Reinsurance News reports that Accenture executives Stefan Sieger and Jeevan Thangudu argued many global reinsurers face "operational inertia" that limits their ability to capture the value of AI. The constraint is described as legacy systems, fragmented workflows and manual processes, according to the conversations the executives outlined with 11 senior operations leaders across six major reinsurers in North America, Europe and Asia. Reinsurance News quotes Sieger saying the workflow was "like flying an Airbus 380 and changing the engines mid-flight." The executives told the outlet that firms making progress are redesigning underwriting end to end, automating renewals by default, compressing time to quote from days to hours, and linking underwriting decisions to real-time accumulation and retro views, producing measurable payoffs. Editorial analysis: Industry observers should view this as a common operational barrier to scaling AI in regulated, legacy-heavy sectors.
What happened
Reinsurance News reports that Accenture executives Stefan Sieger and Jeevan Thangudu argued many reinsurers are struggling to turn favourable market conditions into lasting, AI-driven growth because of what the executives call "operational inertia." Reinsurance News says the executives based their observations on conversations with 11 senior operations leaders across six major global reinsurers in North America, Europe and Asia. The article attributes the phrase "like flying an Airbus 380 and changing the engines mid-flight" to Sieger. The executives told Reinsurance News that the constraint is not capital or appetite for risk but operational factors, specifically legacy systems, fragmented workflows and manual processes. The article reports examples of firms that are redesigning underwriting workflows, automating renewals by default, compressing time to quote from days to hours, and linking underwriting decisions to real-time accumulation and retro views, and that these changes deliver lower operating costs, higher productivity and faster, more disciplined deployment of capital.
Editorial analysis - technical context
Companies in insurance and reinsurance commonly encounter three technical and process barriers when scaling AI: legacy core systems that are hard to integrate with modern ML pipelines, fragmented data and workflow silos that impede end-to-end automation, and manual exception handling that blocks throughput. Observed patterns in comparable sectors show these barriers increase integration and MLOps complexity, drive higher data-cleaning overhead, and slow model-in-the-loop decision latency. For practitioners, this typically means investing in data contract work, API wrappers around legacy systems, and robust exception triage rather than pure model improvements.
Context and significance
Industry context: The piece frames operational inertia as a growth constraint rather than an immediate earnings threat. Reinsurance News quotes the executives saying inertia "does not threaten today's earnings. It constrains tomorrow's profitable growth," and notes that as pricing softens, speed and precision become more decisive for margin preservation and opportunity capture. For AI teams supporting underwriting, that shifts priorities from exploratory pilots to production reliability, real-time scoring, and integration with accumulation and retro controls.
What to watch
Industry context: Observers should monitor three indicators that the article and comparable reporting treat as signals of progress in this space: published case studies showing time-to-quote compression, evidence of automated-renewal workflows in production, and tighter coupling of underwriting decisions with real-time accumulation analytics. Reporting that quantifies operating-cost reductions or productivity gains at specific firms would be a higher-confidence signal of scalable adoption.
Direct quotes
Reinsurance News attributes these lines to the executives: "When the head of underwriting at a major reinsurer described their workflow as 'like flying an Airbus 380 and changing the engines mid-flight,' he was not exaggerating," and "Despite strong earnings and robust capital reserves, many global reinsurers still operate with systems and processes that slow decision making at the very moment speed and precision matter most. We call it operational inertia."
Scoring Rationale #
The story highlights a notable operational barrier to scaling AI in a capital-intensive, regulated industry. It matters to practitioners building models for underwriting and to teams integrating AI with legacy systems, but it does not introduce new models or wide technical breakthroughs.
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