With the technology giants pumping billions of dollars to fund AI projects, it may seem troublesome for startups to find their place in building AI-based business models. However, with the competition now becoming much fierce and the technology is getting more and more simplified, now there is a scope for exploring AI for their benefit. We can see how artificial intelligence, machine learning, and the Internet of Things are now making many fundamental shifts in various sectors. These pave the way to many unconventional sectors, too, with endless possibilities. All these create a perfect playground for all the smart-thinking startups to carve their niche to compete with the big players on the move.
This article will explore some real-time use cases of startups coming up with successful business models in AI. However, artificial intelligence is just a small part of these business models, bringing forth a more successful product and services management strategy. Let’s explore some key takeaways of startups with AI.
Successful AI-based product strategy – Lemonade
Lemonade is a bright startup focusing on the insurtech sector, which released its initial public offering with a total worth of $1.7 billion. The company offers an online platform that addresses many longstanding challenges in the home insurance sector. Lately, Lemonade has built its business through some smart designs and a winning marketing strategy. The artificial intelligence component is now built on top of this basement.
Lemonade’s mobile app and portal are very user-friendly. The process of filings insurance claims and buying insurance policies can be done with digital assistance for decision-making, making it much easier and friendlier than the traditional insurance process. As one of the first innovators in the insurtech sector, Lemonade had quickly gained the edge over other companies. It was also able to quickly snatch many users who were tired of the existing insurance models.
The business models of Lemonade are also very interesting and rewarding. The company takes its fee from the premium payments, which means it has not profited from denying claims. Any unclaimed money goes into the charity funds as the users choose. The company also has a policy that it will not invest the premium into any heavy-polluting industries or anything which causes harm to nature and mankind. So, Lemonade maintains itself as a value-based business in an industry that is historically reviled by many. The mission of Lemonade is to transform the concept of insurance from the necessary evil to social good. For machine learning and AI database-related processes, you can avail the assistance of RemoteDBA.com.
Ultimately, Insurance depends on data, whereas the established insurance providers keep a century of relevant data to develop their risk models and custom-build their insurance policies. Unlike what the others historically do, Lemonade does not keep any traditional agencies’ data and doesn’t have any baggage from the old policies. Lemonade created an entire technology stack from the basement to cater to the needs of the AI.
Each experience over Lemonade is digitized, and the company tends to collect a lot of data through every interaction with the customers. There are many nontraditional data points also involved, which the other insurance agencies do not pursue. This unconventional model enables Lemonade to unique machine learning algorithms that can predict the insurance risk with optimum accuracy and help personalize and automate the previously impossible opportunities.
As of late, Lemonade has two chatbots as ‘Maya’ to help the customers create their custom insurance portfolio in a few clicks. ‘Jim’ bot handles their claim processes. Almost one-third of all the claims are being handled by AI and automatically process and pay the claim in a few minutes. A small percentage of the claims are being escalated to human agents. However, the percentage of bot-handled claims is increasing day by day as the accuracy is getting enhanced.
AI for hardware enhancement – Butterfly Network
Butterfly Network is another startup that is about to be listed at the NYSE after its $1.5 billion acquisition merger with Longview Capital. The product offered by them is Butterfly iQ, a single-probe ultrasound device for body ultrasound. This can connect to a smartphone and run as a mobile app. This simple device costs about $2,000, which is much cheaper than the ultrasound sets that are now being utilized at hospitals, about 10 to 20 times pricier. Butterfly Network aims to build high-end ultrasound machines to be made available to the communities who cannot afford the cost involved in ultrasound using the high-end devices. Butterfly IQ can also make scanning more portable and take it to places where the bulkier ultrasound systems cannot.
Butterfly iQ works on artificial intelligence algorithms, which can help create use cases that are not available in traditional ultrasound devices. For example, an advanced AI feature of this device is a slider feature in the app, showing the quality of the image. As the probe is moved on the body, the slider will shift to ensure whether the device gets a good capture. It also uses a king of artificial neural networks, which is thoroughly trained over hundreds of thousands of images, making it capable of discriminating between bad and good imaging. For example, the initial-level responders of the primary case staff do not have to reach bigger ultrasound machines. Still, they can use these small devices to do proper imaging of the patient’s body and decide the need for further analysis.
Now, Butterfly iQ and the smartphone app come with various cloud storage options and sharing interfaces, which also facilitate data in the broader healthcare context. Butterfly Network also works on adding new ML-powered features to help with the analysis and measurements. Here, AI plays a crucial part in the overall process. A small, portable device for whole-body ultrasound was never possible before, which certainly creates value for the untapped segments of the market. AI offers an added value that helps to improve the software stack and helps to build on top of the device hardware. Given that device uses smartphone for communication, it also can help add more and more AI features and improve the product’s performance consistently over time.
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No matter their challenges, successful startups tend to address an overlooked or poorly addressed challenge to develop a solid product or service strategy. With minimum market penetration, they create and establish a business model that will steer their product in the right direction. Machine learning and AI now lead this revolution and help startups gain a significant advantage over their competitors.
