Claims inflation continues to outpace premium growth, and regulators are scrutinizing operational resilience alongside balance sheets. Yet many insurers remain structurally unable to respond at the speed the market demands.
We call this the performance gap: that is an inability to excel in five areas: operational, financial, compliance, decision making and time to market. This gap, born of years of failure to keep up with necessary technology, skills and systemic change, makes them vulnerable to the extraordinary volatility we see today. The solution is to embrace change but also to understand that installing new IT systems alone will not create instant adaptivity.
Technology is too often treated as a magic wand through which organizations can become dynamic institutions, capable of moving rapidly and without friction. This is a myth perpetuated by techno-optimists. Platforms like cloud or emerging trends such as AI cannot on their own afford new thinking or speed of movement. The reality is that insurers must transform operating models, banish ancient processes and address fundamental underlying restrictions that hinder flexibility.
Shifting sands
Change is urgent because volatility is here to stay. In the P&C world,
Climate severity, social inflation, interest rate dislocation and reinsurance cycle compression are not going to go away anytime soon… and even if they did, they would doubtless be superseded by a new set of challenges needing urgent attention. A propensity for change and capacity to enforce change are always necessary in any fast-changing sector, so it's critical that insurers have that ability to react to what is happening today, and whatever comes next.
Of course, in a digital world, technology is both part of the problem and the solution. Operating disparate legacy systems increases running costs. Information silos mean fragmented audit trails which impact compliance via delayed filings and regulatory exposure. Without a single view of all information, underwriting logic becomes piecemeal and it takes longer to update products, leading to coverage gaps in portfolios.
There is an urgency to address these areas, not just because of market competition and new entrants. Supervisory bodies and rating agencies are also now looking beyond financials to understand insurers' operating models. They see operational resilience and technology readiness as core signals of preparedness.
Companies that will outperform in the coming years will build dynamic operating models capable of absorbing change, aligning decisioning and sustaining performance across cycles.
Knowledge management
Legacy IT systems are a problem but the main obstacle commercial insurers face is that organizational knowledge is dispersed and unmanaged. Information and insights are spread across different documents, applications, databases and platforms. New systems will help but insurers first need to organize institutional knowledge and review processes.
Often, insurers maintain highly complex procedures, characterized by unnecessary steps enforced by rigid systems. And yet this code can't easily be dispensed with: often, it is quite literally the core of the institutional knowledge. These systems have been frozen in time with rules hard-coded. Processes and workflows are not easily configurable without the need for bespoke programming or affecting dependencies. So, even as legacy systems are replaced by new platforms, insurers need to organize knowledge and streamline processes, unlocking silos of inaccessible data wherever possible. For example, when an insurer needs to adjust underwriting rules after a sudden shift in the catastrophe loss pattern, the challenge is rarely just changing a system field. The relevant knowledge may sit across actuarial assumptions, underwriting guidelines, product definitions, regulatory requirements and legacy code. Unless that knowledge is structured and accessible, even a seemingly simple product or pricing change can take weeks instead of days.
Making change bimodal
Startups have the luxury of starting afresh but most commercial insurers don't. They hold vast stores of data and have processes hewn over decades. Change can't be a 'one and done' project but must take place alongside day-to-day operations. It should be treated as a process of continuous, iterative improvement. Not every change needs to be of the 'big bang' variety. Sudden shifts can be more dangerous than planned, gradual adaptation and reskilling. It's important to assess where the biggest pain points or the highest-value opportunities lie and start with those 'quick wins'.
Leaders need to communicate, showing why change is necessary and demonstrating improvements in productivity and competitive advantage. Many staff will be resistant to change but once they see how complex tasks can be more easily achieved and insights are revealed, they will become amenable to new processes and systems.
The harder adjustment is behavioral: learning to trust AI-assisted processes rather than defaulting to familiar but slower habits. In the new world the human is in the loop but the human also needs to be able to let go and step away from processes when sensible.
AI needs your data
Today, for obvious reasons, there is enormous excitement about AI but AI can only excel if the foundational data it ingests is high-quality, clean and appropriately structured.
The old rule of
Technology can help here. AI and machine learning can ingest data rapidly and identify anomalies faster than even the most skilled people. SaaS applications guarantee a stream of new features and capabilities in line with the evolution of the market, and they are written based on best practices with customization options based on preconfigured options to act as guardrails. Security and functionality are regularly upgraded as background processes.
The end goal of all this systemic change is to achieve a state of 'All Systems Go' where data and processes are aligned, decisioning is observable and resilient so that external volatility is transformed from a threat into a new competitive advantage.
Get it right, and this approach delivers dynamic operating models embedding constant, iterative change into everyday operations. Controls are designed into workflows, data lineage is clear by default, and product iteration accelerates without sacrificing governance. Companies running dynamic operating models pull ahead because they integrate signals faster: from exposure data, claims, and emerging loss patterns. They show lower claims leakage, steadier combined ratios across cycles, and faster product repricing when loss development, attritional ratios, CAT load or economic assumptions move.
This then is the future of P&C, technology-enabled but powered by the organizational brain, well-grooved processes and a culture of data excellence and continuous improvement.
