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Is AI Disruption on the way for Kenya’s Insurance Space?

The earliest known reason for introducing insurance protection in Kenya, came during the time of the Colonial British — when they insured their farms and crops against loss, damage etc. Today, Kenya has 70% of the East African Insurance market (among Burundi, Uganda, Tanzania & Rwanda). Still, African Insurance is relatively nascent in terms of size. Only 6 major markets dominate the landscape in a serious way — Egypt, Tunisia, Morocco, South Africa, Nigeria & Kenya. Infact, the number of insurtech startups in the continent altogether is a paltry 50 something. 

The looming political climate coupled with a slowly recovering economy and some fierce competitive tactics used by traditional incumbents places the industry far from ideal in terms of marketplace conditions, including the slowdown in uptake of insurance products by an income-sensitive population.

Yet, Kenya offers a sense of growing appeal for young insurtechs in this region. The market remains largely undisrupted, since insurance penetration is only about 3% (insurance penetration for the African continent is only at 0.3%), attracting large international insurers like Allianz and Swiss Re who have recently entered the market. Kenya, like other countries in the region, has enormous potential similar to South-East Asian economies that also remain largely undisrupted with lower penetration rates.

The positive sentiment surrounding Kenya’s potential for deep tech disruption is not surprising — According to the 2019 Government AI Readiness Index published by the  IDRC and Oxford Insights — Kenya is the most AI ready country in Africa.

Buying Behavior

Insurtech startups are exploring avenues using AI that large, traditional players have less incentive to exploit, such as offering ultra-customized policies, social insurance, and using behavior data from devices to dynamically price premiums.

The Millennial experience is entirely technology driven, while their attitudes and perceptions as consumers will shape the future of how insurance as a service continues to remain relevant.


According to a Kenya Insurance Industry Report, 65% of millennials compare prices across different websites before making a purchase, 68% only buy a product through referrals from friends and social media. Interestingly, 84% of them are opposed to traditional advertising. 

For insurers, loyalty comes at a price — often dictated by the pain point the product/service can eliminate for impatient classes of customers. Analysing buying or browsing behavior can lead to an immense amount of ethically siphoned data. Using ML models and regression algorithms, insurers can create a unified view of their prospect, and realize a multi-targeted approach to create opportunities for upselling or cross-selling.


The report also highlights the importance of making sense of social media behavior — since 41% of millennials use social networking sites to pass on recommendations of products and services to friends and family.

Unlocking market potential requires targeting the uninsured growing middle class in creative ways. In addition to better pricing models, insurtech startups are testing the waters on a host of potential game-changers, such as using deep learning trained artificial intelligence (AI) to handle the tasks of brokers and finding the right mix of policies to complete an individual’s coverage.

Insurtechs are using AI to solve for Kenya’s distribution challenges, by looking at vital consumer needs that have previously been unmet or glossed over. At the same time, there is scope for improving the average consumer’s awareness of artificial intelligence technology, and how they can take advantage of it to solve priority-first issues related to convenience, cost and range of choice.
Nairobi-based Jubilee Insurance, the largest insurer in East Africa is making the most of AI tools like chatbots and automated messaging platforms for streamlining simple customer feedback & support operations. They have also launched forward-thinking products like “Recover in Style” which provides hair and make-up services to Jubilee patients who are hospitalized — services that go beyond the financial needs and into the realm of delivering superior customer experiences.

These efforts highlight a trend pointing towards the growing interest in the use of apps to pull policies into one platform for management and monitoring, creating on-demand insurance for micro-events like borrowing a friend’s car, and the adoption of the peer-to-peer models to create customized coverages. Bluewave, for example, is an insurtech startup offering low-cost insurance products, as low as US$4 a week, aimed at low-resource, low-income users in last-mile environments.

The expanding middle class and growth in mobile phone penetrations will be critical to widening distribution and getting more people to buy micro-insurance sized products for the first time. Badalaa is an on-demand insurtech startup focussed on bringing insurance at the point of transaction where the user needs it. Turaco, a recently funded insurtech, with premiums for as little as US$2 — leverages mobile financial services to provide hospital cashback to customers who have sought treatment at any nationally-accredited hospital in the regions where they operate. These innovations further the consumer’s awareness of AI-enabled insurance coverage and protection in general, in an otherwise underpenetrated marketplace.


Bismart is another example — an insurtech aggregator that allows customers to not only buy the best-in-class insurance products but also make claims directly from their portal as well. 

The biggest learnings for young insurtechs in this space from more mature markets, are about getting the basics right – having a single view of the customer, being able to launch rates and change pricing in real-time, offering customers a multichannel experience without requiring them to fill in the same information over and over again, and settling claims quickly without the need for multiple touchpoints.

Demand-driven models, built on sufficiently large data-sets will be instrumental in driving individual customisation at mass-scale for the sector at large.

webinar: AI for data-driven Insurers

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on April 14, 2020.

We help young insurtechs, build and scale AI-driven products and solutions for last-mile environments. Reach out to us on hello@mantralabsglobal.com, to learn more.

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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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