Generative AI Use Case Library for the Insurance Industry

Identify value-driven generative AI use cases to transform your organization.

Analyst Perspective

Artificial intelligence (AI) is disrupting every industry, but you can get ahead of the shift.

In light of the growing competition in the insurance industry, it's imperative for both established companies and new digital startups to provide exceptional customer experiences. Generative AI (Gen AI) is one key to achieving this.

By strategically implementing Gen AI, insurers can better satisfy customers' increasing demands, discover new areas of opportunity, and develop innovative products and services, all while building a loyal customer base and fostering trust.

By integrating Gen AI into business processes, insurance carriers can streamline operations, mitigate risks, and effectively identify fraudulent activities.

At Info-Tech, our strategic foresight approach is essential for analyzing information. It is imperative for both the IT department and the business to understand what is happening outside of the organization. This understanding can guide new ideas and opportunities, ultimately leading to success.

Gen AI is a powerful tool that insurers must adopt early on to gain an advantage over their competition. Failure to do so will result in falling behind and potentially losing valuable market share.

Ahmed Mapara, MBA
Principal Research Director, Industry Practice
Info-Tech Research Group

Executive Summary

Your Challenge

• You are falling behind in AI technology adoption. Next-generation customer service is defined by new technologies such as predictive analysis modeling, AI, machine learning (ML), and Gen AI functionalities. Leaders in this space will develop a competitive advantage.
• You need a strategy for AI deployment. Your customers want new products, services, and experiences to meet their evolving needs. Gen AI can boost your company's stature if adopted and implemented correctly.
• Larger firms have the scale to invest in advanced technologies. Asset size drives revenues, which influences IT budgets. Smaller firms may work with vendors to fulfill their customized needs.

Common Obstacles

• IT is not always aligned with business innovation. The business increasingly requires advanced technologies to identify new opportunities.
• Business—IT alignment requires more agile techniques. You must become an active partner with the business to understand client needs, create innovative products and services to stay competitive,, and be ready to take advantage of new opportunities.
• New skilled workers are needed to implement new technologies. Data scientists, AI-specialists, client/user experience experts, and process designers are some of the new roles required to transform insurance products and services. Talent acquisition, upskilling, cross-training, and retention are essential to succeed.

Info-Tech's Approach

• You must evolve and mature your core IT capabilities to meet the new demands from the business. Put governance in place to ensure that IT understands business priorities.
• Align your organizational and AI strategy. CIOs need to understand the growing needs of the business in this new environment. A unified approach will serve this purpose well.
• Develop your policies after establishing responsible AI principles. Accountability, transparency, data privacy and security, IT governance, and fairness and bias detection of models are at the forefront of your policies and building a roadmap for Gen AI.

Info-Tech Insight

To gain a competitive edge, insurance companies should use advanced technologies such as Gen AI and ML. These tools can help them better understand their customers, stay ahead of competitors, and quickly innovate new products.

Gen AI is an innovation in ML

Gen AI

A form of ML, whereby, in response to prompts, a Gen AI platform can generate new outputs based on the data it has been trained on. Depending on its foundational model, a Gen AI platform will provide different modalities and thereby use case applications.

Audio – Converts text to sound

Visual – Enables text to image, video, or web design conversions

Code – Creates code in various programming languages based on human language prompts

Text – Creates text-based outputs such as articles, blog posts, emails, and information summaries

ML

An approach to implementing AI, whereby the AI system is instructed to search for patterns in a dataset and then make predictions based on that set. In this way, the system "learns" to provide accurate content over time (think of Google's search recommendations).

AI

A field of computer science that focuses on building systems to imitate human behavior. Not all AI systems have learning behavior; many systems operate on pre-set rules, such as customer service chatbots.

Other industries are using AI – and financial services is not the clear leader

Source: Statista, 2023

AI is poised to revolutionize insurance operations because it can enhance various areas such as risk assessment, claims processing, asset management, and loss prevention.

"Artificial intelligence (AI) in the insurance market was valued at USD 3.64 billion in 2022 and is expected to reach the value of USD 35.77 billion by 2030, at a CAGR of 33.06% during the forecast period."
Source: Data Bridge Market Research, 2023