I’ve spoken often about artificial intelligence’s capability to boost productivity and efficiency. With its ability to automate tasks and speed up processes AI can really change the game in any industry. But AI applications are evolving rapidly, and AI use cases can feel more speculative than empirical. Which is why I was enthused when I read the recent report about how insurance company Alan was using AI in their processes.
If you’re reading this in the US, you’ve probably never heard of the French healthcare and insurance provider, Alan. A significant contributor to the French health-insurance industry, Alan grew from a small startup in Paris to acquire a valuation of $2.9 billion and provide coverage for over 500,000 people across Europe. Their services are primarily designed to provide corporate insurance coverage.
When economies around the world shrunk in 2022, Alan took a big hit to their revenue. In 2023 alone, the company reported losses of around $63 million. When your business is providing corporate coverage and corporations start implementing layoffs… you are going to lose customers. What’s interesting about Alan, however, is not the losses they’re accruing but the way they’re coming out of that hole with the help of AI.
Because Alan is not only an insurance company. They’re also a tech company.
Alan’s healthcare tech provides insurance services that are integrated with various other parallels to work as a one-stop shop for all healthcare needs. Their product is highly integrated and automated. So, at a time when the entire French tech ecosystem is slowing down, and industries across the board are optimizing costs, Alan has been able to use AI to scale up, maintain revenue growth, and keep costs to a minimum. In fact, they expect to see a revenue growth of 40% in 2024, with only a 5% increase in their workforce.
Reading up about Alan’s drive to integrate their processes with technology and the scalability offered by their AI-backed app, has thrown up some interesting insights into how artificial intelligence can be harnessed to create real growth.
Building a Scalable Business Model with AI
By automating routine tasks with AI, such as claims processing through optical character recognition and using an in-house fraud engine, Alan has drastically reduced the time and manpower required for these operations.
This not only speeds up the processing time, leading to faster reimbursements for customers, but also reduces operational costs. Efficient processes mean that a company can handle complex work flows without proportionally increasing its workforce, thus supporting scalability. According to Alan CEO Jean-Charles Samuelian-Werve, with the addition of AI, every employee is now 40% more productive, on average.
Using Data to Power Customer Experience
AI enables Alan to analyze vast amounts of data to make informed decisions quickly. Think about it— any healthcare business will regularly generate a large amount data, like insurance records, medication and supply chain records, patient data, etc. By anonymizing this information and channeling it into machine learning, businesses can safely and ethically train predictive analytics AI models.
With this data-driven approach Alan can optimize product offerings, personalize customer experiences, and identify new market opportunities. In fact, their ability to personalize and customize their product to suit different customers is a big reason why they were able to add 5,000 new names to their client roster in 2023.
AI-powers their customer interactions and tools such as chatbots or automated customer service platforms provide Alan’s customers with immediate and 24/7 support for their queries. This level of responsiveness and accessibility significantly improves the customer experience, building brand loyalty and value. By understanding customer needs and market trends better, Alan can adapt its services to meet these demands more effectively, fostering innovation and maintaining a competitive edge.
Building Your Own AI
With any use of AI comes a significant security component. Because of the large amounts of data being used in AI processes, it’s essential to keep this data (especially consumer data) safe, while also preventing insurance fraud and minimizing risk.
Alan, having invested heavily in the tech side of their business from the get-go, have built their own in-house fraud engine to protect their data and investigate claims more efficiently. This not only protects the company’s financial interests but also ensures that resources are used efficiently, contributing to the overall sustainability and profitability of the business.
A decade or so back, building your own AI just to service a particular aspect of your operations would have been unthinkably expensive. But not anymore. Now, with FTS development models, nearshore talent acquisition, and access to reputable talent management, you can build and maintain an in-house AI team at a fraction of what it would have cost, even a few years back. That’s exactly what we did at PTP, when we built our AI-powered recruiting tool, Gabi.
Better yet, you can export the entire project to an external consultant, who could do the heavy lifting for you, and design the product to your specifications.
Supporting Expansion
Over the last few years, Alan have also expanded their operations to neighboring Spain and Belgium. Like I mentioned earlier, Alan plans to achieve 40% profitability with only a 5% increase in the workforce. And they aim to do this without bringing on new investors.
To quote CEO Samuelian-Werve,
“We don’t need to raise a new round to stay on plan and maintain this growth rate until we reach profitability. At the same time, we’ve received unsolicited offers from investors in the past . . . we’ll continue to look at them, but today that’s not really our strategy.”
This means a large part of the new workflows that an expansion will create will have to be automated, wholly or in part. As Alan expands into these new markets and takes on more customers, the scalability afforded by AI and automation becomes even more critical. These technologies enable the company to maintain high service standards and operational efficiency across different regions without the need for extensive increases in staff or resources.
Conclusion
Alan’s story is not just a testament to the resilience and ingenuity of a modern business navigating through economic downturns but also a beacon for the transformative power of artificial intelligence in the corporate world.
It serves as a clarion call for businesses to embrace AI, not just as a tool for automation, but as a strategic ally in navigating the complexities of modern markets and achieving sustainable growth. In the evolving landscape of global business, Alan’s example is a blueprint for innovation, efficiency, and the intelligent application of artificial intelligence.
