Closing the €5 Billion Automation Gap: How Insurers Need to Navigate the Path to an Intelligent Core
Agentic AI is reshaping Core Insurance Architecture — with first-hand perspectives from four leading vendors
June 25, 2026
Est. reading time 7 minutes
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.
Key Takeaways
Agentic AI value is still stuck in window-dressing pilots. Point solutions outside the core create integration friction and limited scalability — particularly where auditability and process controls are required
The automation gap is biggest where it matters most. Core insurance processes — especially claims — remain largely manual, with over 90% of claims events still processed without automation
Complexity is the bottleneck. High-variance cases in claims resist deterministic automation, driving cost leakage and cycle time overruns that straight-through processing alone cannot address
Agentic AI can close the execution gap at scale. The addressable operational efficiency prize across insurance core processes is estimated at ~€5 billion annually
Vendors of insurance core systems are expanding their offerings to support the next generation of AI-driven insurance operations. Embedded, governance-ready capabilities lower the barrier to realizing measurable process improvements
Core strategy has become AI strategy. Deciding whether intelligence lives inside or outside the core is a defining architectural choice for the next cycle
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Foreword
Through continuous engagement with incumbent as well as emerging players and leading technology vendors in the insurance space, BCG Platinion helps clients navigate, design, and implement state-of-the-art insurance architectures for the Agentic AI era.
Accordingly, this BCG Platinion perspective serves two purposes: first, it explores the architectural implications of Agentic AI in insurance by combining our technology, digital, and AI expertise; second, it provides a non-biased market snapshot through interview-style insights from technology vendors in the insurance space, including adesso, Faktor Zehn, .msg, and Peak3.
Introduction
Our recent studies (BCG Global Megatrends Research, 2025; BCG Insurance Excellence Benchmark, 2025) indicate that most insurers face three main challenges in the lead-up to 2030 and beyond:
Profitability andmargin pressure
Changing customer expectations and demographic shifts
Higher risk volatility and claims inflation
Agentic AI — software capable of autonomously observing, planning, and acting through LLMs — is expected to reshape core insurance processes, from product development and underwriting to servicing, operations, and claims management. At the same time, it has become increasingly difficult to simultaneously balance the needs of insurers, customers, and regulators (Exhibit 1).
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.
Exhibit 1
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.
Insurance Excellence Benchmark
The BCG Insurance Excellence Benchmark is an annual study in which BCG collects and analyzes operational and performance metrics from major insurance players. The benchmark has been conducted for multiple years, providing consistent year-over-year comparisons and a fact base on how the industry evolves across efficiency, digitalization, and customer service outcomes.
Data collection focuses on core insurance and operational areas, including FTE deployment and productivity indicators. It also captures process performance metrics like cycle times, and digitalization measures such as degree of automation and straight-through processing across key processes.
To ensure comparability, participating insurers provide a standardized set of business and volume indicators, including premiums, insured persons, and insured risks. This ensures that operational metrics can be interpreted in context. The benchmark is complemented by thematic deep dives on current topics so as to validate findings, explain drivers of performance differences, and identify practical improvement levers.
The data is aggregated and analyzed to derive industry averages and reference ranges that represent the current market baseline. Participating insurers receive a tailored view of their own results and relative positioning versus peer groups, as well as the overall benchmark perspectives and observed trends. This enables insurers to quantify their performance in operational efficiency, digital readiness, and customer-centricity, while also identifying structural gaps.
Ultimately, it equips them to prioritize targeted initiatives based on a comparable, market-tested fact base.
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 2) using 2024 earned gross premiums (top 10 players across P&C/Life/Health, based on BaFin data):
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 around 20% of operating costs are linked to operational workflows with automation potential, the addressable efficiency pool totals approximately €5bn annually (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.
Exhibit 2
Our IED results, covering major players in Germany and retail insurance, illustrate how operational capacity is distributed across the insurance value chain (see Exhibit 3):
On average, ~52% of operational capacity is concentrated in core insurance processes (44%) and line-specific execution activities (8%), highlighting the significant impact that improvements in core operations can have on overall operating efficiency and profitability
Within core insurance processes, applications (14%), contract and policy management (32%), and operational claims handling (41%) represent the largest activity areas
Exhibit 3
The IEB results also include levels of automation across the respective operational domains. Based on this, we focused on selected P&C lines (personal and motor) as well as Life insurance to assess where automation is already effective — and where processes still rely heavily on manual intervention (see Exhibit 4):
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 remains the least automated domain: more than 90% of claim events are still processed with significant manual involvement across insurance types, underscoring the opportunity for greater operational efficiency and process automation
Exhibit 4
Not only can Agentic IT reduce operating complexity and improve efficiency 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 safely deploy these capabilities at scale within regulated environments.
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, five architectural layers are involved in achieving value in production:
Value Creation Layer – where interaction with customers or other agents in an insurance ecosystem happens. Agentic AI will fundamentally change how insurance business is acquired and serviced in the future
(Gen)AI Layer - where agents live, are orchestrated and managed. agents add probabilistic reasoning to interpret context, handle ambiguity, and orchestrate tools and workflows beyond deterministic rules (especially in high-variance, exception-heavy work)
Data & Knowledge Layer - the agentic data foundation: an insurer’s often dispersed data repositories, incomplete document capture (e.g., OCR), missing metadata, and inconsistent master data are consolidated, enriched and made available to agents (e.g. via a semantic layer)
Core Transaction Layer - where the core insurance (deterministic) logic and (auditable and safeguarded) data ismaintained. This layer needs to provide tools, data and services to agentic use – often the major challenge for agentic adoption
Infrastructure & Cloud Layer – in the age of agentic AI an insurer’s infrastructure needs to support more than ever secure and flexible hybrid cloud setups where LLMs can run and communicate in different environments w/o long set-up times
Exhibit 5
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 complement our perspective, the vendor sections below present selected market viewpoints from technology vendors shaping the future of Agentic AI in insurance. Selected introductory slides provide a broader view of each vendor’s technology positioning and solution landscape.
