Industry
Insurance
Reinventing the Insurance Industry with a Digital-first Strategy
Highlights
- Insurers operating with legacy retirement models struggle with challenges such as poor data quality, complex plan structures, and siloed operations, underscoring the urgent need for modernisation in an expanding market.
- An AI-enabled operating model integrates intelligent data extraction, unified rule engines, automated workflows, and structured exception handling to reduce operational friction and elevate governance.
- Insurers must execute a phased transformation, beginning with process standardisation before automating data intake, deploying rules-as-code, and scaling operations under a unified digital workflow.
- Insurers must act now by investing in strong data foundations, process efficiency, and robust controls to meet future demands and secure a competitive advantage in the growing retirement and PRT market.
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Why is AI now essential for the retirement function?
The retirement function is a critical part of the wealth management ecosystem. It supports participants who rely on their retirement benefits for long-term financial stability and security. Retirement service providers have complex operational requirements ranging from enrolling participants, calculating benefits, managing contributions, regulatory compliance, customer support, and disbursements. Accurate and reliable retirement operations are imperative for retaining participants’ trust.
Pension risk transfer (PRT) is a mechanism through which sponsors (employers) shift the burden and financial risk of managing defined-benefit pension plans to insurance firms, in turn reducing the impact on their balance sheets. For insurance firms, this is an opportunity to access large premium inflows, which can help create long-term portfolios and expand business.
However, the existing retirement models of insurers are riddled with difficulties: poor quality data, complexity in retirement plans, siloed operations, and heavy reliance on manual processes. In our view, insurers must shift to AI-enabled retirement models to make better decisions and maintain robust governance. Such models can help insurers improve operational efficiency, enhance participant experience, strengthen risk management, and build a more efficient and scalable retirement function. Furthermore, the growing size of the retirement market—total annuity sales in the US reached a record high of $464.1 billion in 2025 with momentum set to remain strong in 20261—underscores the urgent need for AI-driven modernisation of insurers’ retirement model.
What are the key challenges in legacy retirement models?
Several challenges plague the retirement function in insurance firms.
Poor data quality: Successful retirement and PRT operations depend heavily on the quality of data (participant, plan, payroll, and benefit data). However, insurance firms often receive data from multiple sources and through external stakeholders, resulting in inconsistencies, duplicate records, missing information and multiple formats, and requires significant effort to verify, validate, reconcile and standardise data. Poor data quality delays processing, increases operational costs, arrives at inaccurate benefits, and may require manual intervention.
Complexity in plans: Retirement and pension plans may contain unique eligibility criteria, complex formulae to calculate benefits, multiple payment options, compliance requirements and plan-specific exceptions. PRT adds complexity: even a small interpretation error can lead to incorrect calculations and payment disbursement. Given that insurance firms are responsible for accurately administering benefits over extended periods, this can expose insurers to compliance risk and penalties. Maintaining consistency across plans and product variations remains an ongoing challenge for operations teams.
Siloed operations: Retirement and PRT administration demand coordination across multiple functions, including intake, data entry operations, compliance, customer service, finance and accounting, and technology teams. However, these activities are typically carried out on different systems with different workflows. Such a siloed way of working results in manual handoffs, delayed processing, and difficulty in identifying bottlenecks. As volumes increase, these inefficiencies can become more pronounced and difficult to manage.
What are the core components of an AI-enabled retirement model?
Next-gen PRT operations will be defined by AI-enabled operating agents that bring together intelligent automation, business expertise, and governance controls. The objective is not simply to process transactions faster, but to improve the quality of decisions and enhance participant experience. In our view, insurance firms must leverage AI technologies to build a retirement model that can manage rising volumes, complex plan structures, evolving regulations, and growing participant expectations. Let us examine the key components of an AI-enabled retirement model.
The model must include an intelligent data extraction engine with the ability to automatically capture and validate details. The engine must also be equipped to structure information related to benefits, payments, and plans. Conventional retirement onboarding and administration is largely a manual exercise involving review of members’ data, benefit statements, and plan documents. Automating this activity improves accuracy, reduces processing time, and supports efficient onboarding, pricing, and benefit payout.
Incorporating a rules engine into the model helps convert plan requirements, eligibility criteria, benefit logic, payment rules, and compliance obligations into executable validation logic. This logic can be consistently applied across the operating model, a vital requirement given increasingly complex retirement products and changing regulations. Rule-based processing helps reduce calculation errors and improves compliance.
