Build the Future of Insurance with AI
Join developers, innovators, founders, students and industry professionals from across Ireland to build practical AI-powered solutions to real challenges facing the insurance industry.
This year's theme — AI & Agents: Strategy, Processes and Roles — explores how AI can transform business workflows, support better decisions and reshape the future of work.
Register for the Challenge →As a proud challenge partner, InsTech.ie has developed seven problem statements grounded in genuine opportunities across the insurance industry. The challenges span four key dimensions:
Each challenge includes industry context, example personas and clear success outcomes — accessible even without an insurance background.
Explore the challenges
Select one of seven industry challenges developed by InsTech.ie — or bring your own idea. You do not need an insurance background.
What's the brief?
Expand any challenge to read the full brief — including why it matters, example personas, the challenge question and what success looks like.
⚙️1. Reimagining Insurance Workflows
Workflow Automation
Why This Matters
Insurance relies on hundreds of interconnected processes, from quoting and underwriting to claims and customer service. Many of these workflows still involve manual hand-offs, duplicated effort, and repetitive administration, slowing down decisions for both customers and employees. With advances in AI and agentic workflows, there is an opportunity to rethink entire end-to-end processes rather than simply automating isolated tasks. The biggest gains will come from redesigning how work flows seamlessly across people, systems, and departments.
Example Personas
David, an underwriter, sits idle waiting for information to be passed from several internal departments before making a risk assessment.
Aoife, a policyholder, experiences frustrating delays because her routine claim request passes through four separate teams.
The Challenge
How might we redesign core insurance workflows using AI agents and intelligent orchestration to eliminate manual hand-offs, accelerate decision-making, and deliver frictionless outcomes?
What Success Could Look Like
- Automated cross-system data transfer and task execution
- Drastically reduced end-to-end processing times
- Elimination of low-value administrative hand-offs
- Real-time tracking and faster resolution for customers
🔗2. Connecting the Insurance Ecosystem
Workflow Automation
Why This Matters
Insurance rarely operates in a silo. Policyholders, brokers, insurers, repair networks, healthcare providers, and assessors all exchange information throughout the lifecycle of a policy or claim. Today, much of that interaction relies on emails, unstructured PDFs, and disconnected systems — creating friction, data loss, and delays. Modern AI, secure APIs, and privacy-preserving data exchange present an opportunity to create intelligent, connected networks where partner entities collaborate seamlessly.
Example Personas
Michael, a commercial broker, spends hours chasing multi-carrier updates and re-keying risk data across different insurer portals.
Rachel, a pension holder, experiences multi-week delays while information slowly transfers between multiple provider platforms.
The Challenge
How might AI enable secure, intelligent collaboration across the broader insurance ecosystem to eliminate data silos, automate inter-company communication, and create seamless journeys?
What Success Could Look Like
- Automated extraction and ingestion of third-party documentation (e.g., garage estimates, medical reports)
- Real-time status sync across multi-party stakeholder networks
- Standardised, secure multi-party data sharing
- Streamlined broker-to-carrier communication channels
🛡️3. Building a Trusted AI Insurer
AI Adoption & Strategy
Why This Matters
AI adoption hinges on trust. Customers want clear reasons behind decisions, employees need confidence in machine-generated suggestions, and regulators demand transparency, auditability, and fairness. To move AI into production, insurers need practical mechanisms that ensure models operate within ethical, explainable, and compliant boundaries without sacrificing performance.
Example Personas
James, an applicant who receives an automated decline letter with no clear explanation as to why.
A Compliance Officer, who needs auditable logging and explicit proof that AI-assisted decisions adhere to regulatory standards and avoid bias.
The Challenge
How might we build interactive trust mechanisms into AI solutions — such as explainability dashboards, audit logging, human-in-the-loop controls, or bias detection — to ensure responsible adoption?
What Success Could Look Like
- Clear, human-understandable visual explanations for AI output
- Built-in human-in-the-loop fallback and override controls
- Transparent governance and audit logging frameworks
- Increased employee and customer confidence in AI-driven outcomes
📈4. From Pilot to Enterprise AI
AI Adoption & Strategy
Why This Matters
Many insurers have built impressive AI proofs of concept, but moving from isolated experiments to production-grade enterprise software remains a hurdle. Scaling AI requires robust technical foundations: standardized testing, modular architectures, monitorable agent frameworks, and clear measurement of operational ROI.
