Vendor Agents Won’t Just Support Your Insurance Core—They May Own It
Profitability and margin pressure are pushing insurers to modernize their core systems, and agentic AI is enabling major productivity gains. From product development and pricing, to underwriting, servicing and claims, vendors can use AI agents to achieve standardized, workflow-embedded reasoning capabilities.
Introduction
Our recent studies1, 2 indicate that most insurers face three main challenges in the lead-up to 2030 and beyond:
- Profitability and margin pressure
- Changing customer expectations and demographic shifts
- Higher risk volatility and claims inflation.
At the same time, Agentic AI3 is expected to reshape core insurance processes4, but it has become increasingly difficult to simultaneously balance the needs of insurers, customers, and regulators.
Insurers operate in an increasingly tight market where margin pressure forces them to improve efficiency. In an attempt to meet customer expectations and stay competitive, many are taking steps to implement Agentic AI and significantly reduce operating cost and cycle times.
Customers increasingly expect rapid, transparent, convenient journeys (e.g., self-service, immediate responses, real-time status updates), but will only accept AI-enabled processes if outcomes feel consistent and fair, and if they believe data is being handled securely and responsibly, as highlighted in a recent BCG article.
Regulators require robust governance, auditability, and clear controls over how information is processed - and how automated decisions are designed. This constrains and slows down the implementation of AI capabilities and their integration in core processes, as outlined in another BCG article.
In all our client work, we see insurers experimenting with bespoke Agentic AI tools - often as pilots that deliver local wins but are far from scaled impact. Many still run on dated, highly customized core systems and face the modernization dilemma, outlined in this BCG article.
A key strategic question emerges: Modernize the bespoke core with Agentic AI–enabled tooling - or leverage a vendor core to accelerate the agentic journey through embedded, governable capabilities?
The automation gap in core processes
Boosting operations efficiency is a core field of action for many insurers, with the improvement of operating cost-to-serve being a top priority. But our BCG Insurance Excellence Benchmark (IEB) still shows a clear productivity gap driven by manual, personnel-intense work across underwriting, claims handling, and customer service.
The question is, how does Agentic AI help insurers reduce cost-to-serve, address topline potential, and absorb demographic-driven capacity constraints simultaneously?
To interpret the automation gap, we translate it into a cost-base view (Exhibit 1) using 2024 earned gross premiums (Top 10 players across P&C/Life/Health)1:
- Operating cost ratio: ~24% (P&C) vs ~10% (Life) vs ~8% (Health)
- Scale effects: only marginal in Life; largely flat in P&C and Health (i.e., no materially higher operating costs despite lower earned gross premiums)
Assuming 20% of operating costs are allocated to FTEs, the automation-addressable pool is ~€4.5bn per annum (P&C ~€3bn; Life ~€1bn; Health ~€0.5bn).
Having established this estimate, understanding where the most labor-intensive work is taking place in core operations is the next step – pinpointing where automation will have the greatest impact.
Our IEB results, covering major players in Germany and retail insurance, illustrate today’s FTE allocation (see Exhibit 2):
- On average, ~52% of total FTEs are allocated to core insurance processes (44%) and line-specific execution activities (8%), indicating that productivity gains in core operations have a direct, material impact on the operating costs (and profitability as a result)
- In terms of core insurance processes, applications (14%), contract and policy management (32%), and operative claims handling (41%) make up the largest FTE buckets
The IEB results also include degrees of automation for the above FTE allocation. Based on this, we have focused on select P&C lines (personal and motor) and Life insurance to see where automation already works - and where manual effort still dominates (see Exhibit 3):
- Across benchmark participants, straight-through processing remains limited: for Underwriting/Applications and Policy Administration, the average is just above 50% in personal lines, and just over one third in motor lines
- Claims handling is the least automated domain: >90% of claim events are still primarily being processed manually across insurance types, validating the high staffing intensity mentioned earlier (and highlighting substantial efficiency potential)
Not only can Agentic IT reduce FTE-driven operating costs by automating exceptions, it can also improve core insurance processes and customer outcomes. For example, in claims, Agentic AI can dynamically analyze documentation and detect potential fraud, and in operations and servicing, it can resolve ~80% of interactions (providing proactive nudges and next-best action recommendations), as highlighted in a recent BCG article.
