Tag: artificial intelligence

AI projects in Insurance are Moving from Pilots to Business Programs | Sherpas in Blue Shirts

Insurers are rethinking their business ethos to become protectors instead of payers. The insurer of the future is aiming to develop a customer-centric value proposition. Carriers are looking at developing innovative products that are contextualized to meet evolving customer needs. And the insurance distribution strategy is shifting to adapt to new product offerings, client needs, and digital technology-led disruption in the ecosystem.

Not surprisingly, insurers are adopting AI and related technologies to drive these capabilities. According to our just released Insurance IT Services – Annual Report, the top three business objectives insurers are trying to achieve with AI projects are customer experience, process optimization, and product innovation.

AI Ins BlogAI Trends in the Insurance Industry

Our annual report studied 80 unique AI initiatives by global insurers to unearth AI trends in the insurance industry. Here are the top ones we identified.

Capabilities

Approximately 53 percent of insurers are developing in-house capabilities for their AI initiatives. But many have large skills gaps that will inhibit their ability to scale pilot projects and realize the expected value from AI initiatives.

Embedded intelligence

Insurers have accelerated their focus on embedding intelligence across the value chain, with higher adoption of AI for sales & distribution and underwriting processes.

Self-service

Insurers are adopting intelligent self-service AI tools to enhance the customer experience.

Mid- and back-office process value

The value delivered through front-office AI initiatives such as chatbots is limited. But real value can be unlocked when AI is applied to optimize mid- and back-office processes such as agent support and claims management.

Data

While structured enterprise data remains the major source of data for insurers (52 percent, per our research), the connected ecosystem – i.e., data from IoT-based devices – is gradually gaining traction, at approximately 35 percent. As insurers evolve in their AI journey, deploying AI and machine learning (ML) to leverage unstructured data from third-party sources and connected ecosystems is likely to increase. But as of today, enterprise data silos, legacy systems, and lack of interoperability standards to tap into the connected ecosystem and third-party data are slowing down insurers’ AI initiatives.

Some Standout Examples

Many insurers have made progress in deploying AI and ML to their data and are starting to see quantifiable results. For example:

  • Zurich Insurance deployed AI in its personal injury claims process. The company claims that AI has helped it save 40,000 work hours, and reduced claim processing time from 58 minutes to five seconds per medical report
  • ICICI Lombard launched a chatbot called MyRA to underwrite two-wheeler, fire, and burglary insurance for SMEs. Since its launch, MyRA has been engaged in 65,000 customer interactions, and has sold more than 750 policies without any human intervention.

AI has the potential to deliver significant value to insurers and their customers. To learn more about how it can impact your business, our recent Insurance IT Services – Annual Report is packed with data and our take-away insights from 80 unique insurance firm AI projects. In it, we outline how AI implementation is impacting the insurance industry, and present various AI use cases across the insurance value chain.

Please write to Ronak and Priyanka to discuss how you’re adopting AI in your insurance business processes.

AI as-a-service: Big Tech Has Provided Platforms, But Where Will the Apps Come From? | Sherpas in Blue Shirts

Our digital services research suggests that 40 percent of enterprises have adopted AI in some shape or form. Of course, they’re relying on the foundational platforms from BigTech firms like Amazon, Google, Microsoft, and TenCent – and even from smaller tech start-ups –to drive meaningful business cases.

But while they can leverage Amazon Sage Maker or Microsoft Bot Framework to do the heavy lifting, they still need a meaningful application that operates on the platform in order to solve their business problems.

Enterprise Challenges with AI

Granted, tech vendors like Oracle, Salesforce, and SAP have made initial progress in integrating AI into their application platforms. But their products are very broad and focus on their own planned areas. And enterprises have multiple, complex requirements that fall outside the purview of these generic applications. Therefore, most enterprises must also build their own AI engines to get meaningful insights from these large-scale applications.

Essentially left on their own, enterprises have to build their own applications to address their needs. But Everest Group digital services research indicates that 60 percent of leading digital adopters struggle for the right talent. And because they lack high-caliber AI talent, they can only take scratch some of the surfaces necessary to create truly valuable apps that can deliver specific business outcomes.

Can Start-ups Help?

We believe this leaves the market wide open to an impending burst of start-ups that can build AI-led niche applications to solve industry-specific business problems. Areas like fraud detection in insurance, compliance management in financial services, and industry-oriented employee engagement and customer experience can significantly benefit from these types of applications. But the key to success here – for both enterprises and these start-ups themselves – will be a focus on building applications for specific business use cases, rather than broad-based platforms. Indeed, AI applications focused start-ups need to commoditize the platform and focus squarely on the application logic that leverages AI.

Enterprises will need to partner or invest in these start-ups to incentivize suitable AI-led applications. Going forward these enterprises should focus to procure off the shelf applications to drive business outcomes than over investing in AI platforms. Unlike today, which requires massive bandwidth to build on top of BigTech AI platforms, these applications will be easy to configure, train, and consume.

The Role of System Integrators

Given that system integrators (SIs) have a strong enterprise DNA and understand business processes, systems, and technologies very well, they can build these applications for enterprises leveraging a BigTech platform. Some of them have made early inroads in areas such as service desk, customer support, and IT operations. However, there is a massive opportunity for business applications and processes. SIs will need to develop point as well as platform-led AI applications that can be plug-and-play in an enterprise set-up. These applications must be pre-trained on industry-fed data for quick deployment and better time to value.

The Road Ahead

It is apparent that enterprises cannot leverage the power of AI on their own. They need to rely not only on large technology vendors, but start-ups and their service partners as well. Though each enterprise must have a pool of valued AI resources, they should not go overboard in investing in them. As AI is not enterprises’ core business, they’re better off letting it be done by companies that are experts.

However, if the AI industry continues to generate next-generation smarter platforms that are do heavy lifting for AI without creating meaningful applications, we will surely see one more AI winter in the near horizon.

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