Accenture’s Stefan Sieger and Jeevan Thangudu argue that, despite strong earnings and well-capitalised balance sheets, many reinsurers are still struggling to translate favourable market conditions into sustainable, profitable growth—and to fully capture the value of artificial intelligence.
The constraint, they suggest, is no longer capital, capacity or risk appetite. Instead, it lies in operational inertia: legacy systems, fragmented workflows and manual processes that continue to slow underwriting decisions at precisely the point where speed and precision have become decisive sources of competitive advantage.
Speaking with Reinsurance News, the executives outlined findings from conversations with 11 senior operations leaders across six major global reinsurers in North America, Europe and Asia.
The discussions focused on constraints in current operating models, efforts to redesign underwriting workflows, and how AI is being deployed in live production environments.
“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,” Sieger observed.
Sieger continued, “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. It does not threaten today’s earnings. It constrains tomorrow’s profitable growth.”
For Thangudu, the firms making meaningful progress are not simply experimenting at the margins; they are re-architecting underwriting workflows end to end.
He added, “They are automating renewals by default so expert time focuses on exceptions, compressing time to quote from days to hours through intake and triage redesign, and linking underwriting decisions directly to real-time accumulation and retro views.
“The payoff is measurable: lower operating costs, higher productivity and faster, more disciplined deployment of capital.”
The executives explained that operational inertia does not immediately erode profitability in a hard market; rather, it constrains profitable growth in volatile and underinsured segments where disciplined speed determines who captures opportunity.
However, as pricing conditions begin to soften and market cycles turn, the ability to operate with speed and precision becomes more critical to maintaining margins and capturing profitable growth.
Legacy systems, fragmented data, manual underwriting steps and compliance drag reportedly compound that inertia.
The executives added, “As catastrophe losses have surged, they have reinforced a structural reality.
“Profitable growth is operational as much as financial. Resilience means the ability to continue writing business with discipline, speed and pricing confidence when volatility rises. It requires operating models that scale underwriting judgment rather than slow it.”
Delving further into where operational inertia manifests, Sieger noted that while reinsurance markets often appear stable on the surface, beneath that stability, it accumulates through “everyday” friction.
Sieger went on, “Manual workflows dilute underwriting focus. Data quality gaps translate directly into pricing uncertainty. When submissions arrive in inconsistent formats or bordereaux contain incomplete fields, underwriters load uncertainty into price or defer decisions. The result is slower turnaround and lower hit rates.
“System fragmentation forces rework across functions. Disconnected intake systems, actuarial models and accumulation views require repeated handoffs. Each handoff adds delay and increases the probability of error.”
Thangudu suggested that reinsurance also has structural features that make operating design central to AI value.
He added, “Treaty business is renewal-heavy. In many portfolios, renewals represent roughly 90% of volume. The logical baseline is automation by default, using existing exposure and claims history. Expert judgment should concentrate on exceptions rather than routine renewals.
“Facultative business is bespoke and broker-driven. Here, the highest value lies in triage and decision support that protects scarce expert time. Speed and consistency influence quote hit rate and portfolio quality.
“Bordereaux ingestion directly affects underwriting quality. Messy bordereaux and unstructured submissions drive uncertainty loading and obscure early signals. Standardisation and lineage control are underwriting disciplines, not operational hygiene.
“Retrocession and catastrophe aggregation require trusted accumulation views. Static batch reporting cannot support dynamic portfolio steering in volatile conditions. Real-time accumulation and retro views improve discipline in both entry and exit decisions.
“Broker asymmetry shapes competitive outcomes. Brokers often see market shifts before reinsurers do. Speed, completeness and consistency influence both access and selection.
“Operational redesign makes it possible for underwriters to pursue new business that previously appeared uneconomic because peak cycles were consumed by renewal rework.”
According to the executives, leading reinsurers are beginning to deploy AI at scale in selected areas, particularly to sharpen risk selection, improve pricing consistency and accelerate decision-making across the underwriting workflow, using catastrophe volatility as the ultimate stress test.
By integrating climate, geospatial, loss, exposure and market data, firms can assess accumulations more dynamically and price with greater confidence in areas of highest uncertainty.
They also emphasised that AI’s impact is inseparable from data architecture.
The pair went on, “Modern reinsurers are investing in ingestion engines that accept whatever arrives, including slips, bordereaux and contract or claims attachments. These inputs are standardised and connected through APIs to downstream systems.
“In practice, many are combining in-house capabilities with external data and technology partners, reflecting a more ecosystem-driven approach to building AI-enabled underwriting capabilities.”
“Together, this architecture creates real-time visibility across underwriting, claims and actuarial teams. It supports more reliable AI deployment because data lineage and ownership are defined clearly. It also produces faster, more consistent quoting and cleaner audit trails.”
Importantly, as Sieger noted, none of the senior operations leaders interviewed described a goal of replacing underwriters.
“Technology alone does not remove operational inertia. Leading reinsurers are building cross-functional hubs that combine underwriting, data science and technology integration. One organisation described establishing a shared service centre to centralise claims and technical accounting. The change improved turnaround time and accuracy while freeing underwriting capacity,” Sieger said.
He continued, “Governance is evolving alongside capability. Decision-making layers are being simplified so that first-line ownership of risk is clear while second and third line oversight remains effective without creating approval bottlenecks that slow underwriting decisions.”
Reinvention, the pair concluded, is already underway with many firms moving from experimentation into selective scaling and early production deployment. Reinsurers that align intake, data architecture, underwriting workflow, talent and governance will be better positioned to deploy capital with precision—capabilities that increasingly define leadership in volatile markets.
As referenced within, Accenture’s Insurance Research team, led by Andre Schlieker, conducted in-depth interviews with 11 senior core operations executives at six leading global reinsurers across North America, Europe and Asia. The interviews explored operating model constraints, underwriting workflow redesign and AI deployment in live production environments.