Efficient retirement administration entails significant time spends on monitoring progress across multiple teams and systems. AI-backed process automation can help streamline these activities, enabling better visibility into case progress, reducing manual handoffs, and supporting faster completion of onboarding, benefit administration, and payment activities. The model can accommodate rising participant volumes without a corresponding increase in operational effort.
Our experience of engaging with retirement providers reveals that a relatively small number of cases account for a disproportionately large amount of operational effort. These cases often involve incomplete records, complex plans, benefit disputes, payment exceptions or compliance concerns. Insurers must adopt a structured exception management approach to quickly identify such cases and route them to the appropriate specialists for review. This approach allows routine transactions to continue while monitoring complex cases.
Regulations require retirement and PRT administrators to demonstrate how participant records, benefit calculations, payment decisions, and operational activities are processed throughout the lifecycle. Maintaining complete records of validations, approvals, changes, and transactions is therefore a critical part of day-to-day operations. Governance controls provide the structure needed to support these requirements by creating a documented record of operational activities and decisions taken. This not only helps during audits and regulatory reviews but also promotes consistency, reduces gaps, and strengthens confidence in outcomes.
Each component translates into a specific capability, and insurers must adopt them through a phased transformation journey, which will help strengthen operations, improve governance, and realise business value.
How can insurers transition to a new retirement model?
In our view, insurers must transition to the new AI-enabled retirement model in phases (see Figure 1) instead of approaching it as a large-scale technology replacement, with a focus on building the capabilities needed to support greater automation, consistency, and scale.
Figure 1: A phased shift to a connected, governed, and AI-enabled retirement and pension operations
Phase 1: Establish the foundation by standardising processes and workflows, strengthening governance, improving data quality, and enhancing document management to bring greater consistency across retirement and PRT operations. This helps reduce operational complexity, improve control, and create a stable base for further upgrades.
Phase 2: Modernise data intake processes to improve how information is received, organised, and managed. Intelligent data extraction tools can read, structure, and validate data from plan documents, participant records, payroll files, benefit statements, and other sources, increasing accuracy and reducing manual effort.
Phase 3: Apply business rules consistently across retirement and PRT processes—introduce rules-as-code to convert plan rules, eligibility criteria, and compliance checks into repeatable decision logic. Laying down clear rules for plan provisions, eligibility checks, benefit calculations, and compliance requirements helps improve accuracy, reduce exceptions, and deliver more consistent outcomes for participants and plan sponsors.
Phase 4: Coordinate operations across the retirement and PRT functions. From onboarding and servicing to payment administration, many activities involve multiple teams and systems. Improving workflow management, reducing manual handoffs, and increasing visibility across processes helps ensure that work is routed efficiently, exceptions are managed effectively, and operations run in a more connected and seamless manner.
Phase 5: Create a unified AI-enabled operating model by bringing together process, data, rules, and workflow improvements, ensuring human oversight and continuous monitoring and improvement. This strengthens controls and delivers a more consistent experience for participants, plan sponsors, and operational teams. As these capabilities mature, insurers are better positioned to expand the use of AI and automation across retirement and PRT operations.
How can insurers prepare for the future of retirement?
The retirement and PRT market is expected to grow exponentially over the next few years. At the same time, participant expectations are becoming more demanding and continue to evolve. As technology evolves, the younger participants, including millennials and Gen Z, increasingly expect digital access, quicker responses, greater transparency, and easy-to-use self-service options. Insurance firms will also need to manage larger pension portfolios while meeting regulatory requirements and maintaining high-quality standards.
As the market continues to evolve, insurance firms will need to evaluate if their existing operating models are equipped to support increasing volumes and complex pension obligations. In addition, firms must assess how they can deliver better experience to a demanding customer segment while ensuring accuracy and compliance and efficiently leverage automation while retaining human expertise critical to effective oversight, decision-making, and governance.
Insurance firms that invest today in strong data foundations, process efficiency, robust operational controls, and knowledgeable teams will be better positioned for future growth. Building these capabilities now can help insurance companies achieve higher operational goals, increase participant trust, and respond more effectively to changing business challenges and customer expectations. The decisions made today will play an important role in determining how successfully insurers navigate the next phase of growth in the retirement and PRT market.
References
1 LIMRA: Final U.S. Retail Annuity Sales Set New Sales High, Totaling $464.1 Billion in 2025, March 2026, Retrieved September 2026, https://www.limra.com/en/newsroom/news-releases/2026/limra-final-u.s.-retail-annuity-sales-set-new-sales-high-totaling-$464.1-billion-in-2025/
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