Agentic AI uses probabilistic reasoning to handle factors like context and ambiguity, dynamically adapting business rules as policies evolve - unlike deterministic, rule-based automation. Using core-system vendor building blocks, insurers can deploy it safely at scale as a “digital regulated employee.”
Architectural building blocks: Closing the automation gap with Agentic AI
While it is deceptively easy to launch Agentic AI pilots, it is extremely difficult to deliver value in production.
True automation gains require insurers to move beyond standalone pilots, embedding Agentic AI strategically where it can reliably change the way work gets done. The main goal is to use the technology to reduce manual effort and shorten cycle times; please see this BCG article for reference.
Based on our project experience, three architectural layers are involved in achieving value in production:
- The Agentic AI Layer - agents add probabilistic reasoning to interpret context, handle ambiguity, and orchestrate tools and workflows beyond deterministic rules (especially in high-variance, exception-heavy work)
- The Data Layer - constraints typically do not affect LLM-models, but they do impact the data foundation: fragmented repositories, incomplete document capture (e.g., OCR), missing metadata, and inconsistent master data can limit reliable automation
- The Infrastructure & Cloud Layer - legacy integration patterns, limited API exposure, brittle interfaces, and long release cycles constrain secure, scalable, and operable connections between agents and core workflows
How vendors tackle these challenges in their commercial core systems with Agentic AI
Vendor core systems increasingly offer standardized, embedded AI out of the box - helping insurers scale adoption with governance and compliance by design.
To provide current insights into the architectural building blocks, we invited selected vendors to share perspectives. The sections that follow reflect the vendors’ own inputs and provide a neutral market view - not a comparison, evaluation, or recommendation provided by BCG Platinion.
Our core insurance platforms are the Insurance Suite and the P&C Factory. AI capabilities are deeply embedded in these core systems and support key value-creation processes across the insurance value chain.
Focus areas include sales, pricing and underwriting, claims management, and policy administration. The Insurance Suite provides basic functions like policy information via chatbot or email bot, automated processing of change requests, end-to-end handling of broker submissions (commercial lines), extraction and classification of documents, recognition of damage patterns using ML, and “Next Best Actions” for claims handlers.
We also position our AI Platform and Insurance Data Platform as the centralized data backbone for scalable, governed AI use.
We provide Agentic AI and AI-supported capabilities across the insurance core, especially in sales, pricing and underwriting, claims management and policy administration.
In sales and underwriting, AI completes application data, makes inputs plausible, and derives tariff attributes from unstructured data. In claims, documents are extracted and classified automatically, ML models detect damage patterns, cumulative losses are identified early, and “Next Best Actions” support handlers.
AI-supported workflows are already a core part of the P&C Factory (AI in a Process), while AI agents can also be used as individual sub-processes (AI as a Process). In policy administration, change requests are processed automatically and broker submissions can be handled end-to-end. In customer service, chatbots and email bots provide policy information and answer standard inquiries.
Production-ready use cases include: policy information via chatbot or email bot; AI-supported assistance for policy inquiries; automated processing of change requests; end-to-end handling of broker submissions (commercial lines); and extraction and classification of documents.
Further production-ready use cases: recognition of damage patterns via ML; early detection of cumulative losses; “Next Best Actions” for claims handlers; identification of recovery potential and consequential damages; automatic reserve setting; completion of application data; automatic plausibility checks; derivation of tariff attributes from unstructured data; and straight-through processing via ML rulesets.
Our AI-related offering combines deeply embedded AI in the core systems, ready-to-use productive agents, and a platform-based target architecture. We emphasize seamless core integration across underwriting, claims, policy administration and customer service. Both the AI Platform and Insurance Data Platform serve as a centralized data lake — providing the foundations for scalable, governed AI use.
Our offering can be described as a platform approach with shared governance, reusable components, and centralized operation — designed with communicating agents in mind.
We offer AI-based customer chat via the Process:it product — customers can ask about contracts, make changes, and receive optimization suggestions. The same concept is planned for internal staff; in claims, Claim:it checks information against product rules and LLM-based reasoning, and can process a claim in an almost fully autonomous way (with human review before payout). Image recognition also applies forensic analysis to flag potentially AI-generated fraud.
