Artificial intelligence in insurance is no longer confined to pilots or back-office experiments. It is steadily moving into the core operational work that insurers once believed required human intervention alone. This shift now spans underwriting, claims, servicing and finance.
Industry data shows that document processing, claims handling and operational automation are among the fastest-growing AI use cases in insurance. Investment patterns reinforce the trend. The global AI-in-insurance market is projected to grow sharply over the next decade, driven largely by the automation of everyday operational work.
The industry is no longer debating whether AI belongs in insurance. It is deciding how deeply it is willing to let it operate.
That decision matters because operating cost has long weighed on the industry. Roughly 22 per cent of global premium goes to running the business. Even after accounting for distribution, a significant share of cost sits in administration, finance, claims processing and servicing. These functions are essential, but they rarely differentiate one insurer from another. They quietly limit growth, flexibility and innovation.
For years, technology offered only partial relief. Traditional automation sped up individual tasks but struggled to understand context or connect work across workflows. Modernisation efforts often meant another rebuild, another integration program, another multi-year roadmap.
AI promised something different. Yet generic AI, applied narrowly or confined to pilots, has not delivered the breakthrough many expected. In practice, it has often struggled to scale or sustain value.
The more promising path does not involve rebuilding the enterprise yet again. The insurance industry has spent decades attempting large-scale system replacements, often with mixed results. A quieter shift is now underway.
A new model is emerging: insurance-trained AI that operates as an intelligent processing layer across existing systems.
These systems are designed to interpret applications, policy documents, claims records and financial data with a level of accuracy that supports real decision-making. They route work, reconcile information and assist judgment without requiring wholesale replacement of core systems.
This shift aligns with how industry leaders increasingly talk about AI. Conversations now emphasise the importance of human judgment. That is not a rejection of automation. It is an acknowledgement that people create differentiation, while processes should simply work.
The aim is not to remove humans from insurance, but to remove the friction that keeps them tied up in repetitive work.
This is what it means for AI to reshape insurance operations. Underwriting support that runs continuously. Claims intake completed in minutes rather than days. Finance processes that reconcile in near real time. As operational effort recedes, talent moves closer to where value is created: risk selection, customer relationships and portfolio insight.
Efficiency, in this context, becomes empowerment.
The remaining resistance is more cultural than technical. Insurance is paid to be cautious, and the industry remembers past technology promises that delivered complexity instead of clarity. But this generation of insurance-trained AI does not require disruptive transformation programs. It integrates quietly, delivers value quickly and improves with use.
Insurance has always prided itself on managing risk. Today, the biggest risk may be inertia.
AI is ready to take on insurance operations. The question is whether the industry is ready to let it.
For more information, visit meaplatform.com
Martin Henley, CEO, Mea Platform