To provide current insights into the architectural building blocks, we asked four vendors to respond to six key questions. In addition, we conducted interviews with their thought leaders, structured around the five building blocks of our reference architecture (see Exhibit 5), as well as a forward-looking discussion on the future of core insurance systems in an Agentic AI landscape.
The vendors’ own inputs and interview responses, providing a unique market snapshot of how leading technology providers view the current state and future direction of Agentic AI in insurance. These perspectives are presented as vendor viewpoints and are not intended as a comparison, evaluation, endorsement, or recommendation by BCG Platinion.
adesso
The following insights represent the vendor’s viewpoint. Introductory slides providing additional context on the company and platform approach are linked here.
adesso
Q1
Platform profile
Q2.1
Embedding Agentic AI
Q2.2
Production-ready use cases
Q3
AI differentiators
Q4
Architectural building blocks
6 sub-questions — interview format
Q5
12-month roadmap
Q6
Strategic focus
adesso · Q1
BCG Platinion asks
Let us start by briefly describing adesso's core insurance platform(s), target segments / lines, and typical deployment model(s).
adesso responds
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.
adesso provides an Agentic layer around this core. The Agentic layer steers Agentic components thus provide value added services to bring inIsure Ecopshere to a full service extend. One example is a GenAI driven Claims solution for all Claims services. This solution creates fully automated GenAI driven processes end to end. Furthermore there are components in ti as Fraud Management, Input Management and the br.AI.n solution which steers GenAI processes.
adesso supports three operating models: on-premises / private cloud, hybrid, and SaaS. In the SaaS model, adesso operates the platform itself on a leading hyperscaler infrastructure; in hybrid setups, responsibilities are shared between adesso and the customer; and in on-premises or private-cloud models, customers operate individual or integrated adesso products themselves with flexible support from adesso. The company describes the platform as highly modular, cloud-enabled, AI-ready, and compliance-by-design.
adesso · Q2.1
BCG Platinion asks
Let us understand where adesso is using Agentic AI within the insurance core today.
adesso responds
In the in|sure Ecosphere, Agentic AI is not designed as a stand-alone add-on, but as a deeply embedded Agentic Layer. Ecosphere forms the transactional backbone, while the Agentic Layer acts as the orchestration layer that securely integrates AI agents into autonomous core processes and enables explainable automation of complex business processes.
We apply Agentic AI in three main areas:
Claims processing — This is currently the primary focus, as it offers the greatest potential to reduce operating costs. adesso combines a fully automated, AI-supported claims management solution with an external partner, which is expected to improve the combined ratio by up to 4%, with adesso Fraud Management (AFM), an AI-based fraud detection solution that assesses claims in real time and flags suspicious cases automatically.
Input management and customer service — Because input management is the central intake channel for customer enquiries, adesso uses AI to process unstructured information in real time, classify it, and route it into the relevant follow-up processes. This is supported by AIM, adesso's proprietary AI-powered input management solution.
Underwriting and pricing — The architecture supports the integration of specialized predictive AI models for real-time risk assessment and pricing. Analytical agents can evaluate historical data and enable automated usage-based premium adjustments.
Overall, adesso positions Ecosphere as an open, agnostic platform: Ecosphere's Agentic AI layer provides the native orchestration, while insurers can integrate third-party or proprietary AI solutions at any time and retain full digital sovereignty.
adesso · Q2.2
BCG Platinion asks
Which Agentic AI use cases are already production-ready and used by clients today?
adesso responds
In the in|sure Ecosphere, Agentic AI is controlled via the deeply embedded Agentic Layer (in|sure Ecopshere Agentic Layer), which orchestrates autonomous core processes across lines of business. The most concrete production-ready use cases today are concentrated in claims and input management.
adesso uses adessoGPT as a chatbot for Tariff information, manuals and other information within the inIsure Ecosphere, helping relieve insurance employees of repetitive daily tasks and improving access to information.
In claims, adesso combines automated claims handling via an external 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 · Q3
BCG Platinion asks
What does adesso think is technically / functionally their unique selling proposition regarding AI?
adesso responds
We do not force customers into a closed solution. Ecosphere is radically open to third-party systems. Our Ecosphere Agentic Layer offers enormous procedural advantages as a native orchestration layer, yet customers can decide at any time to seamlessly integrate their own AI solutions to maintain their full digital sovereignty. Ecosphere's USP lies, on the one hand, in its agnostic approach to the use of AI (and general openness to the integration of third-party services) and, on the other, in the use of pre-built modules that adesso already provides fully integrated.
The highlighted MVP candidates represent early-stage solution areas currently being refined in specific customer environments. Of the three, Claims Resolution is closest to productive maturity, while Underwriting Assistant and Broker Assistant remain exploratory.
adesso · Q4 — Architectural building blocks
How does your architecture address the six layers?
Perspectives shared during vendor interviews.
BCG
Q4.1 Value Creation Layer | What kind of AI-centric applications are available/customizable?
