by Thijs de Boer, Managing Director, Aon Strategy & Technology Group (STG), and Co-Lead of Aon’s UK/EMEA Strategy Consulting Practice, Aon

Commercial Lines cover ~45% of the global P&C insurance market and serve everyone from SMEs to the Fortune 500 in their risk and capital management. They deal with complex, high-value risks across industries such as manufacturing, logistics, construction, and professional services. Unlike personal lines, where risks are relatively standardized, commercial exposures are multifaceted and often interconnected. This complexity demands a more sophisticated approach to underwriting, portfolio management, and broker and client servicing. The market is evolving rapidly as risks become more complex, more volatile, and more interconnected. Data analytics and AI are critical to how (re)insurers will meet today’s and future challenges. The ability to harness data effectively is no longer optional; it is a strategic imperative.

Why Data Analytics/AI (DAI) Will Determine Carriers’ Future Performance

As the global risk landscape becomes more volatile, interconnected, and unpredictable, the (re)insurance industry stands at a critical inflection point. From climate-driven catastrophes and geopolitical instability to cyber threats and supply chain fragility, the complexity of commercial risk is escalating—and traditional underwriting models are no longer sufficient.

All leading (re)insurers now agree: Data, Analytics, and AI (DAI) are central to their strategic success. Over 90% plan to increase DAI investments in 2025–2026, aiming to unlock gains in productivity, premium growth, and loss ratio improvement. Yet, despite this consensus, scaling remains elusive. Only 20% of carriers report advanced DAI maturity, while 72% cite fragmented data and lack of enterprise architecture as major barriers.

This is the pain point. In a softening market—where rate pressure, increased portfolio complexity, and automated follow capacity are reshaping the commercial lines landscape—carriers must evolve fast. The ability to ingest, structure, and activate data across the value chain—from client to broker to carrier—is no longer a differentiator; it’s a survival requirement.

AI must be embedded across underwriting, claims, operations, and portfolio steering. GenAI, behavioral analytics, and predictive modeling are already showing impact—but without foundational data infrastructure, these tools cannot scale. The industry must move from pilots to platforms, from experimentation to enterprise-wide transformation. Ultimately, the role of (re)insurance is to reduce volatility for society and business. And in today’s world, that means upgrading the DAI engine—quickly, decisively, and at scale.

Three Reasons Why the Market Must Act Now

1. Softening conditions are back—while risk volatility keeps climbing.

Underwriting is increasingly flexible, capacity is ample, and price competition is rising—good news for buyers, but margin-dilutive for carriers without structural cost and accuracy advantages. Cycle resilience and profitable growth are common buzzwords in the C-suite, but those who manage the cycle proactively—adjusting their portfolio using granular analytics—will continue to deliver results. As we have seen from reinsurance pools, profitability dispersion by line and region is significant; disciplined portfolio steering, powered by DAI, helps carriers stay in the advantaged quartile as pricing softens.

2. The portfolio mindset is here to stay.

The London market already sends strong signals that facilities, automated follow capacity, algorithmic underwriting, and cross-class facilities are here to stay. Brokers are consolidating risk flow into portfolio constructs (binders, facilities, cross-class umbrellas) and digitizing pre-placement at speed. Platforms like Aon’s Broker Copilot are built to standardize data, expose real-time trading information, and remove frictions—naturally favoring packaged, programmatic deal flow over artisanal single-risk placement, with, in many cases, favorable loss ratios. Facilities concentrate volume, reduce trading costs, and deliver speed and certainty to clients, so brokers will keep pushing them, regardless of the cycle.

3. A volatile world needs a resilient (re)insurance market.

When shocks happen, a resilient insurance market pays promptly and accelerates recovery. As these shocks increase in frequency and severity, it will be critical for the insurance market to be ready to support. The protection gap is anything but closing, so (re)insurers need to accelerate their investments in analytics. For example, AI-enabled claims triage already shortens cycle times by 30–45% and cuts fraud by 10–25%, improving liquidity right after events. This should be further fueled with alternative capital that is keen to invest in non-correlated areas like insurance.

It will be the large (re)insurers who make the difference here. There are still opportunities for insurtechs, but not in the way we thought ten years ago. Large players haven’t been replaced by tech start-ups; profitable growth is more important than just growth, and the innovation focus is now also on commercial lines.

In conclusion

Society is asking the insurance market to do more. Embrace the opportunities that data analytics and AI offer to capture the next wave of profitable growth and remain the partner of choice for clients and brokers alike.