By Arno Nel, AI Product Director at Advania
Machine learning and generative AI have already colonised the core functions – risk modelling, forecasting, claims processing. The tech delivers faster, more accurate results, and most major players now treat it as a standard kit. It’s no longer about whether you have AI; it’s how you use it.
What separates the pack now is strategic implementation. How thoughtfully you deploy next-generation AI across operations, not whether you’ve ticked the adoption box. And it’s not just about automating repetitive processes anymore, it’s about embedding AI into decision-making layers, enabling insurers to respond faster, adapt quicker, and serve better.
The sprint’s already started
AI reshaped pricing and fraud detection years back. Machine learning algorithms have been processing exposure data and calculating premiums since the early 2020s, practically prehistoric by tech standards. That’s the established stuff.
But generative AI is different. Large language models are proving their worth in claims analysis and fraud identification, though most insurers remain cautious about full deployment. This newer tech is transforming customer service interactions, creating personalised responses at scale, and enabling human-like engagement at digital speed.
Advanced generative AI and autonomous systems are opening up fresh pathways to meet both rising customer expectations and increasingly complex regulatory demands. From real-time responses to instant product modifications, service delivery is moving at a speed that would’ve seemed impossible just five years ago. Consumers now expect hyperpersonalised offerings, conversational AI agents, and end-to-end digital engagement- and they expect it across the board.
The next leap: agentic AI
Here’s where things get interesting. Most insurers haven’t fully grasped the significance of agentic AI yet. These systems operate autonomously, adapting in real time to pursue specific objectives. This isn’t just workflow optimisation, it’s fundamental operational restructuring.
Autonomous AI agents can ingest data, interpret policies, interact with customers, and make decisions without constant human oversight. They work together in networks: one agent might assess risk, another might handle compliance, a third might structure pricing, and a fourth might orchestrate decision-making.
Low-code platforms are beginning to democratise some of these capabilities. Business users – those closest to operational pain points – are starting to build AI-powered tools using intuitive interfaces. This could unlock a new wave of innovation from the ground up.
It’s early days for this kind of distributed intelligence. But the promise is huge: agile innovation, scalable efficiency, and an operating model that evolves as fast as the market demands it.
Strategy trumps technology every time
Here’s the fundamental difference: bolting AI onto existing systems versus placing it strategically where it creates real value. Basic AI adoption means you’ve got the tech. Strategic AI implementation means you’re using it intentionally, where impact actually occurs.
The winners won’t be the first to deploy flashy tools. They’ll be the insurers with clean, structured data, measurable objectives, and governance frameworks that empower without stifling. Cultures that reward experimentation while maintaining accountability.
The critical question has shifted from “where can AI help?” to “where should it help?”. In an AI-saturated landscape, your ability to target high-impact opportunities becomes your strongest competitive advantage.
Nobody’s waiting for perfect conditions anymore
Legacy systems resist integration. Regulations are complex. Organisational inertia is real. But insurers can’t afford to wait for the perfect conditions to deploy AI. The risk of inaction now exceeds the risk of moving early and refining as you go.
A clear divide is emerging between insurers who are taking bold, iterative steps toward AI leadership and those still forming committees to discuss committee formation. Real-time risk assessment, autonomous claims processing, and intelligent product design are no longer distant goals.
To succeed, insurers must think in terms of rewiring, not retrofitting. That means end-to-end transformation of domains like underwriting, claims, and customer service. It means architecting systems and workflows that are AI-native from the ground up.
Progress moves slower than the headlines suggest, but much faster than traditionalists expect. The transformation is accelerating, with or without you. You either help build the new operating model, or spend the next decade trying to decode what everyone else already implemented.
