of European insurers surveyed by EIOPA are already actively using Generative AI. Many applications are still at proof-of-concept stage.
Source: EIOPA 2026Apply AI in insurance with real impact.
From claims and underwriting to customer service, distribution, Microsoft Copilot and AI agents: we help insurers identify relevant use cases, enable their people and bring AI safely into real business processes.
of reported GenAI use cases are in back-office functions such as document processing, content creation, coding or underwriting assistance.
Source: EIOPA 2026FINMA highlights data quality, data protection and explainability among key risk topics. At the same time, hybrid insurance and AI capabilities are becoming more important.
AI becomes relevant when it improves real insurance processes.
Instead of starting with individual tools, we look at the tasks and decisions across the insurance value chain. This makes it possible to prioritise opportunities by business value, feasibility, risk and organisational readiness.
01
Customer & Distribution
Customer service, advisory, broker support, quote preparation and communication.
02
Underwriting
Submission intake, risk research, guidelines, document analysis and decision support.
03
Policy & Operations
Documents, knowledge access, internal workflows, email processes and Microsoft Copilot.
04
Claims
Claims intake, triage, document analysis, case preparation, communication and fraud signals.
05
Risk & Compliance
Governance, controls, policies, documentation, human oversight and AI risk management.
From concrete problems to measurable business value.
Strong AI use cases do not start with the technology. They start with tasks that consume time, create handover friction, make knowledge hard to access or slow down decisions unnecessarily.
Claims
High potential
Claims Intake & Triage
Problem
Claims notifications arrive via email, PDFs, images and free text and need to be structured manually.
AI Approach
Extract information, summarise documents, check completeness and prepare or route cases.
Business Value
Less manual preparation and faster case handling.
Underwriting
High potential
Underwriting Assistant
Problem
Underwriters need to bring together extensive documentation, guidelines and external information.
AI Approach
Summarise submissions, highlight relevant information, search guidelines and prepare follow-up questions.
Business Value
Faster preparation with clear human decision ownership.
Customer Service
Scalable
Service Agent Assist
Problem
Employees search for information across different systems, policies, terms and process manuals.
AI Approach
Knowledge access, summaries, response drafts, classification and contextual support during interactions.
Business Value
More consistent answers and shorter search and handling times.
Distribution
Role-based
Broker & Sales Copilot
Problem
Preparation, documentation and follow-ups take significant time around advisory and sales activities.
AI Approach
Prepare conversations, structure product information, summarise meetings and create follow-ups.
Business Value
More time for customers and less administrative follow-up.
Knowledge & Operations
Foundational
Insurance Knowledge Assistant
Problem
Insurance knowledge is distributed across policies, product documents, process documentation and intranets.
AI Approach
Search approved knowledge sources in context, derive answers and show sources transparently.
Business Value
Faster access to knowledge and fewer recurring internal questions.
Risk & Compliance
Human oversight
Policy & Compliance Review
Problem
Policies, requirements and documentation need to be compared regularly and checked for changes.
AI Approach
Compare documents, highlight changes, prepare control questions and support documentation work.
Business Value
More efficient review preparation without handing responsibility over to AI.
Fraud & Claims
Decision support
Fraud Signal Detection
Problem
Suspicious patterns are not always easy to detect across large case volumes, documents and historical data.
AI Approach
Identify signals and anomalies, prioritise cases and prepare additional review steps for specialists.
Business Value
Focus specialist review on cases requiring additional investigation.
Agents & Automation
Emerging
AI Agents for Multi-Step Workflows
Problem
Many processes consist of recurring steps across multiple systems, documents and roles.
AI Approach
Agents gather information, carry out defined steps, prepare decisions and hand over to people.
Business Value
Fewer manual handovers and better orchestrated processes with clear control points.
In insurance, it is not only about what AI can do, but where it should be used responsibly.
The closer AI gets to customers, pricing, risk assessment or claims decisions, the more important clear responsibilities, controls and traceable processes become. Governance should therefore be designed in from the start rather than added later.
Regulatory requirements depend on the specific use case, the organisation and the technology used. Our work does not replace legal or regulatory advice.
Data & Privacy
Which data may be processed and how are quality, availability and confidentiality ensured?