We develop our insurance agents ourselves because generic tools do not fit our software, releases, and domain knowledge closely enough. We are LLM-agnostic, use our own prompt layer, and can work with any model our customers run. Orchestration is handled by a workflow engine; the data layer is managed through a dedicated analytics platform. Over time, we intend to become a multi-model platform with robust governance and agent-to-agent communication via MCP.
We stream suite data into a real-time data service, combining it with other source systems — important for customers connecting legacy and new core environments. The platform feeds reporting, data warehouses, and AI agents, with data dictionaries supporting reliable analytics and production use.
For true end-to-end use cases, a selective API layer around the suite is essential — AI creates value only when linked to workflows and follow-up processes, not as a stand-alone recognizer.
Our cloud strategy is deliberately flexible. The SaaS solution is primarily deployed on a leading hyperscaler, while also supporting customer environments and enabling rapid deployment across other common cloud setups due to its containerized architecture.
We do not expect AI to replace insurance core systems in the next three to five years. Insurers still need the structure, governance, and regulatory reliability of a core platform. AI will therefore sit as an additional architectural layer: the core provides the stable backbone, while agents enable more flexible and individualized interaction at the edge. Customers should also be able to connect their own agents above the protected core via MCP-based access — already established in life insurance and now being extended into P&C.
The roadmap features elements like AI-Workbench, AI Marketplace, Knowledge Management, Context-Engineering, and a future-ready architecture with autonomous agents.
Our strategic direction is to move away from single, embedded AI capabilities and productive agents toward a holistic, governed platform approach. We explicitly contrast point solutions and platform solutions, positioning ourselves as the platform for reusable components, centralized governance, and scalable operation.
The AI Platform provides a unified architecture for orchestrating agents, enforcing governance standards, and enabling enterprise-wide automation.
adesso positions its insurance offering around the in|sure Ecosphere, a modular insurance platform architecture that combines core insurance functionality with surrounding services and products. The platform covers P&C, life, and health as well as cross-functional capabilities such as partner management, commission, in-/ex-casso, and workflow. Core modules can be deployed together within the Ecosphere or integrated individually into an existing application landscape via standard interfaces and REST APIs.
Around this core, adesso combines Fraud Management, Input Management, adesso GPT, and br.AI.n alongside partner solutions — an open ecosystem rather than a closed monolithic core. Products are used by more than 50 insurers across P&C, life, and health in the German market.
adesso supports three operating models: on-premises / private cloud, hybrid, and SaaS. In the SaaS model, adesso operates the platform on AWS; in hybrid setups, responsibilities are shared; and in on-premises or private-cloud models, customers operate individual or integrated adesso products themselves. The platform is described as highly modular, cloud-enabled, AI-ready, and compliance-by-design.
In the in|sure Ecosphere, Agentic AI is not a stand-alone add-on but a deeply embedded Agentic AI Layer. Ecosphere forms the transactional backbone, while the Agentic AI Layer acts as the orchestration layer that securely integrates AI agents into autonomous core processes and enables explainable automation of complex business processes.
adesso applies Agentic AI in three main areas:
- Claims processing — currently the primary focus, combining fully automated AI-supported claims management with adesso Fraud Management (AFM), which assesses claims in real time and flags suspicious cases automatically. This is expected to improve the combined ratio by up to 4%.
- Input management and customer service — AI processes unstructured information in real time, classifies it, and routes it into relevant follow-up processes, supported by AIM, adesso's AI-powered input management solution.
- Underwriting and pricing — the architecture supports integration of specialized predictive AI models for real-time risk assessment and pricing, with analytical agents enabling automated usage-based premium adjustments.
adesso positions Ecosphere as an open, agnostic platform: the Agentic AI layer provides native orchestration while insurers integrate third-party or proprietary AI solutions.
The most concrete production-ready use cases are concentrated in claims and input management. In claims, adesso combines automated claims handling via partner omni:us with adesso Fraud Management (AFM) for real-time fraud detection and case flagging.
adesso also points to customer-facing assistance scenarios such as Digital Roadside Assistance, where a virtual agent supports hotline intake, verifies contract data against the core system, and guides the service process more efficiently.
adesso does not force customers into a closed solution. Ecosphere is radically open to third-party systems. The Ecosphere Agentic Layer offers procedural advantages as a native orchestration layer, yet customers can decide at any time to seamlessly integrate their own AI solutions to maintain full digital sovereignty.