ADS
adesso's current focus is on AI applications along the claims chain for P&C insurers, which the company positions as its most advanced and productization-ready area today, especially from triage through fraud detection. In addition, adesso is developing document-based contextualization capabilities to extract relevant information from contract documents and DMS content for business processes. A broker assistant is a further application area currently being pursued with interested insurers. More broadly, adesso positions these applications within an ecosystem of agents that can also incorporate third-party solutions.
BCG
Q4.2 (Gen)AI Layer | Which LLMs, tools and platforms do you support today?
ADS
adesso positions the Agentic AI layer as a separate intelligence and orchestration layer on top of the transactional core, while the core itself remains a deterministic transaction engine for regulatory and operational reasons. The company currently builds this layer on a leading hyperscaler infrastructure and leverages managed foundation model services to integrate and operate AI models, with a strong focus on compliance and governance capabilities. For agent development, adesso favors open-source orchestration frameworks over deep reliance on proprietary model-provider SDKs. The resulting setup combines a cloud-centered infrastructure approach with an open and flexible orchestration and agent tooling architecture.
BCG
Q4.3 Data & Knowledge Layer | Which data and which data platforms do you need to implement your use cases?
ADS
Relevant data sources include core transactional data, claims and risk data, contract and document data, and additional external signals where needed to identify emerging accumulations and spikes. adesso links its AI use cases increasingly to data-platform capabilities, particularly where insurers need to correlate risks, claims, and pricing signals more dynamically than in traditional retrospective portfolio steering. In this context, the company points to portfolio-oriented data solutions that support near-real-time views on risk and claims developments, with the aim of deriving pricing and steering impulses earlier in the process. At the same time, adesso describes this area as still evolving, with further extension of the solution set under consideration.
BCG
Q4.4 Core Transaction Layer | What additional systems are required to implement adesso's agentic use cases end-to-end?
ADS
adesso sees the core transaction layer as the stable system of record that must provide the necessary transactional capabilities and interfaces, while the agentic and orchestration layers are built around it. The AI architecture is designed to work with adesso's own systems as well as with existing customer environments, so that insurers can roll out individual solutions without a full functional or data migration. End-to-end implementation therefore requires integration between the core, existing surrounding customer systems, and the surrounding agentic layer rather than a fully closed proprietary stack.
BCG
Q4.5 Infra and Cloud Layer | What is your cloud strategy?
ADS
adesso currently deploys its platform on a leading hyperscaler infrastructure and builds its AI-related architecture around managed foundation model services. The broader operating setup includes additional third-party tooling for compliance, observability, execution, and token metering. Overall, the current cloud setup is centered on a primary hyperscaler for both platform and AI infrastructure, while technical deployment across other hosting environments remains possible.
BCG
Q4.6 Your perspective | What does the future of the insurance core look like?
ADS
adesso sees the future insurance core as a stable transactional and regulatory backbone that remains responsible for core processing, compliance, and the economic logic of the insurer, while agentic capabilities develop around it as an additional architectural layer. In this view, the core must be able to expose and support these capabilities, but the “agentic” logic, customer interaction, and surrounding ecosystem services will increasingly sit outside the core itself. At the same time, adesso expects the role of the core platform to become more relevant, not less, because it remains the part of the architecture that secures regulatory requirements, processes, calculation logic, and ultimately the insurer's license to operate. More broadly, adesso frames the future as an ecosystem model in which the core platform is one part of a wider network of agents and partner services, but the decisive part for regulated execution and scalable value creation.
adesso · Q5
BCG Platinion asks
What does adesso have on their Agentic AI roadmap for the next 12 months and by when can insurance customers anticipate new agentic features?
adesso responds
Agentic AI as a survival factor The era of isolated chatbot experiments is over. adesso positions Agentic AI as the next competitive standard: AI that not only supports users, but intervenes autonomously in core insurance processes. In this view, simply adding AI to legacy monoliths will not be sufficient. Instead, core systems need to evolve into open ecosystems that can integrate and scale agentic capabilities.
adesso's answer to this is the in|sure Ecosphere. It acts as the transactional backbone, providing regulatory structure and process logic, while the native Agentic Layer orchestrates autonomous AI processes. This layer follows a best-of-breed approach: insurers can integrate whichever AI solutions create the most value for their use case, whether partner solutions such as omni:us or their own proprietary models. adesso argues that this architecture is designed to create measurable ROI rather than isolated proofs of concept. In claims processing, for example, the company cites a combined-ratio improvement of more than 4% through omni:us, while the claims handler increasingly takes on a supervisory role over AI-supported processes.
adesso · Q6
BCG Platinion asks
Last but not least: What is adesso's strategic angle for the near future in Agentic AI in Insurance?
adesso responds
adesso sees AI as the future operating model of insurance and aims to transform the in|sure Ecosphere into a more flexible, AI-enabled ecosystem. Strategically, the focus is on expanding AI layers such as br.AI.n, strengthening orchestration across applications and processes, and enabling best-of-breed integration of native, partner, and customer-provided AI solutions. In adesso's view, the degree of automation across insurance processes will continue to rise, while remaining bounded by regulatory requirements and customer-specific constraints. This positions the near-term strategy less as the launch of isolated new modules and more as the systematic evolution of the core platform toward scalable, sovereign, and increasingly autonomous AI-enabled operations.
Faktor Zehn
The following insights represent the vendor’s viewpoint. Introductory slides providing additional context on the companyare linked here.
