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AI Integration vs Legacy Systems in North American Life Insurance

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Role: Independent Consultant
Industry: Life Insurance North America

Situation: The client is trying to use AI to improve their customer outreach and internal decision making. They are burdened by legacy systems, which is exacerbated by their strategy of growth through acquisition. The growth strategy also makes it difficult to deploy capital effectively across business lines and geographies. The organization is very collaborative and they tend to avoid major political battles. However, they also have a tendency to avoid addressing sticky problems, allowing them to fester without resolution. As a result, they tend not to take risks in changing their organization structure, product lines, or technology infrastructure.

Question to Marcus:

How would you prioritize the different issues that they face? Should cleanup of the data come first, or should they pick some specific applications and clean up the data as needed?

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Based on your specific organizational details captured above, Marcus recommends the following areas for evaluation (in roughly decreasing priority). If you need any further clarification or details on the specific frameworks and concepts described below, please contact us: support@flevy.com.

Data Monetization

A successful Data Monetization strategy can transform Life Insurance North America's approach to customer outreach and decision-making. To capitalize on AI, the company must first ensure that their data is clean, reliable, and integrated.

Given the legacy system issues and the growth through acquisitions, a central data repository should be established, where data across different systems and acquisitions can be standardized and made AI-ready. Only with accurate and comprehensive data can AI tools effectively identify patterns and insights that lead to better Customer Segmentation, personalized marketing, and risk assessment. The company should also explore data as a product or service, offering insights to third parties or partners, while strictly adhering to privacy regulations. An effective monetization strategy will require close collaboration with IT and Data Science teams to identify marketable data assets and deploy them in a way that complements the core life insurance business.

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Change Management

Addressing the tendency to avoid sticky problems, Life Insurance North America must embrace a comprehensive Change Management process. This involves preparing the organization for change, managing the transition, and ensuring the change is sustained over time.

The use of AI to improve customer outreach and decision-making will require significant changes in both processes and culture. As the organization is collaborative, leveraging this strength to foster a change-friendly environment will be critical. Engage with all levels of the organization to communicate the benefits of AI and data-driven decision-making, and to demystify the technology involved. By creating a flexible change management approach that is sensitive to the company's collaborative culture, the organization can implement new technologies and processes without causing major Disruptions.

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Digital Transformation

The push towards Digital Transformation in Life Insurance North America is essential to remain competitive. Given the collaborative nature of the organization, a phased approach to digital transformation could be most effective.

Start with customer-facing applications that can deliver quick wins and tangible benefits to both the organization and its customers. These early successes can build momentum and support for tackling more complex systems. As for the legacy systems, a careful assessment should be made to determine whether it is more cost-effective to upgrade existing systems or replace them with new, more Agile technology. The digital transformation journey should be aligned with the company's Growth Strategy, ensuring that any new technology can scale with future acquisitions and growth.

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Strategy Development

For Life Insurance North America, Strategy Development should focus on creating a coherent plan that aligns with the company's growth-through-acquisition model and the integration of AI. This involves defining clear objectives for technology and data integration across all acquired entities, ensuring that each acquisition is strategically leveraged to enhance the company's competitive position.

The strategy should detail the steps needed to break down silos between business lines and geographies, and to foster a unified approach to Data Management and Customer Service. Additionally, the company must strategize to close the gap between its collaborative culture and its aversion to addressing difficult issues, as this misalignment could impede effective Strategy Execution.

Learn more about Customer Service Strategy Development Strategy Execution Data Management

Business Transformation

Life Insurance North America's journey towards AI adoption is not merely a technological upgrade, but a Business Transformation. This transformation should be conceived as an enterprise-wide initiative that revises how the business operates, how it engages with customers, and how it makes decisions.

Given the burden of legacy systems, an incremental transformation strategy might be more effective, where non-disruptive AI applications are introduced to enhance customer outreach and decision-making processes. This step-by-step approach can facilitate a smooth transition, allowing the organization to adapt without triggering resistance. The transformation should also include training programs to develop the workforce's digital literacy and AI readiness, to support the company's long-term transformation goals.

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Business Case Development

Developing a comprehensive Business Case for the adoption of AI in Life Insurance North America will be vital to secure investment and executive support. The business case should articulate the Value Proposition of AI, including improved Customer Experience, operational efficiencies, and enhanced Risk Management.

It should offer a detailed analysis of the cost-benefit scenario, taking into account the challenges posed by legacy systems and the need for data cleanup. The business case should also consider the potential return on investment from improved customer targeting and retention through AI-enabled insights. Ensure that the business case speaks to the strategic goals of the company and demonstrates a clear path to achieving those goals through the proposed AI initiatives.

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Financial Modeling

An Integrated Financial Model will be instrumental for Life Insurance North America to manage and deploy capital effectively across business lines and geographies. The model should provide insights into the financial implications of maintaining and upgrading legacy systems versus investing in new AI technologies.

It must account for the costs associated with data cleanup and integration, as well as potential revenue generated from improved customer outreach and retention. Furthermore, the model should be flexible enough to simulate various growth scenarios and acquisitions, thus aiding in strategic decision-making. The financial model will serve as a critical tool in balancing the need for technological advancement with prudent capital allocation.

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Operational Excellence

Integrating AI into Life Insurance North America's operations aims to achieve Operational Excellence. The company must streamline processes to make them more efficient, reliable, and adaptable to changes such as the introduction of AI.

Since the company's growth strategy involves acquisitions, it's essential to standardize and integrate operations to avoid inefficiencies and duplication of efforts. Establishing a Center of Excellence for AI can provide the expertise needed to drive consistency and quality in AI initiatives across the company. This center would also ensure that Best Practices are shared and that the AI strategy is aligned with the overall operational goals.

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Risk Management

In the insurance industry, risk management is paramount. For Life Insurance North America, the use of AI can significantly enhance risk assessment and pricing strategies.

However, it's crucial to manage the risks associated with implementing AI, particularly around data security, model governance, and compliance with regulations such as GDPR or CCPA. A comprehensive risk management strategy should be put in place to address these issues, including regular reviews of data usage policies, model validation processes, and adherence to ethical AI frameworks. Additionally, the strategy should consider the risks associated with the growth-through-acquisition model, such as data integration and maintaining compliance across different jurisdictions.

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Digital Transformation Strategy

To address the burden of legacy systems, a strategic approach to digital transformation is required. Life Insurance North America should develop a Digital Transformation Strategy that prioritizes customer-facing technologies that can drive quick wins and deliver value.

The strategy should include a roadmap for integrating AI into existing processes, outlining the resources required and the expected outcomes. Focus should be placed on building a scalable and flexible IT infrastructure that can support ongoing growth and the seamless integration of new acquisitions. The digital transformation strategy should not only focus on technology but also on the cultural change necessary to support a more agile and data-driven organization.

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