At ITC London 2026, AI makes insurance innovation a must‑have

Insurance technology has long promised reinvention, yet most insurers have not fully completed a transformation. At ITC London 2026, the message was clear that AI is accelerating competitive cycles and that any company that clings to cautious modernization will lock itself into a structural disadvantage, because the technology is ready but most operating models are not.

Across the leadership keynotes and operational deep‑dives, ITC London 2026, held Jan. 26-27, distilled a single, stark proposition that AI will reward insurers that can industrialize experimentation and penalize those that keep innovation confined to a lab. The consensus was that cultural transformation is not a peripheral "agile theatre" but a prerequisite for underwriting and claims functions to deploy models at scale while preserving the controls that regulators demand. Insurers that embed a test‑and‑learn mindset into their governance fabric will be able to iterate rapidly, capture new risk insights and sustain competitive advantage, whereas firms that stick to ad‑hoc pilots will see their AI investments languish.

A second, equally important insight was that insurers have put the most effort into the "back house," improving core platforms, moving to the cloud, and upgrading underwriting workbenches while leaving the "front door" unchanged. In some instances, risk submissions still come in as messy PDFs, long email chains and handwritten notes, pushing skilled staff into manual sorting and slowing decisions. The most persuasive AI use cases presented were therefore not fully automated underwriting, but practical tools that organize incoming information, raise straight‑through processing and let underwriters focus on risk selection, portfolio management and broker‑client service. Panels also reframed governance, bias and compliance not as roadblocks but as design requirements; legacy decisions already contain human inconsistency, and AI can either amplify those weaknesses or, with strong data discipline, monitoring and model oversight, help standardize outcomes across the enterprise.

The ITC London 2026 exhibition floor made it abundantly clear that insurers are moving beyond vague "transformation" slogans and are now demanding solutions that eliminate concrete bottlenecks and bridge critical talent gaps. Vendors converged on five functions: upstream ingestion and document‑intelligence tools that transform unstructured inputs into structured data; workflow‑orchestration platforms that embed model outputs directly into underwriting systems; digital‑trading suites that accelerate capacity checks and term negotiations; governance‑by‑design suites that provide auditability, model‑risk management and reporting; and specialty‑analytics engines focused on emerging perils such as cyber and supply‑chain disruption. The common denominator isn't blanket AI but the strategic placement of intelligence that unlocks throughput, reduces leakage and reshapes the customer experience, thereby delivering operational efficiency and profitability.

A parallel narrative focused on digital follow‑underwriting, where incoming documentation is becoming increasingly machine‑readable and queryable. This capability allows insurers to verify capacity, run compliance checks and place risks in near‑real time, but it also raises the bar for clean audit trails, consistent data interpretation and strong access controls. In the insurance sector, digitization is therefore more than a back‑office fix; it can reshape market efficiency, affect risk‑placement and shift distribution dynamics across specialty commercial, cyber and other supply‑chain lines.

During the two‑day event, speakers recast governance, bias and regulatory compliance as foundational design criteria rather than impediments. In the insurance world, traditional underwriting and claims processes are riddled with human variability; AI tools can either scale those gaps or, when coupled with data discipline, ongoing performance checks and diversified oversight, serve as a corrective layer that informs key decision‑making. The most persuasive guidance stressed the need for transparent ownership of model outputs, rapid remediation of gaps and multidisciplinary design to uncover hidden risks. Collectively, the panel discussions make it clear that strong supervisory structures are essential for scaling AI safely within the tightly regulated insurance industry. In the United States, for example, various committees in the National Association of Insurance Commissioners, including the Big Data and Artificial Intelligence Working Group, have been seeking to better track and evaluate insurers' use of AI systems.

Whether in the insurance sector or elsewhere, practical deployment of AI has become paramount as elevator-pitch references and theoretical discussions no longer suffice. Many insurers favor a hybrid approach that blends internal underwriting expertise with external software partners and outsourced talent to fill gaps in data engineering, document handling, workflow design, model validation and change management. The AI solutions on display were built to plug into existing core and underwriting platforms, improving data intake and decision support; insurers that can seamlessly coordinate vendors, talent and integration layers while preserving controls will turn this technology wave into lasting operational advantage.

Tim Zawacki contributed to this article.

This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.