- Agentic AI in specialty insurance will likely use a multi-agent approach with multiple specialist models rather than a single-model interface, increasing efficiency.
- The future of agentic AI is shifting from merely assisting tasks to achieving true workflow orchestration, actively running processes end-to-end under human governance.
- Despite automation, human insight and judgment remain critical for high-value or ambiguous cases, while AI agents can handle repeatable, tedious tasks.
Mythbusting
Myth: Agentic AI in insurance will use a single-model interface
Reality: A multi-agent approach has been viewed increasingly favourably. Agentic AI will deploy multiple specialist agents: one fine-tuned for large-language models (LLMs), one fine-tuned for reading policy wordings, one for scanning medical reports, one as a rule engine, and so on. This approach can allow for far more efficiency than any single model.
Myth: Generative AI (GenAI) will overrule the need for human insight
Reality: If it’s repeatable, tedious, grunt work, AI agents can do the heavy lifting; if it’s material, humans can decide. Underwriting in specialty lines has long relied on expert judgement of data; it’s just the data gathering process which will be sped up.
There are a few concrete examples of GenAI solutions producing efficiency gains in the industry. For example, a McKinsey report found that an insurer that used AI to provide personalised quotes for prospective customers significantly improved customer satisfaction and freed up time for employees. Another insurer using AI for claims processing cut down the time and cost of liability assessments and made it far more likely that a claim was sent to the correct team for processing.
In both of these cases, the benefits of AI were in enabling humans to focus on high-value tasks, while cutting down on mistakes that cost the company and its employees time and customers.
Myth: AI falls short in more complex casualty claims
Reality: Triage tools assist here, using predictive and generative AI to score claim severity. These tools sift through notes and legal documents to flag high-risk claims. Similarly, AI fraud algorithms, which scan claims for anomalies, ensure genuine claims get paid faster: removing lengthy investigations for simple cases, a huge bottlenecArtificial intelligence (AI) is moving at such breakneck speed that we get a sense of always being one step ahead of the future; for those of us who aren’t developers and software engineers, it becomes nearly impossible to perceive where the world of AI is going to go.
As a result, when trying to conceptualise what AI will look like in the next five, ten, 25, 100 years, there is a trend to just imagine that the future of technology will look like the present, but… more.
What’s around the corner?
The Tinubu report proposes that agentic AI in specialty insurance will shift from task-level assistance to true workflow orchestration – where agents don’t just support processes but actively run them, end to end, under human governance. The next wave won’t be defined by bigger models or flashier interfaces; it will be defined by agents that can reason across systems and collaborate.
One imminent development is portfolio-level intelligence. Today’s early-warning pilots focus on individual buyers or contractors, but the next iteration will allow agents to spot concentration risks, cluster emerging exposures, and propose portfolio adjustments long before renewal season, with a view of portfolio health that comes alive with hourly updates.
Agents are also predicted to grow further embedded into operational infrastructure, interacting with core platforms (policy admin, claims, finance) through governed application push interfaces (APIs). In practice, this means underwriting agents could draft an endorsement, verify its compliance with both internal rules and treaty restrictions, check capacity, run pricing scenarios, and push the recommended action directly into the workflow queue. There will be no excuse for a lack of human oversight on complex tasks: “I didn’t have time” will leave the corporate vocabulary.
Scenario-driven insights are also gaining traction, the report highlights. Instead of asking, “What happens if X occurs?”, underwriters will increasingly be told what events matter, why, and where to focus attention. Finally, expect governance frameworks to mature quickly. As agents take on more autonomous actions, carriers will need robust audit trails, explainability layers, and override mechanisms, and regulation will need to move astride carriers to avoid the financial crime that can seep in through the cracks.
But as AI grows more embedded, so too does the risk surface: dynamics in algorithmic trading and early e-commerce taught us that when too many systems behave similarly, and act simultaneously, small signals can spiral out of control. Who’s to say this won’t happen in insurance?
It’s already happening: Deloitte’s recent report for the Australian government, with fabricated citations due to unvetted model outputs, elucidated how easily subtle model flaws can slip into high-stakes work. There’s no point in practising to run without the track laid with robust provenance tracking and version control. AI agents do what you measure, not what you mean, so alignment risk could also garner misaligned strategic or ethical goals.
And as we’re seeing in casual, consumer AI use, there is a risk of overreliance and human passivity as well. Automation can erode core expertise, leaving operators unprepared if – and when – systems fail. Maintaining human judgement is absolutely critical.
Present solutions for future risks
Managing the attendant risks requires a layered, pragmatic framework that keeps humans actively involved, ensures transparency at every step. Automate boldly, but govern wisely, as a mantra
The report highlights keeping humans involved in code. Routine, low-impact cases can be handled end-to-end by agents, but anything involving high value, novelty, or ambiguity must pause for human judgment. This avoids the ‘autopilot paradox’ seen in aviation, where excessive automation slowly erodes essential skills.
Equally important is provenance and explainability by default: each AI decision should carry the data used, the rules applied, the model version, and a plain-English rationale. Before deploying agents in live workflows, the report continues, insurers should deliberately try to break them to reveal failure modes. This is textbook for avoiding brittle behaviour, such as an agent over-optimising a proxy metric or blindly trusting another agent’s faulty output.
Overall, however, governance must borrow from financial controls: segregated duties, auditable trails, clear accountability, kill-switches, and hardened security.
The future of AI will resemble the world of technology in insurance: but certainly, will be more than just a scale-up. For this reason, speciality insurance must keep a careful hand on the wheel when driving into new, tech-first terrains.
To find out more, read our latest whitepaper, ‘From buzz to boardroom: how agentic AI redefines specialty insurance strategy’, click here.
