This piece is by Srividya Kannan, CEO, Avaali Solutions
An AI agent processes a policy amendment in seconds. Three months later, the customer disputes the revised premium. The insurer knows what the system did but cannot adequately explain why.
Was the correct policy wording applied? Were the latest underwriting rules considered? Was an exception overlooked?
This is an emerging risk of agentic AI in insurance: organisations may be accelerating decisions faster than they are building the ability to defend them.
The promise of agentic AI is compelling. Unlike traditional automation, which typically follows predefined steps, AI agents can interpret information, coordinate tasks across applications, initiate actions and manage exceptions within defined boundaries.
For insurers and brokers dealing with complex documentation, fragmented systems and demanding customers, the opportunity is substantial.
But a fundamental question remains.
When an AI agent makes a mistake, who is accountable for the consequences?
The Hidden Cost of Faster Decisions
Consider a routine mid-term adjustment to a commercial insurance policy.
A broker requests a change in coverage. The insurer must validate the existing policy, assess the requested modification, apply underwriting rules, calculate any premium adjustment, obtain necessary approvals and communicate the outcome.
Agentic AI could coordinate much of this process. One agent interprets the request. Another retrieves policy information. A third checks business rules. Others prepare the endorsement and customer communication.
What previously involved multiple handoffs could potentially be completed much faster.
But speed alone does not establish business value.
If an agent applies outdated underwriting rules, overlooks an exclusion or issues incorrect documentation, the resulting investigation, rework and customer dispute may cost significantly more than the time originally saved.
The true cost of an AI workflow is not what it costs to execute. It is what it costs to correct when something goes wrong.
Insurers must therefore evaluate automation not simply through processing speed or labour savings, but through the total cost and quality of the resulting business outcome.
The Accountability Challenge Extends Beyond Insurers
Brokers face similar challenges.
Imagine an AI agent comparing quotations from multiple insurers, analysing coverage terms, identifying exclusions and preparing a recommendation for a commercial customer.
The agent may produce a convincing comparison within minutes. But what happens if it overlooks a material exclusion or relies on an outdated quotation?
A faster recommendation is of little value if the customer subsequently discovers that an important risk was not adequately covered.
The same concerns arise in claims handling, renewals, policy servicing and customer communications.
This does not mean organisations should exclude AI from these activities. It means organisations must distinguish between automating information processing and delegating consequential business judgment.
AI can accelerate the work. Accountability for the outcome must remain clearly established.
Three Tests of Trustworthy Agentic AI
Before deploying agents into business-critical insurance workflows, organisations should ask three questions.
First, can the agent act correctly?
An AI agent must operate using reliable information, appropriate access permissions and approved business rules.
If policy details are outdated, customer information is incomplete, or systems contain conflicting records, even a sophisticated agent can produce an incorrect outcome.
Reliable integration with authoritative business systems, data validation and clearly defined operating boundaries are essential.
Second, can the organisation explain its actions?
When a decision is challenged, the insurer or broker should be able to reconstruct the relevant evidence, rules, actions, exceptions and approvals.
This requires more than conventional transaction logging.
Agentic workflows should preserve appropriate decision records, including the information sources used, applicable business rules and material actions taken.
The objective is not to expose every internal computation of an AI model. The goal is to ensure consequential business outcomes can be reviewed, explained, and defended.
Third, can the agent be stopped or corrected?
An intelligent system must recognise when it should not proceed.
Conflicting policy information, unusual claim circumstances, missing approvals or material changes in coverage should trigger appropriate controls.
Organisations need clear escalation paths, human intervention mechanisms and ways to correct or reverse actions where feasible.
An agent that cannot be effectively controlled may introduce more operational risk than the manual process it replaces.
Why Orchestration Matters More Than Autonomy
Insurance operations rarely run on a single technology platform.
Policy administration, claims management, document repositories, finance systems and broker communications frequently operate across disconnected applications.
An AI agent may correctly interpret a customer request but retrieve outdated information from another system. It may prepare an accurate endorsement before the necessary approval is recorded. It may update one application while leaving another unchanged.
Each individual task may appear successful, even though the overall process has failed.
This is why agent orchestration matters.
Organisations need workflows that coordinate agents, enterprise applications, deterministic business rules and human approvals within a controlled operating framework.
Not every activity requires the same degree of autonomy.
Document classification, information retrieval and routine validation may be highly automated. Ambiguous coverage interpretations, material policy changes and sensitive claims decisions may require stronger controls and professional judgment.
The objective should not be to eliminate people from insurance operations. It should be to apply human expertise where it creates the greatest value.
Measuring the Right Return on AI
The business case for agentic AI is frequently expressed through productivity improvements: fewer manual activities, faster turnaround and lower processing costs.
These are important, but incomplete.
An insurer that processes claims faster while increasing disputes, rework or compliance exceptions may not have improved its economics.
More meaningful measures include end-to-end processing cost, accuracy, exception rates, rework, customer outcomes, and the ability to reconstruct consequential decisions.
Organisations should establish these measures before deployment and assess whether improvements hold as transaction volumes rise.
An agent that saves minutes at one stage but creates hours of downstream investigation is not delivering meaningful transformation.
The Next Competitive Advantage Is Accountability
Insurance has always been built on trust.
Customers trust insurers to honour commitments. Brokers trust carriers to administer policies accurately. Regulators expect appropriate governance and accountability.
Agentic AI does not change these expectations. It makes meeting them more technically demanding.
The industry does not need AI agents that simply work faster. It needs agents that operate within clearly defined controls, coordinate reliably across systems and produce outcomes that organisations can confidently defend.
The winners in agentic AI will not necessarily be those with the most autonomous systems. They will be those with the most accountable ones.
Because in insurance, a decision is only as valuable as the organisation’s ability to stand behind it
Be the first to comment