Ecosphere's USP lies in its agnostic approach to AI combined with the use of pre-built modules that adesso already provides fully integrated.
adesso's current focus is AI applications along the claims chain — from triage through fraud detection. Document-based contextualization and a broker assistant are also in development, within an ecosystem approach that can incorporate third-party solutions.
adesso builds the Agentic AI layer on a leading hyperscaler with managed foundation model services, with a strong focus on compliance and governance. For agent development, adesso favors open-source orchestration frameworks over proprietary model-provider SDKs.
Relevant data sources include core transactional data, claims and risk data, contract and document data, and external signals. adesso links AI use cases to data-platform capabilities for near-real-time views on risk and claims developments.
adesso sees the core transaction layer as the stable system of record, with the agentic and orchestration layers built around it. The architecture works with adesso's own systems and existing customer environments, allowing roll-out without a full migration.
adesso deploys on a leading hyperscaler with managed foundation model services, supplemented by third-party tooling for compliance, observability, and token metering. Deployment across other hosting environments remains possible.
adesso sees the future insurance core as a stable transactional and regulatory backbone responsible for core processing, compliance, and the economic logic of the insurer, while agentic capabilities develop around it as an additional architectural layer. The core must expose and support these capabilities, but the agentic logic, customer interaction, and surrounding ecosystem services will increasingly sit outside the core itself.
adesso expects the role of the core platform to become more relevant, not less — it remains the part of the architecture that secures regulatory requirements, calculation logic, and the insurer's license to operate — the decisive part for regulated execution.
adesso positions Agentic AI as the next competitive standard: AI that intervenes autonomously in core insurance processes. Core systems must evolve into open ecosystems that can integrate and scale agentic capabilities.
adesso's answer is the in|sure Ecosphere: the transactional backbone plus the native Agentic Layer br.AI.n for autonomous AI orchestration. A best-of-breed approach lets insurers integrate any AI solutions — including partner solutions such as omni:us, which is cited as delivering a combined-ratio improvement of more than 4%.
adesso aims to transform the in|sure Ecosphere into a more flexible, AI-enabled ecosystem — expanding AI layers such as br.AI.n, strengthening orchestration, and enabling best-of-breed integration of native, partner, and customer-provided AI solutions. The focus is on systematic evolution of the core platform toward scalable, sovereign, and increasingly autonomous operations.
At Faktor Zehn, we integrate Agentic AI into our software modules wherever it helps support business decisions, automate complex processes, and sustainably increase efficiency across core insurance processes. Together with a specialized partner focused on AI agents for the insurance industry, we are actively driving the use of Agentic AI within core insurance systems.
We currently deploy AI agents in the following areas:
- Knowledge assistance — intelligent support for business decisions with transparent source referencing
- Customer correspondence — automated, context-based generation of responses to customer inquiries
- Document verification — AI-supported matching of incoming documents with reference data, including plausibility checks
- Document processing — automated analysis, structuring, and summarization of complex, unstructured content
- Fraud detection — analysis of documents and images to identify potential anomalies, including calculation of a fraud score
We focus on a holistic view of processes and achieving a high degree of automation through a combination of deterministic workflows and AI agents.
- A tool that answers domain-specific questions, significantly improving response quality in client service operations.
- AI in insurance customer service that generates concrete response suggestions for customer inquiries, significantly reducing response times.
- Automated document verification for incoming invoices through intelligent matching with existing reference data and detection of inconsistencies, ensuring high data quality.
The key value we offer is the consistent combination of an open, modern core system architecture with integrated Agentic AI capabilities. By providing REST services, we open up our core insurance solutions for seamless integration of AI and process-supporting agents, ensuring customers remain flexible in the long term and can independently manage their technology decisions. Agents can be developed and operated flexibly by Faktor Zehn, customers, or third-party providers.
Our AI strategy deploys AI specifically where it delivers the greatest value — particularly in increasing automation and improving customer interaction. We see significant value in combining established deterministic process chains with AI agents, which are well suited for preparing decisions and interpreting unstructured data.
Our software supports a wide range of deployment scenarios, enabling compliance with data protection, data sovereignty, and regulatory requirements including the EU AI Act — combining technological openness, process integration, and regulatory security.
