The insurance industry is operating in a demanding environment shaped by rising customer expectations, increasingly complex fraud, and tighter regulatory oversight. As agentic AI in the insurance industry gains attention, insurers are under pressure to improve efficiency while maintaining trust, transparency, and accuracy across core functions such as underwriting, claims, and policy servicing. Traditional automation and predictive analytics have delivered progress, but these approaches often remain limited by rule-based logic and continued reliance on human intervention.
Agentic AI is emerging as a practical capability as insurers move beyond experimental use cases. Unlike conventional systems that rely on predefined prompts, agentic AI enables autonomous digital agents that can interpret tasks, plan multi-step actions, operate across systems, and learn from outcomes. This shift from task automation to goal-driven execution is gaining momentum, with 54% of insurance leaders expecting generative and agentic AI to drive the most industry change over the next three years, and 57% prioritising these technologies for investment in 2026.
from reactive tools to proactive agents
Understanding this shift requires a clear distinction between agentic AI and earlier automation approaches. Traditional Robotic Process Automation (RPA) operates through fixed, rule-based scripts, while generative AI chatbots are designed to respond to prompts by generating content or answers.
Agentic AI extends these capabilities by introducing structured reasoning and autonomy. It can translate a high-level objective, such as processing a new policy application, into a sequence of coordinated actions. These actions are executed independently across systems, including core policy platforms and external data sources, with decisions made within defined governance and control frameworks.
For instance, instead of just extracting data from a submission document, an AI agent can manage the entire submission-to-quote process for standard risks. It can identify missing information, initiate requests for clarification, analyse the complete risk profile, and generate a preliminary quote—all while logging its rationale for human review. This allows insurers to move from reactive decision-making to proactive risk management, particularly in complex and high-volume operations.
transforming the insurance value chain
The practical applications of agentic AI are moving beyond theory into tangible operations across core insurance functions. Its impact is most pronounced in several high-value domains.
revolutionising underwriting and pricing
AI agents are transforming underwriting from a manual, document-heavy process into a streamlined operation. They can autonomously extract and validate structured and unstructured data from emails, PDFs, and images to populate systems. By analysing this data against historical patterns and external sources, they generate comprehensive risk profiles and preliminary assessments, highlighting gaps and accelerating turnaround times for human underwriters who can then focus on complex or large-risk cases
enabling autonomous claims processing
Claims processing remains one of the most operationally intensive functions in insurance. Delays, manual reviews, and fragmented data often lead to higher costs and lower customer satisfaction. Autonomous claims processing addresses these challenges by enabling AI agents to manage claims end-to-end. Agentic systems can validate claims data, assess coverage, detect anomalies, and recommend settlements in near real time. AI-enabled claims operations can reduce processing times while improving accuracy in routine and low-complexity cases.
advancing fraud detection and risk management
Fraud remains a persistent challenge for insurers across regions. Traditional detection methods often struggle to keep pace with evolving fraud patterns and organised networks. AI agents analyse large volumes of structured and unstructured data to identify suspicious behaviour, correlate signals across claims and policies, and prioritise investigations. Advanced AI techniques are increasingly effective at detecting complex fraud scenarios that would be difficult to identify through human intervention alone.
personalising the customer experience
Conversational AI agents can handle routine policy inquiries, generate tailored renewal explanations, and support follow-up actions. More advanced agents can proactively reach out to customers with relevant advice or coverage adjustments based on life events inferred from permitted data, strengthening engagement and loyalty.
building a future-ready AI operating model
For senior leaders, the value of agentic AI in insurance lies less in isolated use cases and more in building an operating model that supports autonomy at scale. Real impact comes from embedding AI agents into the core of insurance operations, rather than layering them onto existing processes. This requires a deliberate shift in how workflows, data, and governance are designed across the enterprise.
Several factors are critical to enabling this transition. Reimagining workflows end-to-end allows organisations to move beyond task automation and unlock the full potential of autonomous execution, often requiring closer coordination across underwriting, claims, and policy servicing functions.
Establishing a robust data and governance foundation is equally important, as agentic AI depends on access to clean, integrated data while operating within clearly defined risk, compliance, and ethical guardrails. Finally, strengthening human–AI collaboration remains essential. While AI agents can handle routine decisions at speed, human judgment continues to play a central role in managing exceptions, exercising empathy, and overseeing outcomes. This makes workforce upskilling a priority, enabling teams to train, supervise, and work alongside AI agents effectively.
turning agentic AI into sustained value
As agentic AI adoption accelerates, insurers face an opportunity to move beyond incremental efficiency gains towards more adaptive and resilient operations. Success will depend on aligning technology with operating models that balance autonomy, control, and human oversight. Organisations that take a structured approach to governance, workflow design, and skills development will be better positioned to realise long-term value while managing emerging risks.
Learn how Infosys BPM applies generative AI and agentic capabilities to help insurers build agile, data-driven operations across the insurance value chain.
Frequently Asked Question
How is agentic AI different from RPA or GenAI chatbots in insurance operations?
Agentic AI executes goal-driven, multi-step workflows across systems, not fixed scripts or prompt-based replies.
It can plan actions, resolve missing information, and progress work (e.g., submission-to-quote) within defined governance boundaries and rationale logging.
This enables scalable automation without limiting value to isolated tasks.
What risk controls should insurers require before scaling agentic AI in underwriting or claims?
Implement entitlement controls, human approval checkpoints for material decisions, and traceable decision logs.
These controls protect accuracy, regulatory defensibility, and trust when agents act across core policy and claims platforms.
The result is faster throughput with controlled exposure in high-stakes processes.
Which insurance workflows should be prioritized to prove value while limiting governance burden?
Prioritize high-volume, rules-constrained workflows with clear exception paths.
Examples include routine claims triage, data extraction/validation, and standard-risk underwriting steps where outcomes can be measured (cycle time, leakage reduction, service consistency).
This creates an ROI base before expanding autonomy into complex adjudication.
