From exploration to innovation: 4 key stages of AI adoption for insurers

When it comes to AI, insurers no longer need much convincing about the potential value of the technology to their business. What they need is help, partnership, and a path to adoption that recognizes the unique demands of the insurance industry.  

Some of the world’s largest insurance companies are busy innovating on early use cases to help them evaluate the potential impact of generative AI on their operations and businesses. Most other insurers are not far behind.                         

With the recent availability of Microsoft Copilot for Microsoft 365, which integrates the magic of the technology into everyday applications like Microsoft Teams and Excel, insurers understand that their employees and customers alike will welcome generative AI into their operations (if not demand it). Most insurers want to innovate quickly but carefully, deriving maximum value from even the earliest steps while incurring minimal risk to the business. 

Helping insurers unlock business value and deepen customer relationships through technology is what Microsoft Cloud for Financial Services is all about. In our work with early AI adopters, we have identified a set of progressive milestones that can help insurance companies explore generative AI so that its value can be assessed and scaled as quickly and productively as possible.  

4 stages of AI maturity in insurance 

Today, most of the insurance companies we work with are evaluating projects at what we call the “exploring” phase—the early horizon where the technology is deployed on a limited basis. While these use cases tend to focus on internal business scenarios, they are executed with an eye to the far horizon of opportunity, beyond improving operations to being central to new product development and reimagined processes.  

To help insurance manage this long-term approach, we recommend following a four-level maturity model that describes the AI adoption journey from early consideration to innovation at scale. 

1. Exploring: Conducting research and developing plans and demos to learn what AI can and cannot do.  
2. Experimenting: Building a set of limited use cases to determine value and inform next-step planning.  
3. Scaling: Generating an innovation flywheel of more advanced use cases impacting the business at multiple levels. The most innovative insurers are generally here. 
4. Innovating: Integrating AI into core business processes and new offerings, with governance and training to create a new business-as-usual.  

From exploring to experimenting 

In a remarkable way, generative AI can feel almost too appealing. Brainstorming ideas for use cases can produce lists that are quite lengthy. So, it is important to prioritize.  

The north star in this phase is speed to value, which can be achieved by building use cases that are relatively simple to design and easy to deploy. Use design thinking techniques to ideate use cases and map them in a two-by-two “value versus implementation” matrix to find the ones that will deliver higher business value.  

Early use case scenarios are often designed to help employees do their jobs more efficiently. For example, in underwriting, it can take the form of an internal chatbot to answer agent questions or help triage submissions. Claims managers can realize immediate benefits using generative AI to transcribe first notice of loss conversations. In marketing, it can speed the process of developing presentations or drafting new content. 

We work together with insurers by first conducting envisioning workshops, choosing the most strategic options, then building and deploying rapid prototypes. This is where leveraging your technology partner or service provider can reap great benefits.  

 Keys to success in this phase: 

• Begin with three to five use cases.
• Focus on inward-facing scenarios with defined business value.
• Define timelines and success metrics that can be validated.

From experimenting to scaling generative AI 

With the learnings from early efforts in hand, insurers can gain the confidence to move up to more substantive use cases. Business value is the key criterion, and so every candidate should be evaluated on scalability (for example, if it won’t scale, don’t do it). Use cases can also include more than just text-based, with visuals or audio incorporated for richer experiences. Already, we are seeing insurers building on early success to generate an innovation flywheel that generates speed, scale, and learning through experience.  

To move to this next level, the IT landscape needs to be made AI-ready. The most important step is to prepare your data estate by migrating to a modern platform such as Microsoft Fabric, which unifies data and analytics, and has generative AI built in. This positions the company to build custom copilots, chatbots, and other AI enhancements using Microsoft Azure OpenAI Service and other cutting-edge solutions. It also ensures that critical concerns such as privacy, security, and compliance are fully addressed.  

This is also the point to consider the organizational implications of AI, not only identifying how roles will be impacted but also ensuring that frameworks and training are in place to ensure responsible AI over the long term.  

Keys to success in this phase: 

• Don’t stay small—apply learnings to larger efforts tailored to the business’s unique needs.
• Get your data estate ready for AI.
• Set up steering committees to ensure responsible AI and quality assurance frameworks. 

From scaling to long-term innovation  

Early adopters of AI in insurance are already building solutions designed to directly impact their operational efficiencies and, increasingly, the products and services they deliver. It won’t be long before deeper innovation with AI will create significant differentiation among competitors, and that has implications for every organization.  

To enable the greatest competitive advantages with AI, insurers will require a comprehensive cloud foundation that identifies and manages data from many sources, and ensures that AI tools integrate smoothly with existing systems. This is key to enabling AI development to scale as quickly as business requirements demand. You want to be sure that cloud and AI are being provided responsibly, and that it meets the industry’s stringent requirements for data privacy and protection.  

Organizationally, business processes will change to ensure safety and responsibility. We advocate creating committees or offices to define company values for using AI, ensuring guardrails in operations, and managing pipelines of use cases and measurement across the company.  

Finally, you want to ensure that your people are ready. Roles will change over time, and the workforce will accommodate this evolution best with training to build on their skill sets. A great step you can take today is to put AI in their hands now with Microsoft Copilot for Microsoft 365, which integrates powerful capabilities into the productivity tools they use every day.  

Keys to success in this phase: 

• Build a comprehensive AI technology foundation.
• Evolve business processes and deploy AI governance frameworks.
• Engage with and upskill employees to foster adoption and unlock creativity.

Continue on your generative AI journey 

As your organization considers how best to embrace generative AI, we invite you to reach out to your Microsoft representative or technology partner for insights and ideas to move forward with confidence.  

You can learn more about how Microsoft Cloud for Financial Services is helping our customers realize the future of insurance in the era of AI, unlocking business value, and deepening customer relationships.  

The post From exploration to innovation: 4 key stages of AI adoption for insurers appeared first on Microsoft Industry Blogs.

Source: Microsoft Industry Blog