We position AI-centric applications above the systems of record in a layer of workflows, task management, and AI agents. Capabilities include policy and coverage inquiries with transparent reasoning, intelligent customer response generation, invoice and document validation, automated document analysis, and fraud scoring. Available via chat interface or integrated via REST. We provide a stepwise adoption path from standalone assistants to semi- or fully autonomous agents.
LLM-agnostic approach; model selection depends on insurer context and target infrastructure. Agents interact via tools or REST and are not limited to Faktor Zehn-owned systems. No cloud lock-in — operation by Faktor Zehn or the customer is both possible.
The Faktor Zehn Suite supports all common relational databases. For AI use cases like knowledge bases or vector search, no fixed platform standard is prescribed — the data architecture is adapted to the customer's existing landscape. The key requirement is access to structured core data and, depending on the use case, additional document or knowledge sources.
We have a technology-agnostic, modular architecture. Integration with surrounding systems (inbox/task management, document sources, operational systems) is handled via REST. We do not impose proprietary document management; AI outputs can prepare or trigger follow-up actions within the core process.
Flexible deployment: on premises, private cloud, or customer-specific or partner-operated cloud environments. For Faktor Zehn–managed cloud scenarios, operations are jointly managed through a partner-supported delivery model supporting multiple cloud providers. Sovereign cloud setups, such as hosting within Germany, are supported — ensuring compliance with regulatory and data-residency requirements and positioning us for the growing demand for Sovereign AI solutions.
We see Agentic AI as an additional layer on top of stable systems of record. Core business logic should continue to reside in the core system or closely related services, while agents flexibly access that functionality. Not every process step should become agentic: deterministic steps remain clearly advantageous for reasons of cost, stability, and determinism. AI should be used where it creates real value — such as in document understanding, fraud indication, or contextual interpretation. We therefore expect a coexistence with tight integration between a stable insurance core and a flexible agent layer.
- Knowledge assistance (rolling out over 12 months) — standardized integration of AI services for context-based rule searches with transparent source references, targeting improved quality and traceability of business decisions
- Claims automation — further automation of the claims notification process to significantly increase STP rates and reduce processing times
- Service operations — full integration of AI-supported assistance solutions, including automated quality-assured responses to customer inquiries
The field of AI continues to evolve at a very rapid pace. Our core platform provides the foundation on which we integrate AI functionalities quickly, flexibly, and at scale. In parallel, we are driving the holistic optimization of business processes through AI — particularly in claims, where we expect increasing automation through AI agents, improving efficiency, speed, and service quality.
We embed AI into the insurance core in a controlled, compliant manner — combining innovation speed with regulatory security for long-term value creation.
We offer two core products: Graphene, an enterprise-grade, modular, cloud-native SaaS insurance core platform, and Fusion, an insurance sales and orchestration platform for scalable distribution models. Peak3 also provides a growing portfolio of agentic AI solutions.
Graphene supports all major lines of business on one platform, with a no-code product configurator enabling rapid product development and iteration. Besides traditional products, Graphene supports usage-based, parametric, and accumulator products across the full insurance value chain.
Deployed as a cloud-native, cloud-agnostic SaaS platform, Graphene offers flexible deployment options: regional public instance (Peak3-managed, multi-tenant public SaaS), private single/multi-tenant instance (Peak3-managed private SaaS), or private single/multi-tenant instance (client/partner-managed). Full microservices architecture supports end-to-end or selective module deployment.
Recognised by Celent (XCelent 2025), IDC MarketScape (Major Player 2025), and ISG Provider Lens (Leader/Rising Star 2024).
We build agentic AI solutions that can be deployed across the value chain using a twofold approach: pre-built AI agents that are production-ready and can be deployed quickly, and an AI orchestration platform that enables insurers to co-build and manage custom agents across the lifecycle.
Our AI agent build is focused on three categories:
- Assessor and Triage Agents — autonomously assess transactions, develop risk reports and scores with transparent evidence chains, and assist with final decisions
- Intelligent Document Processing Agents — vision-powered AI agents that intelligently recognize, extract data from, and automate the validation of documents
- Voice & Text Customer Agents — manage and automate customer interactions 24/7 through conversational multi-modal AI agents
The following agentic AI use cases are production-ready and in implementation with clients today:
- Voice & Text Customer AI Agents
- Intelligent Document Processing (IDP) Agents
- AI Assessor & Triage Agents
Other solutions ready for production roll-out include voice AI telemarketing agents, compliance call agents, and collection agents — all designed as AI-driven outbound calls with human-like voice synthesis, multi-turn conversation capability, real-time intent analysis, and dynamic strategy adaptation.