Faktor Zehn
Q1
Platform profile
Q2.1
Embedding Agentic AI
Q2.2
Production-ready use cases
Q3
AI differentiators
Q4
Architectural building blocks
6 sub-questions — interview format
Q5
12-month roadmap
Q6
Strategic focus
Faktor Zehn · Q1
BCG Platinion asks
Let us start by briefly describing Faktor Zehn's core insurance platform(s), target segments / lines, and typical deployment model(s).
Faktor Zehn responds
Faktor Zehn GmbH is a specialized provider of IT solutions for the insurance industry. As a software company, it develops modern applications based on a contemporary Java architecture. Its modular software components can be deployed individually or combined within the Faktor Zehn Suite to support the full range of core insurance processes, from product definition and quotation to policy administration, claims management, and partner management.
Faktor Zehn's goal is to help insurers address the challenges of digital transformation in a targeted and practical way. Its solutions are already in use at more than 20 insurers. Faktor Zehn also benefits from a broad network of experts and experience drawn from more than 350 international projects.
The company's software supports a wide range of insurance lines, with a strong focus on non-life lines of business, including personal, commercial, motor, and industrial lines. Faktor Zehn offers flexible deployment models ranging from traditional on-premises installations to a SaaS model in which operations and maintenance are fully managed by Faktor Zehn.
Faktor Zehn · Q2.1
BCG Platinion asks
Let us understand where Faktor Zehn is using Agentic AI within the insurance core today.
Faktor Zehn responds
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 developing AI agents for the insurance industry, we are actively driving the use of Agentic AI within core insurance systems.
Currently, we 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 taking a holistic view of processes and achieving a high degree of automation through a combination of deterministic workflows and AI agents.
Faktor Zehn · Q2.2
BCG Platinion asks
Which Agentic AI use cases are already production-ready and used by clients today?
Faktor Zehn responds
Introduction of a tool that answers domain-specific questions for service employees, significantly improving response quality.
Implementation of AI in insurance customer service that generates concrete response suggestions for customer inquiries, significantly reducing response times.
Introduction of automated document verification for incoming invoices through intelligent matching with existing reference data and detection of inconsistencies, ensuring high data quality.
Faktor Zehn · Q3
BCG Platinion asks
What does Faktor Zehn think is technically / functionally their unique selling proposition regarding AI?
Faktor Zehn responds
The key value we offer in the context of artificial intelligence 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. This ensures that 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 follows the principle of deploying AI specifically where it delivers the greatest value, particularly in increasing automation and improving customer interaction. Strategically, we see significant value in combining established deterministic process chains with AI agents. The latter are particularly well suited for preparing decisions and interpreting unstructured data.
At the same time, our software is designed to support a wide range of deployment scenarios, enabling compliance with data protection, data sovereignty, and regulatory requirements, particularly with regards to the EU AI Act.
Overall, this approach combines technological openness, process integration, and regulatory security, making AI a scalable differentiating factor within the insurance core.
Faktor Zehn · Q4 — Architectural building blocks
How does your architecture address the six layers?
Perspectives shared during vendor interviews.
BCG
Q4.1 Value Creation Layer | What kind of AI-centric applications are available/customizable?
F10
We position AI-centric applications in a layer above the systems of record, consisting of workflows, task management and AI agents. The capabilities currently available support a wide range of intelligent automation scenarios: enabling policy and coverage inquiries with transparent rule-based reasoning and source references; generating intelligent, context-aware customer responses with automated routing; validating invoices and documents in claims processes through reference matching and plausibility checks; automatically analyzing and summarizing complex documents; and detecting potential fraud in documents and images by applying detailed fraud scoring mechanisms. These capabilities are currently available through a chat-like interface, but they can also be integrated into other front ends and processes via REST. We provide a stepwise adoption path, ranging from standalone assistants to integration into inbox and task-management environments (and eventually to semi or fully autonomous agents). We also remain flexible at platform level: operation by Faktor Zehn or by the customer is both possible (there is no lock-in to any cloud).
BCG
Q4.2 (Gen)AI Layer | Which LLMs, tools and platforms do you support today?
F10
We follow an LLM-agnostic approach and do not commit to specific model providers. The models used depend on the insurer context, existing vendor relationships, and target infrastructure. Agents interact with operational systems via tools or REST services and are not limited to Faktor Zehn-owned systems; third-party systems can also be connected. One advantage is that the core systems' REST services already provide semantically interpretable data, which makes them easier for agents to consume. We also remain flexible at platform level: operation by Faktor Zehn or by the customer is both possible (there is no lock-in to any cloud).
BCG
Q4.3 Data & Knowledge Layer | Which data and which data platforms do you need to implement your use cases?
F10
In the transactional core, the Faktor Zehn Suite supports all common relational databases. For AI use cases like knowledge bases, additional data stores, or vector search, we do not prescribe a fixed platform standard. Instead, the data architecture is adapted to the customer's existing landscape; for example, existing databases or managed cloud services. From our perspective, the key requirement is access to structured core data from the systems of record and, depending on the use case, to additional document or knowledge sources.
BCG
Q4.4 Core Transaction Layer | What additional systems are required to implement Faktor Zehn's agentic use cases end-to-end?
F10
Implementing Faktor Zehn’s agentic use cases end-to-end typically requires integration with additional surrounding systems, such as document and input management solutions. In most cases, these capabilities are already established within the insurer’s IT landscape. Task management capabilities, by contrast, can also be provided as part of the Faktor Zehn solution. This approach is enabled by a technology-agnostic and modular target architecture. Faktor Zehn’s solution exposes standard REST services and agentic capabilities, allowing AI agents to access core business data and to prepare or trigger process steps both within Faktor Zehn systems and across connected surrounding systems.