Example Personas
A Department Manager, who wants to roll out AI tools across their operational team safely without disrupting daily activities.
An Enterprise Architect / Executive, who needs clear telemetry, safety guardrails, and measurable business metrics before approving production deployment.
The Challenge
How might we design production-ready frameworks, developer tooling, or enterprise integration layers that allow insurers to scale AI experiments safely, reliably, and measurably?
What Success Could Look Like
- Plug-and-play architecture for existing enterprise tools
- Standardised evaluation, monitoring, and safety guardrails
- Clear telemetry and real-time business value dashboards
- Modular, maintainable software design built for scale
🤖5. The AI Co-pilot for Insurance Professionals
Productivity Automation
Why This Matters
Insurance professionals spend significant time searching knowledge bases, analysing lengthy underwriting guidelines, summarising dense claim files, and synthesising policy terms. Rather than replacing human expertise, AI can serve as an active desktop co-pilot — augmenting human decision-making, surfacing relevant context instantly, and allowing experts to focus on high-value, complex cases.
Example Personas
Ciara, a senior claims assessor, spending hours analysing complex, multi-page medical and legal reports.
Darragh, a commercial broker, manually digesting lengthy risk profiles to prepare annual client renewal summaries.
The Challenge
How might we leverage conversational interfaces, custom RAG, or desktop assistance tools to build an intuitive co-pilot that enhances the speed and accuracy of insurance experts?
What Success Could Look Like
- Instant context retrieval and automated document synthesis
- Augmentation (not replacement) of expert decision-making
- Significant reduction in search and synthesis time
- Intuitive, human-centric UI/UX design
💬6. Insurance Without Complexity
Productivity Automation
Why This Matters
Insurance is notoriously complex for both customers and frontline staff. Lengthy forms, obscure terminology, dense policy documents, and repetitive data entry lead to customer drop-off and support call congestion. Modern conversational interfaces, multi-modal vision models, and natural language processing allow insurers to convert complicated interactions into intuitive, conversational experiences.
Example Personas
Mary, who wants a straightforward answer on whether her policy covers storm damage without reading a 40-page PDF document.
Patrick, a customer service rep, who struggles to find precise policy answers while keeping a customer waiting on the line.
The Challenge
How might we use conversational AI, dynamic interfaces, or document vision to make insurance interactions simple, natural, and instantly accessible for both customers and support teams?
What Success Could Look Like
- Simple, conversational, or multi-modal application processes (e.g., photo-to-quote)
- Instant, accurate natural-language answers from complex policy docs
- Elimination of redundant data entry and technical jargon
- Accessible, self-service experiences
🌍7. Reinventing Insurance for the AI Era — Moonshot Challenge
🚀 Open Innovation
Why This Matters
AI is enabling a shift from reactive indemnity — paying out after a loss occurs — to proactive risk management, continuous underwriting, and parametric coverage. If insurance were designed today around real-time data, predictive models, autonomous agents, and ubiquitous sensors, it would look fundamentally different. This challenge encourages teams to look past incremental improvements and design bold, novel models for the future of protection.
Example Personas
A small business owner, who wants continuous risk monitoring and early alerts to prevent loss before a claim ever happens.
A forward-thinking carrier, wanting to shift from an adversarial payout model to an active risk-prevention partner.
The Challenge
If we were designing an insurance product, model, or entity from scratch in an AI-first world, what bold new paradigm would we create?
What Success Could Look Like
- AI-native insurance products (e.g., real-time parametric protection, automated micro-policies)
- Proactive, predictive risk prevention tools
- Entirely new business models or revenue structures
- High-impact, transformative product design and vision
The challenge is open to everyone
You do not need to be an AI expert or have an insurance background. Register individually or as part of a team.
Mark your calendar
Ready to Build Something That Matters?
Bring your skills, curiosity and ideas to the Finance & Insurance stream — and help shape a more intelligent, trusted and accessible future for insurance.
Register for the National AI Challenge 2026 →