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5 InsurTech Trends for 2023

3 minutes read

For 2023, we believe that InsurTech will be used to supplement the rising concerns of inflation, arrested economic development, and heavily burdened pension schemes by catering to customers with greater attention to detail. 

# Digitally Enabled CX 

Insurance models in the present context have become bloated and complicated to the point where customers feel alienated. Customer needs are also converging across a wide range of areas: health, retirement, and investment management, to name a few. Simplifying the existing delivery model is key, and one such model that is likely to emerge is that of being a ‘distribution specialist’.

These firms are predominantly client-centric and extremely capital-light as they do not take on balance sheet risks. These firms will invest heavily in client-facing technology, and those that curate a delectable insurance discovery and delivery experience will have a huge leg-up over their peers. These developments are in line with Gartner’s predictions for the InsurTech industry, where digitally enabled CX is listed as a key success factor for InsurTech in the coming years.

# InsurTech native Telematics

Analysts and experts alike have been citing usage-based insurance programs as the next big thing in the world of insurance for nearly two years now. But how effective can usage-based programs be if they rely entirely on the customer to predict their decisions and make purchases accordingly? 

This is where telematics systems come in. As cars become increasingly ‘smart’, it will become easier and cheaper to integrate telematics into the insurance plan to implement a real-time ‘pay as you go’ plan. Telematics will be crucial for developing markets in Asia as societies become increasingly digitized and people start to get comfortable with the idea of insuring themselves and their vehicles separately. 

# Algorithmic Risk Assessments

Research has shown that with the application of machine learning models to the risk assessment strategies employed by risk analysts, Insurance companies can decrease the time taken to evaluate customer profiles by allowing faster servicing and thereby leading to greater customer loyalty and satisfaction. This will allow companies to process claims swiftly and accurately, thereby allowing risk assessment professionals to focus on refining their models.

Some firms have already demonstrated success by incorporating AI into their workflows. Lemonade, an insurance company that is ‘digital first’ has seen massive success by using AI to facilitate claims, quotes, and personalizing prices and interactions with individual customers.

# Broadening capabilities in the Metaverse

With over $25Bn dollars having been invested into it by Facebook alone, Metaverse is here to stay for the long run. And for Insurers, the possibilities offered by metaverse are hard to ignore. This means they finally have a tool to combine the efficiencies of AI-powered chatbots, with the warmth of face-to-face interactions. Internal training, conducting sales pitches, and using NFTs to verify personal documents are some of the most highly anticipated use cases.

Max Life insurance, a leading Indian insurance player has already started to think about how best to use the metaverse to boost employee engagement and morale.

# Disruptors will strive to stay afloat

Much of what made new-age insurers attractive to customers was the way they structured themselves (tech-first, expedited claims, etc.) that were antithetical to running an insurance business at scale. Kimberly Harris-Ferrante of Gartner predicts that the coming year will see a lot of new Insurtechs pivot to more traditional operating models, with the successful ones being acquired and the others being forced to shut shop.

Some have already closed down, such as GoBear (Asia Pacific) citing increasing regulatory and compliance pressures as the primary reason. Other examples include Kinsu (from Latin America) and Coverly for small businesses.

Conclusion: 

2023 is likely to see the beginning of the final stretch of digital transformation in the insurance industry as many have already caught on to the basics that are required to run a robust digitally-enabled sales and servicing operation. Conservatism will go hand-in-hand with novel, disruptive technologies as incumbents will lap up all existing software capabilities to bolster direct distribution, simpler delivery mechanisms, and a narrower focus on servicing the customer. Expect greater use of APIs, hybrid cloud architectures, and ‘headless tech’ in the coming year.

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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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