BCG
Q4.5 Infra and Cloud Layer | What is your cloud strategy?
F10
Faktor Zehn follows a flexible deployment approach: deployment is generally possible on-premises, in a private cloud, or in customer-specific (or partner-operated) cloud environments. For Faktor Zehn–managed cloud scenarios, operations are jointly managed through a partner-supported delivery model. The offering supports multiple cloud providers and enables deployment in sovereign cloud setups, such as hosting within Germany. This ensures compliance with regulatory and data-residency requirements, while maintaining a high degree of flexibility. As a result, we are well positioned to address the growing market demand for Sovereign AI solutions.
BCG
Q4.6 Your perspective | What does the future of the insurance core look like?
F10
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 in closely related services, while agents flexibly access that functionality. In our view, 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, contextual interpretation, or work preparation. For that reason, we expect a coexistence with a tight integration between a stable insurance core and a flexible agent layer.
Faktor Zehn · Q5
BCG Platinion asks
What does Faktor Zehn have on their Agentic AI roadmap for the next 12 months and by when can insurance customers anticipate new agentic features?
Faktor Zehn responds
Our roadmap is designed to drive the targeted expansion of AI functionalities within core processes. A key component is the standardized integration of AI services for knowledge assistance, enabling context-based rule searches with transparent source references. This functionality will be made widely available over the next 12 months, and is specifically aimed at improving the quality and traceability of business decisions.
In the claims sector, a major focus is on further automating the claims notification process. The goal is to significantly increase straight-through processing (STP) rates while reducing processing times.
In addition, the full integration of AI-supported assistance solutions for service operations is being advanced. This includes both supporting employees with domain-specific questions, and automatically providing quality-assured responses to customer inquiries.
Faktor Zehn · Q6
BCG Platinion asks
Last but not least: What is Faktor Zehn's strategic angle for the near future in Agentic AI in Insurance?
Faktor Zehn responds
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. At the same time, our platform enables customers to leverage this extensibility themselves.
In parallel, we are driving the holistic optimization of business processes through the use of AI. Particularly in the claims area, we expect increasing automation through AI agents, sustainably improving efficiency, speed, and service quality.
Simultaneously, we embed AI into the insurance core in a controlled, transparent, and compliant manner. This combines technological innovation speed with regulatory security and creates a robust foundation for the productive use of AI and long-term value creation.
.msg
The following insights represent the vendor’s viewpoint. Introductory slides providing additional context on the company and platform approach are linked.
.msg
Q1
Platform profile
Q2.1
Embedding Agentic AI
Q2.2
Production-ready use cases
Q3
AI differentiators
Q4
Architectural building blocks
6 sub-questions — interview format
Q5
12-month roadmap
Q6
Strategic focus
.msg · Q1
BCG Platinion asks
Let us start by briefly describing .msg's core insurance platform(s), target segments / lines, and typical deployment model(s).
.msg responds
Our core insurance platforms are the msg.Insurance Suite — the broader insurance platform — and the msg.P&C Factory, which focuses specifically on policy administration and claims in P&C. 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, and automated processing of change requests. It also offers 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 the msg.AI Platform / msg.Insurance Data Platform as the centralized data backbone for scalable, governed AI use.
.msg · Q2.1
BCG Platinion asks
Let us understand where .msg is using Agentic AI within the insurance core today.
.msg responds
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 supports data completion, validation, and risk-related decision preparation. In claims, AI is used for document handling, pattern recognition, and decision support within workflows. In policy administration, AI helps automate servicing processes and broker-related workflows. In customer service, AI supports policy inquiries and standard communication through conversational channels.
More broadly, AI-supported workflows are already embedded in the msg.P&C Factory today (AI in a Process), while AI agents can also be deployed as individual sub-processes (AI as a Process). Productive AI agents in customer implementations include claim:it, process:it, and ask:it.
.msg · Q2.2
BCG Platinion asks
Which Agentic AI use cases are already production-ready and used by clients today?
.msg responds
Our use of agents to deliver policy information via chatbot or email bot is production-ready, as is our AI-supported assistance capability for policy inquiries, the automated processing of change requests, end-to-end handling of broker submissions (commercial lines), and the extraction and classification of documents.
Other production-ready use cases involve the recognition of damage patterns via ML, early detection of cumulative losses, “Next Best Actions” for claims handlers, and the 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 are also production-ready use cases we offer.
.msg · Q3
BCG Platinion asks
What does .msg think is technically / functionally their unique selling proposition regarding AI?
.msg responds
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. AI-supported workflows are a core component of the msg.P&C Factory, and both the msg.AI Platform and msg.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.
.msg · Q4 — Architectural building blocks
How does your architecture address the six layers?
Perspectives shared during vendor interviews.
BCG
Q4.1 Value Creation Layer | What kind of AI-centric applications are available/customizable?
MSG
We already offer AI-based customer chat via the Process:it product. Customers can ask about contracts, make changes, and receive suggestions on how to optimize coverage or premiums. The same concept is planned for internal staff, so case workers can retrieve condensed information and trigger actions through chat (instead of navigating multiple screens). AI is also used in document intake and claims handling — documents are classified, analyzed, and routed into workflows. When it comes to our claims solution, Claim:it, an agent checks information against product rules and LLM-based reasoning, and can process a claim in an almost fully autonomous way (with human in the Loop principial when convience Score, for example, is not high enough). Using image recognition, it also uses a forensic approach to flag potentially AI-generated fraud.