- AI-native core integration — Graphene was designed from the ground up with AI readiness. Clients have the flexibility to deploy AI within Peak3 solutions or externally, integrating easily via MCP.
- Standalone or integrated deployment — agentic AI applications are decoupled from the Graphene core, allowing deployment of Peak3's AI solutions on top of other core systems.
- LLM and cloud agnosticism with long-term optionality — designed to be agnostic of any specific LLM or cloud platform, giving insurers full control over which underlying models best fit their needs.
- Compliance-first architecture with full CI/CD lifecycle management — explainability, observability, and security are architected into every AI component, with evidence chains, confidence-level citations, human-in-the-loop governance, and strict guardrails.
Solutions are designed to be multi-modal and support multiple languages. Clients can configure underlying capabilities to fit their needs on top of pre-defined capabilities (e.g., FWA rules in claims assessment).
LLM-agnostic multi-model framework with a Model Gateway for efficient routing and load balancing across multiple foundational models. Protocols: MCP, A2A, Open APIs, webhooks, tool calling, and SSE APIs. Key platform components include Agent Management, Agentic Workflow Engine, RAG Pipeline, LLM Operations, and a CI/CD + LLMOps factory.
Graphene provides built-in Data Warehouse and Customer Data Platform (CDP) capabilities. Real-time data synchronization, event streaming, and third-party API integration are supported. SOPs, rule libraries, policy terms, and compliance guides are managed via RAG pipelines with vector databases.
Graphene is an end-to-end core platform with most required systems built in or pre-integrated. External systems (scanning solutions, telephony, payment gateways) are integrated as needed.
Cloud-native and cloud-agnostic, built on microservices with containerized deployments. Primarily public hyperscalers; private and sovereign cloud also supported.
The insurance core will continue to play an essential role. Business logic, rules, calculations, and regulatory compliance cannot be fully absorbed into an agentic AI application layer. The core provides the deterministic, auditable, and compliant transactional backbone that insurers require.
However, the core must evolve into an “intelligent core” — an AI-ready platform serving as the foundation for agentic operations. Agentic AI operates as an orchestration and intelligence layer on top of the core, augmenting workflows while the core retains business logic, data integrity, and compliance governance — exposing its functionality through APIs and MCP so agents can interact with all modules.
- Available now / Q2 (continuous enhancements) — Agentic Claims: end-to-end AI-powered claims processing including FNOL chatbots, IDP, AI assessor/FWA, and claims assistant with HITL
- Q2/Q3 — Expanded AI Orchestration Platform: enhanced agent lifecycle management, broader MCP/A2A integration library
- Q3 — Agentic Underwriting: AI-assisted risk assessment, automated data gathering, and underwriting decision support
- Q3/Q4 — AI telemarketing and customer-facing agentic applications
Agentic backend configuration expected within H2 (currently POC stage).
Our focus is on deepening and scaling pre-built AI agents — expanding the portfolio from claims (where we have the deepest capabilities today) into underwriting and sales, while continuously enhancing existing modules (IDP, FWA, voice/text agents).
We continue co-building with customers and maintain long-term optionality across all deployment and model options, including Sovereign AI.
Conclusion: Agentic AI will sit both inside and outside the core
Insurers need to choose from three emerging paths to scale Agentic AI:
- Embed capabilities directly within vendor core systems
- Roll out an enterprise-wide agentic AI platform across the company
- Pursue a hybrid approach that combines both.
The best approach is ultimately about finding the right mix across these three paths, because much of the productivity upside sits in exception-heavy core work - especially policy changes, servicing, and claims (where high-variance cases are common).
In precisely these areas, vendor-embedded Agentic AI can help reduce FTE-driven operating costs by automating exceptions that traditional automation struggles with. At the same time, it can improve customer outcomes like speed, transparency, and consistency - driving higher levels of satisfaction, retention, and conversion at scale.