Explainability & Quality
How are outputs reviewed, understood and appropriately validated in critical applications?
Human Oversight
Where does AI support and where does human review or decision-making remain mandatory?
Third-Party Risk
How are external models, platforms, providers and dependencies reflected in governance and operations?
Security & Access
Which roles, permissions and security mechanisms apply to data, systems and AI applications? AI Security Testing helps assess attack surfaces in chatbots and agents.
Documentation & Ownership
Who owns a use case and how are purpose, controls, changes and operating processes documented?
Insurance expertise remains essential. AI capability is added on top.
Effective AI adoption is not just a technology project. Claims teams, underwriters, customer service, sales, Legal & Compliance and leaders need different capabilities, examples and guardrails for their day-to-day work.
Role-based Enablement
01
AI Literacy & Responsible AI
Foundations, opportunities, limitations, privacy, risks and responsible use.
02
Role-specific Use Cases
Relevant applications based on real tasks from claims, underwriting, service or operations.
03
Tools, Prompting & Copilot
Productive use of approved tools with practical exercises from everyday insurance work.
04
Workflows & Agents
From individual prompts to repeatable ways of working, automations and agentic workflows.
05
Adoption & Scale
Embed champions, internal standards, governance and continuous improvement across the organisation.
From skills and use cases to governance and implementation.
Depending on your starting point, we begin with enablement, a concrete use case, Microsoft Copilot or a strategic question. The individual building blocks can be connected, but they do not have to start as a large transformation programme.
People
AI Enablement & Training
Role-based AI training with insurance examples, tools and concrete tasks from everyday work.
Corporate AI Training →Opportunities
AI Use Case Discovery
Systematically analyse claims, underwriting, service and operations and prioritise opportunities by value and feasibility.
AI Use Case Workshop →Productivity
Microsoft Copilot
Bring together Copilot readiness, governance, role-based use cases, training, adoption and internal champions.
Microsoft Copilot →Organisation
AI Advisory & Governance
Design strategy, operating model, AI governance, portfolio management and adoption for scalable AI use.
AI Advisory →Execution
AI Agents & Workflows
Prototype prioritised use cases, test technical feasibility and turn them into productive workflows or agents.
Dreamleap product links will be added laterAI expertise for people, processes and implementation.
We combine AI enablement, use case discovery, Microsoft Copilot, advisory and an implementation perspective. This creates not only knowledge about AI, but the capability to apply it responsibly across the organisation.
17'000+
people trained
150+
clients
Since 2019
AI Enablement & Transformation
Common questions about introducing AI in insurance.
Which AI use cases are particularly relevant for insurance?
Particularly relevant applications include claims, underwriting, customer service, distribution, knowledge management, document processing, fraud detection and internal operations. Which use case makes sense depends on business value, data availability, technical feasibility, risk and organisational readiness.
How can AI be used in claims management?
AI can structure claims notifications, summarise documents and images, extract information, classify cases, prepare follow-up questions and support triage. Critical decisions and controls should remain clearly anchored with people depending on the use case.
How can AI support underwriters?
AI assistants can summarise submissions, surface relevant risk information, search underwriting guidelines, identify missing information and prepare decision support. The goal is not to replace expertise, but to speed up information processing.
What role can AI agents play in insurance?
AI agents can coordinate multi-step tasks across documents, knowledge sources and systems. They can gather information, execute defined steps, trigger follow-up questions and hand results to employees for review. Governance, permissions and control points are essential.
What matters from a governance perspective when using AI?
Relevant topics include data quality, privacy, security, explainability, responsibilities, human oversight, third-party risk and traceable documentation. The specific setup depends on the use case and regulatory context.
Which AI skills do insurance employees need?
Alongside insurance expertise, AI literacy, safe use of approved tools, critical evaluation of AI outputs and an understanding of data, risks and responsible use are becoming more important. Role-based training connects these capabilities with real insurance tasks.
How should an insurance company get started with AI?
A strong starting point combines clear business goals, an overview of existing initiatives and prioritisation of relevant use cases. Skills, governance and technical conditions should be considered in parallel. Not every organisation needs to begin with a large transformation programme.
Where can AI create the greatest value in your insurance organisation?
Tell us briefly where you are today. We help you assess relevant use cases, capability needs and the right next step.