BCG
Q4.2 (Gen)AI Layer | Which LLMs, tools and platforms do you support today?
MSG
We develop our solutions in-house because generic solutions can never match the depth of processing or guarantee the determinism of our deeply integrated solutions. We are LLM-agnostic, use our own prompt layer, and can work with whichever models the customer runs. Orchestration is handled by a workflow engine, while the data layer is managed through a dedicated analytics platform. Over time, we intend to create a multi-model platform with stronger governance and agent-to-agent communication via MCP.
BCG
Q4.3 Data & Knowledge Layer | Which data and which data platforms do you need to implement your use cases?
MSG
Data from the msg suite streams into a real-time data service and combines it with other source systems — especially important for customers that must connect legacy and new core environments while still getting fast reporting and analytics. Our real-time data platform also feeds downstream reporting, warehouses, and AI agents. Our data dictionaries help map structures quickly and support reliable analytics and production use.
BCG
Q4.4 Core Transaction Layer | What additional systems are required to implement .msg's agentic use cases end-to-end?
MSG
For true end-to-end use cases, we believe having a selective API layer around the suite is essential. The key idea is that AI creates value only when it is linked to workflows, rules, and follow-up processes, not as a stand-alone recognizer. With this in mind, we are pushing tighter integration between the API layer, AI layer, and process layer.
BCG
Q4.5 Infra and Cloud Layer | What is your cloud strategy?
MSG
The 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. Stack:it Cloud is also a viable deployment option.
BCG
Q4.6 Your perspective | What does the future of the insurance core look like?
MSG
We do not expect AI to replace insurance core systems. In our view, 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. Vendors must drive innovation proactively and help customers adopt AI in a controlled way. That includes enablement, change support, and practical AI building blocks for the customer layer. Customers should also be able to connect their own agents above the protected core via MCP-based access; this is already established in life insurance and is now being extended into P&C.
.msg · Q5
BCG Platinion asks
What does .msg have on their Agentic AI roadmap for the next 12 months and by when can insurance customers anticipate new agentic features?
.msg responds
The msg.Insurance Suite already provides basic AI functions as part of the implementation roadmap. The roadmap includes build-out elements such as AI-Workbench, AI Marketplace, Knowledge Management, Context-Engineering, domain-specific / universal / task agents, and a future architecture with communicating agents.
In software engineering, the roadmap includes:
DoR agent — productive
AI in coding — in use
Requirements assistant — under expansion
Test generation — under expansion
Quality gate vision
We cannot yet specify public availability dates and module-by-module customer-value metrics such as STP uplift or cycle-time reduction.
.msg · Q6
BCG Platinion asks
Last but not least: What is .msg's strategic angle for the near future in Agentic AI in Insurance?
.msg responds
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, system integration, and platform solutions — positioning the platform solution as the target for reusable components, centralized governance, and scalable operation.
The msg.AI Platform provides a unified, scalable architecture for orchestrating AI agents, enforcing governance and security standards, and enabling enterprise-wide automation.
Peak3
The following insights represent the vendor’s viewpoint. Introductory slides providing additional context on the company and platform approach are linked.
Peak3
Q1
Platform profile
Q2.1
Embedding Agentic AI
Q2.2
Production-ready use cases
Q3
AI differentiators
Q4
Architectural building blocks
6 sub-questions — interview format
Q5
12-month roadmap
Q6
Strategic focus
Peak3 · Q1
BCG Platinion asks
Let us start by briefly describing Peak3's core insurance platform(s), target segments / lines, and typical deployment model(s).
Peak3 responds
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, and its no-code product and workflow configurator enables rapid proposition development and iteration. Besides traditional insurance products, Graphene supports usage-based, parametric, and accumulator products — the platform covers the entire insurance value chain end-to-end.
Deployed as a cloud-native, cloud-agnostic SaaS platform, Graphene also offers flexible deployment options: (1) Regional public instance (Peak3-managed, multi-tenant public SaaS), (2) Private single/multi-tenant instance (Peak3-managed private SaaS), or (3) Private single/multi-tenant instance (client/partner-managed).
With full microservices architecture, Graphene supports end-to-end deployment or selective module implementation (e.g., replacing only the claims module).
Peak3 and Graphene have been recognized by Celent, IDC, ISG and Gartner.
Peak3 · Q2.1
BCG Platinion asks
Let us understand where Peak3 is using Agentic AI within the insurance core today.
Peak3 responds
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 that can be deployed across different parts of the value chain:
Assessor and Triage Agents — to automate and improve decisions with AI agents that autonomously assess transactions, develop risk reports and scores with transparent evidence chains, and assist experts with final decisions
Intelligent Document Processing Agents — to eliminate manual work and increase accuracy with vision-powered AI agents that intelligently recognize, extract data from, and automate the validation of documents
Conversational Customer Support Agents — to manage, improve, and automate customer interactions 24/7 through conversational multi-modal AI agents
Peak3 · Q2.2
BCG Platinion asks
Which Agentic AI use cases are already production-ready and used by clients today?
Peak3 responds
The following agentic AI use cases are production-ready and in implementation with clients today:
Conversational Customer Support 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. These are all designed as AI-driven outbound calls with human-like voice synthesis, multi-turn conversation capability, real-time intent analysis, and dynamic strategy adaptation.
Peak3 · Q3
BCG Platinion asks
What does Peak3 think is technically / functionally their unique selling proposition regarding AI?
Peak3 responds
AI-native core integration: with a full microservices and open architecture, Graphene was designed from the ground up with AI readiness. We provide flexibility to clients to deploy AI within Peak3 solutions or externally, easily integrating via MCP, API or CLI.
