Karandeep Singh

Karandeep Singh

Director, Artificial Intelligence & Machine Learning

As generative artificial intelligence (AI) continues to rapidly evolve, it has sparked transformative change across the insurance industry. In recent years, the traditional insurance model of manual decision-making had already shifted to an increasingly data-driven ecosystem. However, the latest advancement in AI, which seamlessly integrates large language models (LLMs), has delivered the capability for AI models to engage in human-like conversations, demonstrating a significant leap in sophistication. For insurers, AI-based decision-making has quickly evolved from asking, “Should we do it?” to “How do we do it responsibly?”

According to CGI’s 2023 Voice of Our Clients (VOC) research, AI is the top cited innovation investment over the next three years for executives in both the property and casualty and life and pension insurance sectors. It’s clear that responsibly implementing AI has become a strategic imperative that offers a competitive edge and fosters innovation in operational processes, product development, and service delivery.

Three ways AI and generative AI are impacting the insurance value chain

AI and its associated technologies have shattered the traditional boundaries of the insurance value chain, revolutionizing the industry in three key ways:

  1. Swifter underwriting: A traditionally time-consuming and manual activity, underwriting has undergone outstanding improvements with the implementation of AI. Machine learning algorithms analyze vast datasets to assess risk factors, enabling swifter and more accurate underwriting decisions. Generative AI is now able to act as a decision support tool for underwriters, offering insights into risk factors by aggregating and sharing a wealth of valuable knowledge scattered across various data sources.
  2. Intelligent customer interactions: Conversational AI chatbots and virtual agents combined with generative AI capabilities have transformed how insurers interact with customers. These intelligent agents can efficiently handle customer queries, provide personalized policy recommendations, and enhance customer engagement. It seems likely that by 2024, most websites will have a generative AI chatbot to assist customers with queries and complete routine tasks on behalf of the agents.
  3. Product augmentation: Data and AI have transformed the insurance product landscape, enabling products like usage-based insurance, spot insurance, and embedded insurance. With generative AI, new product ideas can now be created, tested, marketed, and successfully launched within weeks. 

Two keys to strategic success - top-down leadership and bottom-up expertise

Our 2023 VOC research revealed that across sectors, extended digital strategies are needed. Although 97% of executives have digital strategies in place, in the property and casualty sector, only 35% include their entire ecosystems. In the life and pension sector, that number drops to just 22%.

While the potential benefits of data-driven insurance powered by AI are vast, executives are beginning to understand that successfully implementing and sustaining such benefits require a holistic approach, involving both top-down leadership and bottom-up expertise.

With top-down leadership, the first critical step is developing a clear vision for leveraging AI and related technologies. This vision should identify the strategic opportunities that AI offers while involving and engaging stakeholders from the outset to ensure alignment and support across the organization.

Bottom-up expertise involves analyzing the organization's current data, technology stack, and the AI knowledge and experience that exists. Conducting a technical feasibility study using a small proof-of-value can help assess whether the leadership vision can be translated into reality and will inform the practicality of the AI strategy.

Implementing data-driven insurance and AI impacts the entire insurance ecosystem, including employees, partners, and customers. If it is applied from a narrow technological perspective only, the transformational potential of AI will likely be overlooked.

Best practices for navigating data-driven insurance

When building a data-driven insurance model powered by AI, success depends on embracing a number of best practices, including the following:

  • Create a vision: Establish a clear vision for leveraging AI and related technologies. Engage stakeholders early to ensure alignment and support.
  • Conduct a feasibility study: Assess the practicality of your organization’s vision by evaluating existing data, technology infrastructure, and AI expertise within your organization.
  • Develop an AI portfolio: Prioritize AI initiatives based on their potential benefits, complexity, and scalability, aligning them with your strategic vision.
  • Focus on quick wins: Start with small, successful AI projects to build confidence and gradually scale up deployments.
  • Establish a data strategy: Explore external data sources while navigating privacy and legal considerations. Combine external and internal data for comprehensive AI insights.
  • Follow an IT strategy: Define IT capabilities and stay vigilant in monitoring data, AI, and data science markets for informed make-or-buy decisions.
  • Manage your data: Establish data pipelines for preprocessing and maintaining data acquired for AI use.
  • Build an AI architecture: Choose a scalable, secure architecture balancing tool and algorithmic standards with flexibility to handle large data volumes.
  • Decide whether to make-or-buy: Weigh the competitive advantages of internal AI development against collaborating with a consulting firm for best practices-based implementation.
  • Attract and develop AI talent: Invest in recruiting specialists, as well as training for internal staff. Consider a citizen data science approach to enable domain specialists to adopt a low-code no-code development strategy.
  • Strengthen your organizational model: Create a core function dedicated to building AI competencies, ensuring close collaboration with business departments.
  • Encourage a cultural shift: Foster an ethical AI culture through effective vision communication, AI ambassadors, and corporate events.

Explore how data-driven insurance and AI can transform your business

By transitioning to data-driven insurance, insurance companies can compete effectively with both established and non-industry insurance providers through process, product, and service innovation. To discover additional best practices that will lay a foundation for successful AI implementation and to gain practical guidance on how to succeed in each of these areas, download our white paper, Data-driven insurance: A path to strategic advantage.

Contact me to learn more about CGI’s work in helping insurers pursue data-driven insurance and generative AI to create strategic advantage in the face of new disruptors. Together we can explore how CGI’s unique “12 Weeks to AI” offering applies best practices to ensure our clients' success, no matter their AI maturity level.

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About this author

Karandeep Singh

Karandeep Singh

Director, Artificial Intelligence & Machine Learning

Karandeep is an artificial intelligence (AI) expert and leads the emerging technologies team at CGI. In addition to being a trusted advisor to CGI clients for AI strategy, data engineering, machine learning implementation at scale, data governance and privacy, Karandeep manages a talented and diverse ...