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The 5 hidden problems for Insurtech

For Insurance giants, the marketplace is changing. For young insurtechs trying to displace these giants and keen on disrupting the landscape altogether; the next big market is becoming plain and obvious: Millenials and the generations that will follow them.

A new wave of AI-driven technologies is making subtle changes to the way young people are re-thinking the whole “Why do I need insurance again?” decision.

Millennials —  are most likely to purchase insurance through an app with a few taps on their smartphones — are driving less frequently than previous generations — thereby creating a market for lower cost, pay-per-mile auto insurance. 

Yet, despite the proclivity of this demographic to stay away from ownership (and, with that, the need for coverage), they do own assets that they want insured. Insurtech is well poised above all else, to satisfy their unique coverage needs.

A majority of the World’s insurance purchases are done physically (in-person), while only a small portion of sales comes from either the web or mobile – yes, even in 2019 and for the foreseeable future, that remains true.

The Hidden Problems of Insurtech


The ‘Insurtech’ model can be broken down into — those that operate at the broker-level, those that offer insurance services/products or product-level, and those that have a hybrid approach (such as peer-to-peer insurtech) that has an insurance product with a strongly linked brokerage aspect to it. Here is a look at the challenges that surround young companies operating in these models.

#1 Partnerships are stark & sparse


For existing incumbents, the advantage is obvious — seize on the hype created by insurtech upstarts, who are capturing previously untapped audiences towards new & innovative products. 

Also, read – Top Innovative Insurance Products of 2019

Large insurers will even venture into setting up their own start-ups; or invest in new technologies within their own business.  However, despite the mutual benefit-for-all reasoning behind partnerships, these are spread thin across most regions.

Without the support of a large insurer or two, insurtechs will find it hard to manage the unit economics of the policies they sell; which brings to question the sustainability of this model for scaling.

#2 Innovation beyond downstream distribution


Insurtechs that have either chosen not to partner/ not managed to attract the right partnership with large insurers — arguably face greater challenges. Most of the insurtech-startup funding pool has moved into distribution, and rightfully so.

Distribution has brought about long-awaited changes to delivering new products and customer experiences — aspects of the business that Insurance giants consistently struggle to produce in.

Insurance, however, has four fundamental units: the underwriting of insurance, claims servicing, regulatory overhead, and distribution (actual selling).

As these insurtechs grow, the looming question remains: how will they manage the other parts of insurance, if all the money has gone into refining one stream?. For example, are they sufficiently capable of handling claims and underwriting as the business scales? These questions are yet to be answered, and the models are yet to be proven.

#3 Frequent changes to the legal & regulatory framework


“Not all insurtech businesses qualify as insurance companies” since they depend on the type and extent of the services provided. A regulatory distinction is essential to separate them — without which a reliable guarantee cannot be given to customers in the event of a loss.

Legal and regulatory commitments change with region and country, hence insurtechs are typically unsuitable for covering potentially large losses. 

#4 Attitudes of the next generation


Younger generations are less likely than previous ones to pay heed to the importance of insurance. They simply do not see it as an important financial instrument. These challenges have plagued the industry for several decades, and insurtechs will have to assume this challenge for themselves as well. At its core, insurance is a hard product to sell, no matter how good the package looks.

Technology in insurance and advancements to customer experiences are making the furthest inroads, the industry has ever seen. Yet, low insurance penetration levels are still an indicator of how difficult it is for insurtechs to find adoption among the masses.

#5 Intelligent Customer-Experiences


Thanks to Big Tech (like Google, Amazon, Apple, etc.) — customer experience has evolved rapidly. Digital products and services are now highly customisable and can be delivered at a high quality consistently. Yet, it has taken until now for the same to slowly seep into insurance. Sensing a huge opportunity, Big Tech has started moving into the insurance on-demand space, which has forced the larger insurers to adapt quickly. 

Insurtechs, who are by-default product- and tech- first, tend to fare better than their much larger counterparts. Yet challenges with data will persist. Just how well insurtechs are using data, remains to be seen. 

Will technology in insurance have to face a test of time?

The use of exceptional data and advanced analytics can help link the behavioural characteristics of customers and their spending habits – true fodder for machine learning models. How will insurtechs leverage useful insights to tackle age-old insurance selling challenges, such as intention to abandon, the propensity to purchase, or the right communication channel — will be the true test of competitive advantage.

Mantra Labs is a deep-tech advisor & consultant for young Insurtechs helping them create a strategic vision and an agile evolution road-map that addresses challenges from scaling to delivery. To learn more, reach out to us at hello@mantralabsglobal.com.

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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.

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