Standalone or integrated deployment of AI agents: we decouple our agentic AI applications from the Graphene core platform, allowing the deployment of our AI solutions on top of other core systems.
LLM and cloud agnosticism with long-term optionality: we design to be agnostic of any specific LLM or cloud platform. Insurers control which underlying models — whether public or private — best fit their needs.
Compliance-first architecture with full CI/CD lifecycle management: explainability, observability, and security are architected into every AI component. Evidence chains, confidence-level citations, human-in-the-loop governance, and strict guardrails ensure compliance.
Peak3 · Q4 — Architectural building blocks
How does your architecture address the six layers?
Perspectives shared during vendor interviews.
BCG
Q4.1 Value Creation Layer | What kind of AI-centric applications are available/customizable?
P3
Pre-built solutions include AI agents for risk assessment and triaging (e.g., underwriting and claims), intelligent document processing (across all document types) and conversational customer support. These solutions are designed to be multi-modal and support multi-languages. Clients can configure underlying capabilities to fit their needs on top of pre-defined capabilities (e.g., FWA rules to be applied in claims assessment).
BCG
Q4.2 (Gen)AI Layer | Which LLMs, tools and platforms do you support today?
P3
We design to be LLM-agnostic and support a multi-model framework. The architecture includes a Model Gateway that facilitates efficient management, routing, and load balancing across multiple foundational models. Insurers are in full control of selecting the underlying models — whether public or private — that best fit their regulatory, performance, and cost requirements. Protocols and standards we support include: MCP (Model Context Protocol), A2A (Agent-to-Agent), Open APIs, webhooks, tool calling, and Server-Side Events (SSE) APIs for real-time streaming. Key platform components include: Agent Management (centralized configuration and lifecycle management of AI agents), Agentic Workflow Engine (dynamic task orchestration guided by SOPs), RAG Pipeline (retrieval-augmented generation for knowledge management), LLM Operations (model management, fine-tuning, and performance monitoring), and a CI/CD + DevOps + LLMOps factory for enterprise-grade stability and observability in production environments.
BCG
Q4.3 Data & Knowledge Layer | Which data and which data platforms do you need to implement your use cases?
P3
Generally, if deployed within Graphene, no further data platform is required, but integration with external data sources (for data sitting outside of the core) may be needed. Graphene provides built-in big data platform capabilities, including a Data Warehouse and Customer Data Platform (CDP). Real-time data synchronization and event streaming ensure AI agents operate on current information. Hot case caching supports low-latency decision-making. Third-party data APIs can be integrated for enrichment (e.g., external fraud databases, medical data services). The platform enforces unified data access across functional modules with consistent identifiers, data quality standards, systematic data lineage, and ACID-compliant transactions. Depending on the use case, further data may be needed, such as SOPs, rule libraries, policy terms, regulatory guidelines, fraud patterns, medical codes (ICD-10, CPT), FAQ listings, product specifications, and compliance guides — which are managed via RAG pipelines with vector databases for semantic retrieval.
BCG
Q4.4 Core Transaction Layer | What additional systems are required to implement Peak3's agentic use cases end-to-end?
P3
Graphene is designed as an end-to-end core insurance platform, which means most required capabilities are already built-in or pre-integrated. Generally, any system that sits outside of a normal core application needs to be integrated with (e.g., scanning solutions for physical claims documents, telephony system for outbound AI calls, payment gateways, etc.).
BCG
Q4.5 Infra and Cloud Layer | What is your cloud strategy?
P3
Our cloud strategy is built on three principles: cloud-native, cloud-agnostic, and flexible deployment. Graphene is built entirely on microservices with containerized deployments. Generally, we deploy on public hyperscalers, but subject to availability of required services (including self-build) we also deploy on private and sovereign cloud.
BCG
Q4.6 Your perspective | What does the future of the insurance core look like?
P3
The insurance core will continue to play an essential role. Business logic, rules, calculations, and regulatory compliance traditionally managed by core platforms cannot be fully absorbed into an agentic AI application layer. The core provides the deterministic, auditable, efficient and compliant transactional backbone that insurers require. However, the core must evolve from a passive system-of-record into an “intelligent core” — an AI-ready platform that serves as the foundation for agentic operations. This means agentic AI operating as an orchestration and intelligence layer on top of the core, augmenting and automating workflows while the core retains business logic, data integrity, and compliance governance. The core exposes its functionality comprehensively through APIs and modern protocols (MCP) so AI agents can seamlessly interact with all modules. While AI in the core can be delivered as an integrated experience, we believe it should be architecturally separated.
Peak3 · Q5
BCG Platinion asks
What does Peak3 have on their Agentic AI roadmap for the next 12 months and by when can insurance customers anticipate new agentic features?
Peak3 responds
Our agentic AI roadmap for this year includes the following phases:
Live with continuous enhancements: Agentic Claims — end-to-end AI-powered claims processing including FNOL chatbots, IDP, AI assessor/FWA, and claims assistant with HITL
Currently in client pilot: Agentic Underwriting — AI-assisted risk assessment, automated data gathering, and underwriting decision support (currently for health, with additional product lines to be added)
Currently in internal pilot (Q3 release): Agentic Backend Configuration — prompt-based configuration of products, calculations, rules and calculations in the core system for faster speed to market
Q4: AI Telemarketing Agents (as well as other customer facing agentic applications)
We expect to release agentic configuration of backend capabilities within H2 (currently POC stage).
