Insurance CIOs are not short of AI ambition. They are short of clarity on which use cases will deliver measurable value within 12 months, and which ones remain expensive experiments that collapse under operational reality.
In its AI Use-Case Assessment for P&C and Life Insurance Industry, Gartner evaluates 18 prominent AI use cases and scores each on two axes: value (revenue, efficiency, risk management, nonfinancial benefit) and feasibility (technical readiness, internal adoption, external environment). The result is a prioritisation framework that separates likely wins from calculated risks and marginal gains.
FintechOS operates in several of the use-case categories Gartner identifies as likely wins. What follows is a practical companion to the assessment, focusing on where AI delivers the most tangible value in underwriting, claims and policy operations, and how FintechOS’ AI-fluent operating model maps to those priorities.
What Gartner says about AI use cases in insurance
Gartner core message: AI value in insurance is real but unevenly distributed. Not every use case offers the same return, and feasibility varies significantly depending on technical maturity, internal readiness, and the regulatory environment.
Gartner groups the 18 use cases into three categories:
Likely Wins – medium-to-high value combined with medium-to-high feasibility. These are use cases where most insurers can expect positive returns. Examples include submission and intake processing, underwriting risk assessment, claims predictive analytics, AI-assisted sales, AI for legacy modernisation, fraud analytics and detection, and customer service virtual assistants.
Calculated Risks – medium-to-high value but lower feasibility, meaning the payoff is real but implementation is harder. Examples include autonomous underwriting, autonomous claims processing, underwriting virtual assistants, customer-facing chatbots, and vehicle damage assessment.
Marginal Gains – lower value with variable feasibility, making them more selective investments. Examples include AI agents for HR/recruiting and personalised policy generation.
Three observations stand out to us from the assessment:
- The highest-scoring use cases are infrastructure plays, not AI plays
Gartner likely wins share a pattern. Submission intake. Underwriting risk assessment. Claims predictive analytics. Legacy modernisation. These depend on structured data flows, well-understood decision logic and clearly bounded automation scope. They work when the foundation underneath them is solid. The AI itself is almost secondary to the data architecture and process orchestration that enable it. - Underwriting is where the strongest signal sits
Two of Gartner top-rated use cases, underwriting risk assessment and submission intake processing, are essentially two halves of the same workflow. Intake converts unstructured broker submissions into structured, actionable data. Risk assessment applies decisioning logic to that data consistently and at speed. Gartner notes that new data sources are emerging and modelling sophistication is improving as insurers build greater data science maturity. The value is clear, and the feasibility is high where the underlying infrastructure supports it. - Governance and organisational readiness are the real feasibility constraints
Across the calculated risks category, Gartner consistently identifies governance, explainability and cultural readiness as the primary barriers – not technical capability. Autonomous underwriting and autonomous claims score well on value but low on feasibility precisely because regulators consider them high-risk, companies lack trust in the data, and the market is fragmented. This is not a technology problem. It is an operating model problem.
The use cases that matter most for underwriting and claims operations
FintechOS does not play in all 18 use cases. We are not a climate risk modelling vendor, a vehicle damage assessment platform, or an HR screening tool. Our focus is the product operations layer for insurance – where several of Gartner highest-scoring use cases converge.
The use cases most relevant to FintechOS’ platform:
Submission and intake processing (Likely Win)
Gartner describes AI systems that take incoming content for underwriting, complaints or claims, extract data, check validity and input into required systems to expedite work. This maps directly to how FintechOS’ Agentic Workforce handles document intake within Policy Admin workflows – automated extraction, validation and system population with human oversight for exceptions.
Underwriting risk assessment (Likely Win)
Gartner describes the use of AI tools to support predictive risk modelling to assess individual policy risk or portfolio risk. FintechOS’ approach deploys specialist agents that handle discrete steps: data normalisation, risk indicator cross-referencing against appetite rules, and exception flagging for human review. Each agent operates within defined boundaries rather than attempting to replicate an underwriter’s full judgement.
AI for legacy modernisation (Likely Win)
Gartner rates this highly because AI can accelerate migration of business logic, data mapping and system documentation from legacy platforms. FintechOS’ architecture is designed to sit across existing systems – providing a modern product operations layer that unifies data and processes while legacy infrastructure is gradually retired. You don’t have to finish modernising before you start getting value from AI. The two run in parallel.
AI-assisted sales (Likely Win)
Gartner describes platforms for insurance agents or distributors to access content and capabilities supporting sales – recommendation engines, next best action, product bundling. FintechOS’ embedded distribution capabilities and Policy Admin layer enable this by giving distribution partners a configured, AI-supported interface for matching customers to products within the insurer’s own risk and eligibility framework.
Underwriting virtual assistant (Calculated Risk)
Gartner notes that solutions in the market today are fragmented and unable to support fully autonomous claims, and that buyers would need to integrate multiple solutions. FintechOS’ platform approach addresses this by providing a unified product operations layer where claims workflows, predictive models, and automation rules are configured and governed from a single system rather than stitched together from disconnected point solutions.
Where we believe FintechOS aligns to Gartner assessment
Gartner scoring framework evaluates use cases across seven dimensions. Here is how FintechOS approaches the ones most relevant to our platform.
- AI that supports underwriting without replacing human judgement
Gartner is clear on autonomous underwriting: regulators consider this high-risk, and the market is not ready for fully autonomous processing except in commoditised lines. Companies in complex product lines are reluctant to fully automate end-to-end processes due to cultural barriers and concerns about legal risks associated with inaccuracies.
FintechOS’ approach is consistent with this. The Agentic Workforce handles bounded tasks: submission intake, data extraction, risk indicator analysis, portfolio pattern detection. Decisioning remains with underwriters, governed by the insurer’s own appetite rules, with human oversight at every material step. This is Trustworthy AI by Design in practice – not autonomous underwriting, but augmented underwriting with full governance. - Intake processing as a platform capability, not a standalone tool
Gartner notes that insurers want straight-through processing and faster processing cycles, which start with the processing of incoming documents and content. Paired with OCR, content can be automatically verified and entered into internal systems for claims FNOL or underwriting.
FintechOS embeds intake processing directly into Policy Admin workflows. Document extraction, classification, validation and system population are part of the governed product operations flow, not a separate tool that requires manual handoff into downstream processes. - Legacy modernisation through continuous evolution, not rip-and-replace
Gartner identifies AI for legacy modernisation as a likely win because it helps insurers overcome obstacles that previously made modernisation unfeasible. The internal feasibility is high because it directly addresses the legacy dilemma that blocks business growth and agility.
FintechOS is built for this. A core-agnostic product operations layer that connects to existing policy admin, claims and distribution systems while providing a modern foundation for AI-enhanced workflows. Insurers can modernise incrementally, one product line, one workflow at a time, without the risk and cost of wholesale platform replacement.
- Feasibility through platform architecture, not bespoke integration
Gartner feasibility dimensions consistently highlight integration complexity and market fragmentation as primary constraints. Autonomous claims, for example, scores lower because buyers would need to integrate multiple solutions to enable the vision.
FintechOS addresses this structurally. Data Core provides an API-first integration layer that connects AI capabilities to existing cores, distribution platforms, data providers, and specialist services. The goal is to reduce the integration overhead that Gartner identifies as a key feasibility barrier, so that deploying AI-enhanced workflows does not require rebuilding the surrounding infrastructure.
- Governance as an accelerator, not a constraint
Gartner repeatedly flags governance, accuracy and regulatory scepticism as feasibility constraints – particularly for autonomous processing and customer-facing AI. Regulators have mixed views on how AI can be used to make claims or underwriting decisions. Errors may lead to penalties, fines and brand injury.
FintechOS builds governance at the platform level. SOP-driven controls govern what AI agents can and cannot do. Every action is auditable. Human oversight is structurally required for material decisions. This is not AI deployed first and governed later. Governance is the operating framework – and carriers who have it in place can deploy AI faster because the regulatory prerequisites are already addressed.
A practical framework for prioritising AI investments
Gartner assessment provides a useful starting point. Here is how we see insurers translating it into action:
Start with likely wins that compound
Submission intake and underwriting risk assessment are high-feasibility use cases that directly enable higher-value ones (autonomous underwriting, claims automation, personalised pricing). Deploying them first creates a foundation rather than a standalone pilot.
Focus on the operating model, not the model
Gartner notes that feasibility depends on internal readiness and stakeholder trust as much as technical capability. AI that is embedded into existing workflows, governed by existing policies, and operated by existing teams is more feasible than AI that requires new processes, new roles, and new governance from scratch.
Pressure-test vendor claims against Gartner feasibility dimensions
Ask: what is the technical integration requirement? What internal change management is needed? What external dependencies exist (regulatory clarity, partner adoption, data sharing)? If a vendor cannot answer these clearly, the feasibility is lower than their marketing suggests.
Treat governance as investment, not overhead
Gartner flags governance as a feasibility barrier. But insurers that have governance frameworks in place (explainability, audit trails, deterministic controls) can deploy AI faster because they have already addressed the regulatory prerequisites. Governance does not slow you down. Lack of governance does.
Download the Gartner AI Use-Case Assessment (licensed copy)
If you want to go deeper into the scoring methodology and review all 18 use cases in detail, you can download the licensed report from FintechOS.
“Gartner, AI Use-Case Assessment for P&C and Life Insurance Industry, Kimberly Harris-Ferrante, 12 November 2025
GARTNER is a trademark of Gartner, Inc. and/or its affiliates.”
Closing thought: AI value comes from operational readiness, not model sophistication
From our understanding, Gartner assessment makes one thing consistently clear: the use cases that score highest on both value and feasibility are not the most ambitious. They are the ones where AI is embedded into existing workflows, governed by existing policies, and focused on bounded tasks that augment human decisions rather than replacing them.
That is the direction FintechOS has been building toward with Policy Admin and the Agentic Workforce. An AI-fluent operating model where AI capabilities – intake processing, risk modelling support, knowledge discovery, workflow assistance – are embedded directly into product operations, governed by deterministic controls, and deployed incrementally without requiring insurers to rebuild their infrastructure.
If your AI roadmap includes moving beyond isolated pilots toward an operating model where AI is structurally integrated into how you underwrite, administer and distribute insurance products, we are happy to share how that works in practice.
FintechOS is used by 60+ financial institutions globally, with Policy Admin and product operations deployments across North America, the UK, and Europe, including customers such as Vibrant Credit Union, Hanscom Federal Credit Union, Vernon Building Society, Admiral Money, Bankinter, Groupe Société Générale, TBI Group, and ProCredit Group.
