vehicles is projected to develop at an exponential rate, increasing by 13.9% annually until it reaches nearly $60 billion in 5 years (Research and Markets, 2025); within a car insurance company, the overall market growth will be 2.5% annually, totaling $796.8 billion in 2030. The upward trend suggests that although the autonomous car insurance market will keep expanding, albeit at a moderate pace, the autonomous vehicle insurance market can’t necessarily be considered an extension of the traditional market. The automated self-driving entity is typically the manufacturer of the automated vehicle, and the no-user-in-charge operator is a separate legal entity, such as UK firm Blasis, which will be established under the United Kingdom’s Automated Vehicles Act 2024 (Borras, 2025). The Act shifts the center of liability from persons to corporate entities, altogether altering the risk assessment models. Slide 03 Industry Gaps The gap analysis highlights three key mismatches between today’s insurance regimes and what will become a necessary insurance regime with the advent of CAV. To start with, the gap in liability. Fault in existing insurance models revolves around the driver, but CAV technology will involve the manufacturer, developer, and infrastructure provider (Borras, 2025). Second is that of the data and analytics gap. Whereas historical and demographic data are used in existing insurance models to determine insurance, real-time integration of sensor data, software performance data, and cyber data will be required in order to make an underwriting decision with CAV insurance products. Last but not least, there’s the innovation gap. The insurance models that cover existing technologies are one-time, annual contracts, but CAV technology will require dynamic insurance contracts that evolve based on consumer usage (or lack thereof) of CAV technology (Research and Markets, 2025). What industry leaders are faced with is whether they are prepared to undergo incremental change or fundamental change. Vlad Schepkov (2026) shows recent collaboration opportunities, such as between Cheche Group and Volkswagen, Cardif Airstar, that show that smart businesses are already creating the future insurance models. Part 2 Industry Context It’s important to understand the CAV industry context to get insights about the industry. The new segment is seeing a higher growth rate of 13.9% CAGR as compared to the growth rate of the traditional auto insurance segment of 2.5% CAGR, which translates to a $59.8 billion opportunity by 2030 (Research and Markets, 2025a). Technology progresses step-by-step, and it does not go back. “Level 2+”: Cares that remove steering and speed control from the driver’s hands, but still require a driver to operate. This is now commonplace in new car lineups and will make up 40% of new sales by 2035. The numbers are indicative of an existing complexity for insurers. Lastly, the industry has grown to encompass not only insurers and insureds, but manufacturers, technologies, and government as well (Borras, 2025). The Automated Vehicles Act of 2024 is a legal reconstruction of the rules of accountability and creates a new type of legal subject, an authorized self-driving entity, which is ultimately responsible for the vehicle’s performance. Important to note, new car average weights will be over 4,600lbs starting in 2026, increasing accident severity while decreasing accident frequency (S&P Global, 2025). There are over 41 companies participating in an emerging market. Part 3 Industry Gaps in Practice Gap 1: Liability Framework Analysis Perhaps the most basic of the challenges to the traditional insurance model is the liability gap issue. Since the current “fault”, or “breach”, relies on a previous set of rules of negligence, the “fault”, or “breach”, is one belonging to a human driver. This drastically changes with the Automated Vehicles Act 2024, which introduces two new legal entities: In most cases, the manufacturer will be called the automated self-driving entity, and the no-user-in-charge operator will be Waymo or Uber (Garlick, 2026). The difference between the facelifted model and the new one couldn’t be more extreme. The world we are heading towards has been proven. Each CAV will have a Fault Identification with Event Recorder (FIDER) system to detect faults in the software and sensors. Insurers don’t have forensic tools in order to interpret this information independently. Gap 2: Data & Analytics Gap Data and analytics are a huge challenge to the bread and butter of risk assessment. With the current use of historical data and demographic information, insurers are now confronted with unprecedented data sets provided by CAVs, such as lidar data, global positioning system (GPS) paths, decision-making processes, acceleration patterns, incident replays, among others (Research and Markets, 2025b). Insurers can use data to modify policies in real time based on continuous, usage-based risk assessments, replacing traditional risk assessment methods. But the data and analytics gap is due to the fact that manufacturers and tech firms have the data, and they aren’t even sharing information. Manufacturers do not like to disclose information because of Intellectual Property and Trade Secrets issues. What’s evident from the partnerships is the potential that’s there. For example, Cheche Group and VW have formed a partnership to integrate driving behavior data such as harsh acceleration, braking, and turning with road and claims data to deliver unique risk scores to enable better driving and better pricing (Vlad Schepkov, 2026). SDV generates a whole new set of data that insurance companies have to deal with. Two vehicles of the same model and year can have different software definitions. Gap 3: Product Innovation Gap Product innovation gap is the gap between the product being offered and what CAV customers need. In […]