Peak3 · Q6
BCG Platinion asks
Last but not least: What is Peak3's strategic angle for the near future in Agentic AI in Insurance?
Peak3 responds
Our focus is on deepening and scaling pre-built AI agents: Expand the portfolio of production-ready AI agents from claims (where we have the deepest capabilities today) into underwriting and sales, while continuously enhance existing modules (IDP, FWA, voice/text agents).
Importantly, we don't want to build in a vacuum and are co-building with customers before productizing solutions. We want to continue a strategy of long-term optionality for clients, supporting all deployment and model options (incl. Sovereign AI).
Conclusion: Agentic AI will sit both inside and outside the core
Incumbent insurance core system vendors are clearly raising the bar, combining deep industry expertise with ambitious Agentic AI roadmaps and growing innovation momentum. At the same time, a new generation of AI-native challengers is entering the market with GenAI-powered offerings that operate independently of traditional core system platforms.
Our data is unambiguous: insurance core operations carry a significant and largely untapped automation opportunity, concentrated precisely where manual effort is highest — claims, underwriting, and servicing. Closing this gap requires more than isolated pilots. It demands an architectural commitment. The fivelayers that determine production impact (the Value Creation Layer, (Gen)AI layer, the Data and Knowledge layer, Core Transaction Layer and the Infrastructure layer) all must be designed together, not bolted on sequentially.
As vendor offerings and core systems evolve from passive systems of record into AI-enabled, active, intelligence-bearing platforms, the strategic question shifts: not whether to adopt Agentic AI, but where intelligence should live in the architecture, and who owns it. That decision — inside the core, outside it, or both — defines the three paths available to insurers today.
Insurers need to choose from three emerging paths to scale Agentic AI:
Focus on rolling out an enterprise-wide agentic AI platform across the company
Focus on embedded capabilities directly within vendor core systems
Pursue a hybrid approach that combines both from the beginning
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 operational complexity by automating repetitive and exception-heavy workflows. At the same time, it can improve customer outcomes like speed, transparency, and consistency — driving higher levels of satisfaction, retention, and conversion at scale.
The race is on and insurers embracing agentic AI to close their automation gap in a smart way will be able to tap into the savings potential to fund the AI scaling journey. Please get in touch with our experts below to learn more!
Through ongoing dialogue with board members, technology leaders, and solution providers across the insurance market, we continuously track emerging architectural and AI trends shaping the industry.
Authors
Christoph Fritsch
Managing Director
Vienna
,
Austria
Christoph is Managing Director at BCG Platinion and leads the BCG Platinion Office in Vienna. He specializes in architecting, blueprinting, planning, and coordinating implementations of large-scale IT transformations—anchored in architecture, road mapping, and sourcing/cost optimization.
Christoph is a core member of the Insurance Practice Group and has been working for local, European, and global insurers for more than 13 years. He also leads BCG Platinion’s Insurance Architecture Benchmarking core team and supports clients in translating strategy into scalable target architectures that accelerate value creation.
Jens Müller
AI Tech Architecture Director
Frankfurt
,
Germany
Jens is a Director at BCG Platinion and leads the European Data Architecture community and the Architecture community in Central Europe. Throughout his career, he has always been passionate about both engineering and being an IT architect.
He started out as a SW engineer, moved on to analyzing and designing domain-specific, enterprise-wide architectures across industries, focusing on financial services. With BCG Platinion for 14 years now, he leads teams designing core system modernization approaches, architecture blueprints, developing platform buildup strategies and shaping delivery organizations.
Andrea Seier
Senior Director - Insurance Benchmarks, BCG Vantage
Dusseldorf
,
Germany
Andrea is aSenior Director – Insurance Benchmarks at BCG Vantage and a core member of BCG’s Insurance Practice. She has extensive expertise in insurance performance benchmarking and operational excellence. Andrea holds a PhD in Economics, with a thesis focused on private and occupational pensions.
She leads BCG Vantage’s Insurance Benchmarks business and, together with her team, delivers industry-leading benchmarking programs, including the Insurance Excellence Benchmark and IT Benchmarking in Insurance, enabling insurers to improve performance, efficiency, and competitiveness.
Thomas Sonnleitner
Principal, Tech Advisory & Delivery
Vienna
,
Austria
Thomas Sonnleitner is a Principal in the Vienna office of BCG Platinion, specialized in functional and IT architectures as well as large-scale transformation programs.
He supports insurance and financial institutions in modernizing their IT landscapes, from target architecture design to implementation. With experience in both traditional and agile delivery models, he combines deep technological expertise with industry insight to drive sustainable digital transformations.
Fabian Burzlaff
AI Tech Architect
Stuttgart
,
Germany
Fabian Burzlaff is an IT Architect at BCG Platinion in Stuttgart, specializing in IT architecture, AI-driven software engineering, and large-scale transformation programs. He combines deep technical expertise in cloud platforms, software engineering, and GenAI with hands-on experience in banking, insurance, and healthcare transformations. Fabian supports clients in designing scalable digital architectures, optimizing enterprise IT landscapes, and translating emerging technologies into practical business impact.
Ennio Kerber
AI Tech Architect
Berlin
,
Germany
Ennio Kerber is an IT Architect at BCG Platinion in Berlin, specializing in cloud infrastructures, enterprise architecture, and distributed information systems. He supports clients across energy, financial services, insurance, and telecom in designing scalable IT landscapes, cloud-native platforms, and long-term transformation roadmaps. Ennio combines hands-on software engineering expertise with deep architectural knowledge and holds certifications in cloud and enterprise architecture frameworks.
