What will define insurance performance in 2026

Across underwriting, claims, and delegated authority, the market is moving from exploration to accountability. Data quality, pricing discipline, and operational control are no longer future ambitions. They are commercial requirements.

Insurers that invested early in structured data, integrated workflows, and measurable execution are beginning to see compounding advantages. Others are discovering that transformation delayed is transformation denied.

The trends shaping 2026 are less about new technology and more about how the industry is choosing to apply it.

Data quality becomes more valuable than new models

Research by McKinsey & Company shows that the primary challenge is not access to advanced algorithms, but the availability of clean, well-governed, and decision-ready data that can be trusted at scale.

This constraint is most visible in commercial and speciality insurance. Underwriting workflows remain heavily document-driven, built around submissions, endorsements, loss histories, engineering reports, and broker correspondence. As a result, unstructured and semi-structured documents continue to dominate underwriting inputs, creating friction across automation, analytics, and explainability.

In practice, this produces a clear divide. Insurers that can structure exposure data, enforce consistent risk attributes, and trace underwriting and pricing decisions back to original inputs are able to operationalise AI. Those that cannot find that model sophistication alone does not translate into control or impact.

Agentic AI moves from hype to constraint-driven adoption

The implementations that progress are those that deliberately narrow the scope of agency. Instead of broad autonomy, insurers deploy agents for clearly defined tasks such as document intake, triage, referral routing, and data validation. Anything beyond this quickly runs into governance and accountability constraints.

This is most evident in regulated markets such as the UK and the US, where explainability and auditability are non-negotiable. Agentic systems are viable only where authority is explicitly bounded, escalation paths are predefined, and ownership of decisions remains clear.
Within these limits, agentic AI delivers value by reducing operational friction and improving consistency, without undermining control. Responsibility stays with the underwriter. The system supports execution, not judgment.

Mispricing correction becomes unavoidable

In 2026, the issue is not the existence of mispricing, but the speed at which its effects are now surfacing. Claims performance, capital allocation, and reinsurance terms are increasingly linked, leaving little room to defer correction.
Insurers that can connect quote outcomes, bind decisions, and claims development back to original pricing assumptions can respond selectively. They can isolate underperforming segments and adjust without destabilising the wider portfolio.

Those without this visibility face blunt alternatives. Broad rate increases, capacity withdrawal, or tightened appetite definitions become the default, often at the expense of broker relationships and long-term positioning.

Claims overtakes underwriting as the primary automation target

Claims operations concentrate volume, cost, and customer experience in a way few other insurance functions do. A large share of day-to-day activity sits in low-severity, repeatable processes that are operationally expensive but analytically simple.

That is precisely why insurers are increasingly prioritising claims workflows for end-to-end automation. Straightforward claims can be processed with minimal human involvement, while more complex cases benefit from earlier triage, better information capture, and clearer routing to specialists. The goal is not to remove expertise, but to deploy it earlier and more precisely.

This shift is driven less by technological ambition and more by economics. Expense ratio pressure, combined with rising customer expectations around speed and transparency, is forcing insurers to focus on areas where efficiency gains translate directly into financial and experiential outcomes.

London Market modernisation shifts from infrastructure to behaviour

By 2026, the limiting factor in the London Market will no longer the availability of platforms, especially given that the core infrastructure has largely been put in place. The remaining constraint sits in day-to-day behaviour. Despite access to modern placement and data capture tools, a significant share of workflows continues to default to email-driven coordination, manual follow-ups, and fragmented record keeping.

This gap between platform availability and actual usage is becoming increasingly visible. Participants operating with integrated, system-led workflows are able to move faster, capture cleaner data, and maintain clearer audit trails. Those relying on informal processes experience growing friction as speed, transparency, and responsiveness become competitive differentiators rather than operational preferences.

As a result, faster turnaround times, clearer exposure visibility, and more reliable reporting are becoming baseline expectations.

Embedded insurance in the US enters its discipline phase

Early momentum was driven by speed to market and distribution reach. Products were launched quickly, integrations were optimised for launch rather than longevity, and underwriting assumptions were often secondary to partnership expansion. By 2026, this approach will become harder to sustain.

Simple propositions, predictable pricing behaviour, and stable claims handling are taking precedence over complex coverage structures and experimental features. Embedded insurance is being evaluated less as a marketing differentiator and more as a product with clear operational and economic expectations.

That is why programmes that rely on fragile integrations, manual exception handling, or loosely defined ownership models struggle to scale once volumes increase. Where accountability is unclear, friction accumulates quickly, both operationally and commercially. And, insurers that treat embedded insurance as a product discipline rather than a growth experiment will simplify coverage, improve integrations, and align underwriting, claims, and partner operations around repeatable patterns rather than bespoke exceptions.

MGA scrutiny increases as capital tightens

Capacity providers are increasingly focused on data transparency, reporting cadence, and near-real-time exposure insight. In both the London Market and the US, bordereaux reporting cycles are shortening, and performance reviews are becoming more frequent and more granular.

Lloyd’s of London has been explicit that delegated authority performance, data quality, and oversight are core priorities. Underperforming coverholders are facing corrective action regardless of growth narratives.

Losses linked to late detection of exposure accumulation, authority creep, and weak monitoring have already materialised. The response from capital is predictable: tighter terms, reduced lines, or withdrawal.

Compliance becomes continuous and system-driven

Regulators and internal audit functions increasingly expect evidence of ongoing control, not just documented intent. This is pushing compliance into operational systems, where decisions, approvals, and exceptions are captured automatically and reviewed continuously.

The shift is subtle, but structural, and it favours insurers with integrated workflows and reliable data lineage.

Underwriting talent evolves from judgment to systems thinking

Judgment remains essential, but top performers in 2026 will be those who understand portfolio dynamics, feedback loops, and system constraints, not just individual risks. Underwriters who can interpret pricing signals, operational capacity, and claims trends together are becoming disproportionately valuable.

Technology success is measured by economics, not delivery

Insurers are becoming less patient with transformation initiatives that lack measurable impact. Therefore, cycle time reduction, loss ratio movement, expense efficiency, and capital utilisation are now the dominant success criteria. Programmes that cannot demonstrate progress against these metrics struggle to maintain executive sponsorship.

Wrapping up

Insurers are being pushed to connect data, decisions, and outcomes across underwriting, claims, and delegated authority, under increasing economic and regulatory pressure.

And, this is where many initiatives break down. AI and automation are introduced, but workflows remain fragmented, data is not decision-ready, and accountability is diluted rather than reinforced.

Vega IT works at this intersection. Our focus is on turning complex, document-heavy insurance operations into controlled, measurable systems. We help insurers structure data, embed AI within real workflows, and create feedback loops that link pricing intent to portfolio performance. Contact us to learn more.