If 2025 was a year of learning about artificial intelligence and notching early wins then 2026 might well be the year of putting knowledge gained into action to execute at scale and develop best practices for driving greater ROI while breaking new ground for creating value.
In meaningful ways, insurers shifting their organizational mindset from “Can we trust this?” to “How fast can we integrate this safely and effectively?”
These companies’ leaders already know that the next wave of competitive differentiation will come from operationalizing AI into the broader business ecosystem with measurable, auditable results.
In the coming 12 months – and beyond – insurers will coalesce AI transformation efforts and resources in underwriting, claims, policy servicing, and customer experience enhancement applications and expand them into other areas, like actuarial analysis, compliance and audit preparation, finance and accounting workflows, producer management, and even talent development.
Though still in the evaluation stage, such initiatives reflect a growing recognition that AI’s impact will ultimately extend across the entire insurance enterprise.
10 AI in Insurance Predictions for 2026
Here are some predictions about how the most successful insurance businesses will use 2026 to improve competitiveness, address talent shortages, and strengthen operational resilience through expanded use of AI.
1. AI will shift from curiosity to a core capability
In 2025, insurers were taking formative steps toward making AI a core operational capability rather than an isolated experiment.
2026 will mark a shift from AI readiness to AI reliance. Mid-tier and regional insurers, not just the largest carriers, will begin embedding AI, unlocking speed, precision, and transparency across the value chain, from submission intake and loss run processing to claims indexing and straight-through processing to policy servicing.
Insurance’s AI spend is expected to grow by more than 25% in 2026, according to industry forecasts. Leadership buy-in will become a critical success factor – executives who move beyond experimentation to establish dedicated AI centers of excellence and cross-functional governance will see measurable impact.
Another success factor will be insurers moving beyond exploring what AI can do and beginning to develop the operational muscle required to scale it responsibly, profitably, and find workflows where AI initiates work automatically.
2. Trust will be built on transparency – responsible AI and explainability are no longer goals but baseline expectations
The biggest takeaway from many insurers’ vendor evaluations was clear: transparency matters. Carriers put black-box systems under the microscope to ensure any systems used were explainable, auditable, and aligned with evolving regulatory expectations.
Here are three reasons why this diligence will pay off in 2026, as AI transparency becomes a more attainable objective:
- Increased focus on algorithmic transparency and bias mitigation will reward insurers prepared to meet stricter requirements.
- Easier-to-explain AI models will enter production in pricing and underwriting – even as many companies continue relying on older actuarial methods.
- Carriers that initiated governance-first AI programs in 2025 will be able to leverage them to win trust from customers, regulators, and partners.
3. Data will be your competitive edge
The 2025 focus on vendor vetting underscored for insurers that “data is destiny.” In 2026, this maxim will direct how risk is priced, predicted, and prevented.
For example, insurers will move beyond evaluating risk to actively influencing it through IoT and telematic device data, which will deliver real-time insights about insureds’ behaviors. Predictive risk models that drive preventative actions (e.g., “cyber hygiene” alerts and property maintenance reminders) will engage policyholders as active partners in risk reduction. Also, dynamic pricing and even parametric models will create opportunities for insurers to more efficiently deliver customer-centric solutions.
An interesting trend helps to explain this shift. According to the LexisNexis 2025 U.S. Auto Insurance Trends Report, distracted driving violations have increased by 50%, highlighting emerging risk patterns that traditional pricing processes struggle to capture. AI will continue to transform the underwriter’s toolkit as a risk-shaping platform, allowing faster, fairer, and more context-aware decisions.
4. Claims service will take on a whole new look – claims processing in real-time will become the standard
In the Roots “State of AI Adoption in Insurance 2025” survey, a majority of claims professionals identified improving processing efficiency and reducing claims cycle time as core goals. After working with vendors to conduct proof-of-concept or pilot programs for automated FNOL and document-extraction in 2025, insurers made significant progress toward delivering faster, fairer, and more consistent decisions.
For 2026, many carriers will be ready to scale AI fully into production to enable automated real-time claims ecosystems that combine triage and damage assessment functions, automated payment triggers for simple claims, and human-in-the-loop (HITL) oversight for complex cases.
This hybrid approach developed in 2025 will define success in 2026, allowing AI agents to manage volume and precision while adjusters focus on tasks demanding negotiation skills and empathy to deliver greater value to the customer. In 2026, claims teams will intensify their focus on responsiveness and transparency to deliver better customer experiences – strengthen customer trust and retention – and AI will be at the center of this transformation.
5. AI will empower smarter reinsurance and capital strategy
As insurers learned in 2025, clean, explainable data models do more than improve operations; they enhance capital strategy and resilience.
In 2026, this enhanced data position will have a ripple effect in key areas. In the reinsurance arena, transparent model documentation and bias treaty negotiations are important. The development of parametric and event-triggered payouts will accelerate, supported by AI’s ability to incorporate data from satellite readings and other sources. And AI-driven risk simulation will reshape portfolio management and capital efficiency, turning insights from 2025’s test beds into actionable capital strategies.
Already, Swiss Re, Munich Re, and other global giants have invested in model transparency and risk frameworks. For 2026, reinsurers expect these practices to strengthen the insurance value chain as cedents and brokers align their data standards to support automated treaty structures.
AI’s impact on the reinsurance layer will be profound, improving not only how risk is priced but also how it is transferred, diversified, and capitalized. The carriers that use AI to link operational data to capital performance will gain a true strategic edge in 2026.
6. Users will want AI liability and governance protection products
2025 was filled with difficult questions during vendor vetting: “Who owns the decision when AI is wrong?” How are your models validated and updated?” “What is covered by a vendor when actions executed by AI cause harm?”
For example, an insurer uses an AI agent to automatically issue certificates of insurance, but a data synchronization issue causes a COI to reflect higher limits than the policy actually provides, and it is issued without human review. When a loss occurs, the discrepancy triggers a coverage dispute, leaving the insured exposed to contractual penalties, project delays, and out-of-pocket costs.
Insurers that established AI governance frameworks in 2025 are entering 2026 better equipped to define, underwrite, and price these novel exposures, providing answers with innovations in AI liability coverage, model governance protection, and more precise and expansive technology assurance riders. Such offerings will be designed not only for policyholders using AI but also for insurers and AI developers and insurtechs themselves to protect against operational losses, data drift, model bias, or AI-driven errors within their own environments.
A growing number of carriers that are exploring self-insurance structures or reinsurance layers specifically for AI-related operational risk will be central to how cyber liability continues its growth from an emerging niche product into a standard coverage line, evolving to fit the nature of AI risk across insurance operations.
7. AI agents will be fully embedded and continue to grow in scope
Insurers in 2025 widened the scope of experimentation with embedded and micro-duration products such as travel add-ons and gig economy coverage. In 2026, expect successful models from these tests to be ready to scale in 2026.
With AI-driven underwriting and instant pricing, carriers can now confidently offer coverage in context – at the point of need, and for the duration required, like retail and mobility platforms being able to “bake in” frictionless coverage. or faster, leaner, more personalized small/medium enterprise (SME) coverage to boost small commercial carriers’ profitability.
Analysts predict the global embedded insurance market could surpass $180 billion in gross written premium by 2026, driven by AI’s ability to contextualize risk and deliver instantaneous quotes. For insurers, embedded distribution is no longer experimental, but a critical new growth channel.
In 2026 and beyond, carriers that modernize their technology stacks with AI will integrate seamlessly into partner ecosystems, increasing their capacity to personalize coverages, reduce acquisition costs, and expand reach without adding operational complexity.
8. Teams will be getting creative with talent through restructuring, upskilling, and recruitment
The emergence of AI partly results from a long-standing insurance talent shortage, as a large contingent of insurance’s experienced workforce nears retirement age, with too few new entrants prepared to replace them. Automation will help fill capacity gaps, but it cannot replace expertise or industry knowledge.
The needed influx of Gen Z and even Gen Alpha talent expects flexibility, growth opportunity, and access to modern tools. Yet paradoxically, many Gen Zers in insurance are not being encouraged to use AI in their roles.
While insurers pursue their AI transformations, they will also rethink outsourcing. 2026 is shaping up to be the year when traditional labor-based BPO models evolve as these outsourcing vendors embed AI and analytics into their own services. A sizeable portion of work farmed out in-house as AI automation capabilities increase, and insurers seek more control and faster integration.
Success in 2026 will rest on insurers investing in AI, pairing seasoned experts with data specialists, modernizing work environments, and using AI agents to eliminate repetitive work. Partnerships with on-campus insurance organizations (e.g., Gamma Iota Sigma) will engage digital natives to ensure a future AI and professionals who are prepared to use it effectively.
9. AI agents will own tasks end-to-end
Early chatbot implementations promised frictionless service but often disappointed both customers and employees by generating static, text-based interactions while actually not executing any real work. The next phase of insurance’s AI evolution will be powered by intelligent digital systems designed to perform complex tasks: AI agents.
Unlike chatbots, AI agents can perform tasks end to end, including submissions, many types of claims, policy system updates, communications/correspondence, and escalating exceptions to human experts. As connected, auditable, and domain-trained AI agents become embedded across underwriting, claims, billing, and service operations, insurers will see meaningful improvements in accuracy, cycle time, and consistency.
Analysts project that by late 2026, more than 35% of insurers will deploy AI agents across at least three core functions, cutting processing time by up to 70%. For the first time, AI will evolve from an informational assistant to a true operational partner.
Insurers that combine this new agent technology with the governance, data, and operational discipline established in 2025 will be the ones to unlock true enterprise-scale performance and measurable ROI in 2026.
10. There will be new challenges ahead in 2026
The progress made in 2025 will not eliminate the risks ahead. 2026 will bring new challenges, including:
- Regulatory tightening on AI fairness and explainability
- Cyber threats specifically targeting AI models and pipelines
- Model drift and hallucination risks as more generative AI tools go live
- Data access limitations due to privacy laws
- Public trust risks if AI decisions continue to be seen as biased or opaque
Carriers that spent 2025 building audit trails, governance boards, and fallback controls will be best positioned to withstand these pressures.
Best Practices for Successful AI Project Implementation and Expansion in 2026
Whether your organization is still formulating its AI strategy or scaling AI deployments in production, the priority is the same: Move with purpose, structure, and measurable accountability.
Key actions:
- Operationalize your AI governance framework. Activate 2025’s policies and committees as functioning structures guiding day-to-day decisions, model updates, and compliance oversight.
- Scale proven pilots into enterprise programs. Expand early successes in common AI use cases – FNOL, loss runs, policy endorsements, COI automation – into broader operational use where risk and ROI are both well understood.
- Invest in workforce transformation. Prepare teams to thrive alongside AI agents by embedding AI literacy, change management, and continuous learning across every business unit.
- Measure impact beyond productivity. Track performance across accuracy, compliance, customer satisfaction, and speed to resolution. Use total cost of ownership (TCO) and ROI frameworks to communicate enterprise value to leadership.
- Close the talent gap proactively. Collaborate with talent pipeline partners to build internship and mentorship programs, and AI education programs to attract and retain the next generation of insurance professionals.
- Continuously evaluate vendors and models. Treat AI due diligence as an ongoing discipline to ensure every model remains secure, explainable, and bias-tested.
Reinvest efficiency gains. Apply reduced overhead expenses from AI automation to sustain long-term competitiveness via innovation incubators, AI skill development, new use case exploration, etc. In 2026, success will increasingly be determined by insurers’ capability to scale responsibly, measure relentlessly, and build cultures ready for continuous evolution.
Turning Insight into Measurable Impact in 2026
2025 was a year of exploration and experimentation, in which insurers that tested solutions, built knowledge and set operational guardrails to define responsible adoption in the years ahead.
2026 is the next phase of a voyage, where progress outweighs perfection and acting with purpose will drive measurable gains efficiency, accuracy, and customer trust.
The insurers that thrive in 2026 will be those that evolve to understand AI not as a short-term experiment but as a core enterprise capability and a catalyst for growth and resilience.
For insurance, the challenge is clear: Invest in AI with an eye toward transparency, workforce transformation, and technological evolution by exploring use cases that elevate operational efficiency AND human performance.
