Almost everyone in the insurance sector is talking about AI, yet few organisations have managed to deploy it at scale. By 2025, nearly 90 per cent of insurers are expected to implement some form of AI. That figure sounds impressive until you consider that only around seven per cent are likely to move beyond pilot programmes. The challenge is not ambition or budget. According to IntellectAI, it comes down to trust, accuracy, and the often chaotic reality of insurance data.
Human-in-the-loop, or HITL, is critical for establishing that foundational trust. It represents the crucial distinction between AI that performs well in demonstrations and AI that genuinely enhances the work of underwriters and brokers.
The state of human-in-the-loop in insurance AI
Insurance is not lagging when it comes to AI adoption. In 2024, 77 per cent of carriers launched major AI initiatives across underwriting, claims, and operational functions. This surge of activity has propelled the generative AI underwriting market, which is projected to expand from $1.09bn to more than $14bn over the next decade.
Despite this momentum, many teams remain stuck in pilot mode. Deploying AI is straightforward; entrusting it with high-stakes decisions is not. Insurance demands precision. Decisions on risk appetite, coverage interpretation, premium validation, and regulatory compliance cannot tolerate approximations. AI excels at recognising patterns and processing data rapidly, but it struggles with nuanced details buried deep within policy documents.
HITL bridges this divide. By combining AI’s speed with human expertise in insurance, it transforms experimental technology into a dependable, scalable tool. When implemented poorly, it simply creates additional work.
Why traditional HITL models fail underwriters
Many AI validation models marketed as HITL fail to deliver meaningful value. Typically, AI extracts information, flags potential issues, and passes raw data to the insurance team for review.
This approach forces underwriters and brokers to spend their time verifying outputs they cannot fully trust rather than assessing risk. Any time saved initially is lost during this manual verification process.
The consequences extend beyond inefficiency. It slows decision-making, frustrates teams, and undermines AI’s promise of faster quotes.
Insurance-specific AI challenges
Insurance data is notoriously complex. ACORD forms may appear standardised but often contain handwritten notes, endorsements, and carrier-specific variations. Loss runs, schedules, and scanned PDFs can be processed quickly by AI, but humans are essential to interpret what is truly relevant.
Errors in coverage terms, premiums, or sublimits can lead to costly mistakes. AI can suggest decisions on risk appetite and compliance, but human validation is necessary to ensure adherence to underwriting standards and regulatory requirements. HITL in insurance is therefore not optional; it safeguards both the business and its customers.
How IntellectAI’s integrated HITL model works
IntellectAI approaches HITL differently. Human review is conducted within its dedicated operations team rather than by the insurer.
Data is validated, normalised, and reconciled before it reaches clients. Inconsistencies are resolved, missing information addressed, and edge cases managed by specialists with deep industry expertise.
The platform provides real-time quality checks for every document, field-level reconciliation for conflicting information, and continuous model training to enhance AI accuracy over time. HITL is integrated into the core platform rather than offered as an optional add-on.
Measurable benefits of proper HITL implementation
When implemented effectively, HITL delivers tangible benefits. Underwriting cycles can shrink from three to five days to under 24 hours. Productivity can increase by up to 30 per cent. Human validation reduces errors in coverage and premiums, and trust in AI rises, driving adoption rates up to fourfold.
These improvements manifest in cleaner data, faster decision-making, and calmer, more confident insurance teams.
HITL versus full automation
HITL is essential in the early stages of AI adoption. As models mature, some processes may shift to human-in-the-loop, where humans monitor exceptions rather than review every output.
The aim is not to eliminate human involvement entirely but to ensure it is deployed where it adds the most value. AI manages volume while humans exercise judgment.
Read the full blog from IntellectAI here.
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