Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(1)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(31)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(149)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

Autonomous Vehicle Insurance: The Present and Near Future

We’re about to witness the evolution of autonomous vehicles from Level 0 to Level 2. While Level 0 is completely human-driven; Level 1 vehicles can control braking and parallel parking themselves. Level 2 vehicles can operate automatically, but with a human ready to control exceptional situations.

The success of self-driving cars depends solely on the safety it brings to transportation. With increased safety, will we even need insurance for autonomous vehicles?

Perhaps, the traditional insurance policies might face a setback. But, autonomous vehicles will certainly open new avenues for innovative insurance products.

The Stevens Institute of Technology predicts that there would be over 23 million fully autonomous vehicles by 2035 in the US alone. 

To stay competitive with the changing dynamics of auto insurance, insurers need to address new risks. But before, let’s take a look at potential risks in the autonomous vehicle insurance sector.

Autonomous vehicle insurance: the evolution of autonomous cars from Level 0 to Level 5

Potential Impact of ‘Autonomous Vehicles’ Revolution

The shift to autonomous vehicles tends to bring dramatic changes in auto insurance premiums.  

Instead of individual policies, researchers foresee insurance policies turning towards original equipment manufacturers (OEMs) and service providers such as ride-sharing companies. The new auto insurance products would be an outcome of the following transportation changes.

New Road Regulations

With autonomous vehicles on the roads, safety regulations are prone to change. For instance, the US National Highway Traffic Safety Administration intends to reconsider its current safety standards to accommodate AVs in existing transportation. But, this reformation will take the presence of human drivers into account.

Increased Safety and Reduced Claims

With increased safety and reduced accident claims, the revenues from traditional premium policies might decline.  

Insurers often follow a “no-fault” system to lower auto insurance costs by taking small claims out of the courts. For minor injuries, insurers compensate their policyholders regardless of who was at fault in the accident. 

However, fender-benders would be more than it is with autonomous vehicles. Also, blockchain in insurance would become integral to investigate the root cause of the accident. And, of course, there won’t be much scope for lenient “no-fault” policies. 

Change in Insurance Liability

Traditional liability insurance pays for the policyholder’s legal responsibility to others for bodily injury or property damage. With autonomous vehicles, the liability is going to shift towards OEMs, suppliers, or car-rental service providers.

Underwriting?

Currently, automakers must adhere to around 75 safety standards. This underwriting considers that a licensed driver will control the vehicle. The safety standards are going to change with more AVs on roads.

The present-day premium is high for a handful of autonomous vehicles because of insufficient data with underwriters and actuaries. However, chances are, major OEMs will cover the insurance premiums in the vehicle cost. 

For instance, Tesla, one of the pioneers of autonomous vehicles, provides auto insurance at 30% lower rates than other insurance providers. Tesla having a better understanding of its vehicles’ technology and repair costs, believes can provide low-cost insurance. This is also a threat to insurance carrier fees.

Scope for New Autonomous Vehicle Insurance Products

Accenture estimates that autonomous vehicles will generate at least $81 billion in new insurance revenues in the US between 2020 and 2025. It also foresees opportunities for insurers in cybersecurity, product, and infrastructure landscapes. Let’s take a look at new auto insurance avenues. 

Cyber Security

While AVs ensure safety, there are unidentified cybersecurity threats. Vehicles fueled by IoT technology deal with comprehensive telematics data. Capturing every moment of the user proposes risks like identity theft, privacy invasion, misuse of personal information, and attacks from ransomware. According to the Center for Strategic and International Studies and McAfee, globally cybercrimes cost around $600 billion annually. The shared data from autonomous vehicles bring the financial sector at risk.

On the other hand, monitoring the performance of vehicles and the driver’s behavior behind the wheel can reduce claim investigation turn around time. 

Therefore, future insurance products will also focus on moral and financial threats to passengers.

Product Liability

The product liability insurance might shift from automotive to sensors and algorithms behind the autonomous vehicle. The OEMs will be also liable for communication or Internet connection failure along with machinery and software failures.

Insurance Against Existing Infrastructure

It will take more than 30 years for autonomous vehicles to completely dominate transportation. The upcoming insurance products will take existing infrastructure into account. For example, AVs need insurance if it damages due to puddles or potholes on the road.

Also, car ownership tends to decline with rental and pay-as-you-use models. This opens a fleet-level opportunity for insurers for driverless cars.

Source: Accenture X Stevens Institute of Technology “Insuring Autonomous Vehicles” report

Insurers need to adapt to the rapid technological advancements. Cloud-based insurance workflow platforms or IaaS (Insurance as a Service) models help in achieving operational gains in the entire insurance value chain. 

Concluding Remarks

AVs are going to dominate the world’s highway because of improved safety and convenience. Companies can leverage this opportunity to introduce innovative autonomous vehicle insurance products. 

Growing IoT is blurring the fine-line between different verticals of insurance. To stay competitive, insurers should also indulge in creating new distribution channels and partnerships with OEMs and technology service providers.

Cancel

Knowledge thats worth delivered in your inbox

Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot