As agentic AI moves swiftly from concept to practice, spurred by mounting complexity and legacy constraints, it is transforming the work of underwriters, claims teams, and core operations.
In insurance, there’s no shortage of hype around technology. Every year, a new buzzword promises to change everything. Most fade, some stick – and a few rare ones fundamentally reshape how we work.
Agentic AI is one of those rare ones.
Its transformative potential is not about shiny demos or another chatbot answering FAQs. In specialty insurance, the stakes are too high for gimmicks. Our work touches trade, health, infrastructure, and global risk. Mistakes are not only costly – they can ripple through entire economies. A USD 300 million loss can hinge on a single overlooked data point.
That’s why at Tinubu, we don’t talk about agentic AI as an experiment; we build with it, deploy it, and make it part of the way insurers operate today. Far from being a mere theory, agentic AI is already reshaping our industry.
Elevating specialty insurance automation
Specialty lines are scaling fast: credit, political risk, marine, energy, accident, and health. Every segment is under pressure to grow while facing rising volatility and increasingly complex compliance requirements including GDPR/CCPA, DORA/NIS2, the EU AI Act, Solvency II, and IFRS 17.
Underwriters and claims specialists are asked to move faster, handle more data, and deliver flawless compliance. Legacy systems can’t keep up. They slow processes, fragment information, and trap experts in admin instead of judgment. Keeping the lights on and preserving the status quo is no longer good enough.
This is where agentic AI steps in, not to replace people, but to free their time, focus, and energy. Orchestrating multiple AI agents that can plan, reason, and act in parallel delivers something legacy workflows never could: clarity at speed, grounded in real data. Agentic AI doesn’t cut jobs; it cuts through the noise and distractions.
Beyond single AI: Leveraging teams of AI specialists
Most people know AI today as a single model such as ChatGPT, Claude, and others – essentially a ‘smart generalist.’ While useful, these generalist models alone are inadequate for the depth and complexity of insurance. With agentic AI, instead of one brain or one model, you get a team of AI specialists, each with its own task: crawling data, checking compliance rules, analysing risk exposures, and summarising results. They don’t just fetch information; they reason together, re-rank answers using domain-specific logic, and return results that are explainable and auditable.
It’s not magic; it’s engineering, and it is changing the game. Rather than replacing people, it removes friction and allows specialists to focus on judgment.
Unlocking new risk frontiers
Agentic AI accelerates risk assessment by blending exposure data, historical patterns, and real-time feeds. It can simulate ‘what if’ scenarios in minutes, enabling underwriters to test multiple outcomes without delay. QBE’s GenAI ‘Cyber Underwriting Assistant’ cut the time to review broker submissions by 65%, letting underwriters process more submissions while improving risk selection.
It also redefines ‘specialty’. Risks once considered uninsurable, like supply chain fragility, cyber-physical risks, or climate micro-exposures, become possible to underwrite. Beyond efficiency, agentic AI is becoming a powerful tool for pricing and risk selection, strengthening insurers’ role as market makers.
"Agentic AI accelerates risk assessment by blending exposure data, historical patterns, and real-time feeds. It can simulate "what if" scenarios in minutes, enabling underwriters to test multiple outcomes without delay."
Building claims trust at speed
For claims teams, speed is trust. AI agents can scan supporting documents, check SLA compliance, and flag anomalies, cutting closure times dramatically while ensuring fairness and regulatory alignment. For Allianz Partners, AI reduced average A&H claims processing from 19 days to four, with 71% of claims processed in fewer than 12 hours and payments as fast as six hours.
Migration and modernisation fuel business agility
The toughest, least glamorous challenge may be moving from deeply customised legacy systems to modern SaaS platforms. Traditionally painful, expensive, and disruptive, they are now radically different: with multi-agent AI, migrations that once took three years can be done in six months. Integrations that took months can be completed in weeks. Far from theory, this is agility in action
A real-world case: Putting agentic AI into practice
One insurer we worked with faced exactly this challenge: a heavily customised legacy system with thousands of undocumented configurations, customisations, and bespoke workflows. The fear of migration delays – or worse, business interruption – was real.
Here’s how we tackled it: AI agents crawled and catalogued thousands of documents, configs, and support tickets, rebuilding knowledge that had long been lost. The system flagged migration risks and inconsistencies automatically, generating structured, and phased migration plans in minutes – a task that would normally require weeks of consultant work. The AI functioned as a virtual consultant, available 24/7 for migration-related queries. The results were reduced risk, zero disruption, and full ROI visibility – a potent demonstration of how agentic AI moves beyond theory into practical, measurable outcomes.
Beyond efficiency optimisation
Migration may be where agentic AI proves its worth, but that’s only the beginning. The next wave of applications in specialty insurance is already visible:
- Risk early warning: Systems that continuously scan exposures, market signals, and portfolio data to detect risks before they escalate.
- Autonomous monitoring: Credit portfolios monitored around the clock, with AI agents flagging shifts in exposure or compliance in real time.
- Contract intelligence: Automated parsing of complex insurance contracts, highlighting compliance risks and obligations instantly.
- Embedded decisioning: Underwriting and claims augmented by AI that can surface context-specific insights the moment they’re needed.
What’s powerful here isn’t just automation, but augmentation: AI extends the reach of human judgment rather than replacing it.
What it takes to succeed with agentic AI
Deploying agentic AI in insurance isn’t just a question of plugging in new tech. Success comes from orchestrating five foundations together.
Clean, structured, and connected data is the lifeblood. Agentic AI can only be as strong as the sources it pulls from: policies, claims, risk models, external signals. Data quality and governance aren’t optional; they are the starting line.
For underwriters, this creates a new kind of human-AI ‘symbiosis’. Training teams to work with AI outputs, validate results, and build trust in new workflows becomes essential. Culture change is as important as software infrastructure.
Further, multi-agent AI requires infrastructure that can scale, integrate, and stay secure. Model Context Protocols, API-first architectures, and hybrid deployments (cloud plus on-prem) ensure flexibility without compromising compliance.
Agentic AI cannot live in a lab. It must be embedded into daily operations with clear ownership taken in validating outputs, retraining models, and measuring ROI. Governance and accountability make the difference between pilots and production.
Finally, AI must move from proof-of-concept to product, packaging use cases into tools insurers can actually adopt: migration assistants, claims accelerators, and risk early-warning dashboards with clear business value. Without these foundations, AI remains aspirational. With them, it becomes a competitive advantage.
Balancing innovation with responsibility
No technology is without risks. Agentic AI introduces legitimate concerns: bias, overreliance, and explainability. Independent research shows momentum and caution. Capgemini finds only 14% of organisations have AI agents partially or fully deployed, and executive trust in fully autonomous agents has fallen from 43% (2024) to 22% (2025), underscoring why transparency and human-in-the-loop controls matter.
What if overreliance on AI becomes the next systemic risk category in specialty insurance? To avoid that outcome, our approach rests on three principles:
- Domain-first intelligence: Our systems are trained and re-ranked with insurance logic, not just generic data.
- Transparency by design: Outputs are traceable, with clear links back to the underlying data sources.
- Human in the loop: Underwriters and claims specialists remain the decision-makers. AI supports, but doesn’t override, their judgment.
Insurance-native intelligence
The temptation with AI is to see it as ‘just another tool’. In reality, we’re talking about something bigger: the foundation of insurance-native intelligence systems, platforms that understand our processes and risks and reason with our data. That’s why, at Tinubu, we see agentic AI not as a feature but as part of the future fabric of specialty insurance.
Looking ahead, agentic AI could become an industry-wide utility layer, the backbone of carrier and broker ecosystems. Consider how Google and Coinbase partnered on an agentic payments protocol (X402), envisioning AI agents conducting transactions directly with one another. Could insurance agents interact the same way, without human intermediaries? The precedent already exists: in high-frequency trading, over 50% of stock transactions are executed without human involvement. Insurance could be next.
A quiet revolution
By turning complexity into clarity, bottlenecks into breakthroughs, and operations into opportunities, agentic AI is ushering in a new era for a century-old industry. Underwriters gain time to focus on nuanced risk calls. Claims teams resolve faster and build stronger trust. Executives gain transparency and speed without sacrificing compliance. And technology leaders finally see a way out of the endless cycle of legacy headaches.
To be clear, success in this domain requires more than algorithms. It takes data, people, architecture, operating discipline, and the courage to productise AI into tools that deliver real outcomes. The frontier is here. The question is no longer if agentic AI will reshape specialty insurance, but how fast we are ready to embrace it.
Agentic AI is here. The differentiator is how you deploy it.
Explore the operating models, guardrails, and real use cases shaping specialty insurance today.
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