The insurance industry has been circling AI like a curious bystander peering into a shop window: intrigued, hopeful, but hesitant to step in and buy. Money is flowing into AI pilots, proofs of concept, and experiments, yet few insurers can point to meaningful results. Why? Because they're looking in the wrong direction.
The obsession with shiny, peripheral use cases is distracting leaders from where AI can deliver tangible business gains today: underwriting accuracy, faster claims processing, and customer engagement that builds loyalty at scale. Instead of chasing abstract AI visions, insurers need to double down on practical, high-value applications that directly improve efficiency and profitability.
In doing so, they can unlock the kind of sustained performance gains that separate leaders from laggards. McKinsey found that over the past five years, AI leaders in the insurance sector have created 6.1 times the total shareholder returns of those trailing behind.
Missed Opportunities Staring Insurers in the Face
Take claims processing. It remains one of the most manual, error-prone, and expensive parts of the insurance value chain. While some carriers have experimented with chatbots, the real leap comes from using AI-driven document processing and natural language models to analyze claims documents, detect fraud, and trigger near-instant payouts. Zurich's use of ClaimsX, which uses publicly available data for real-time claims assessments, is a clear example of this in action. Yet many insurers still rely on siloed, human-heavy processes that delay settlements and frustrate customers — perhaps not surprising, given that fewer than half have taken meaningful steps to integrate AI across core functions, according to KPMG.
Underwriting is another underexplored goldmine. Dynamic, AI-enabled underwriting models can use health data, wearables, and behavioral insights to set fairer, more personalized premiums. Nedbank Insurance's funeral policy, which adjusts coverage based on partial premium payments instead of outright cancellation, shows how flexible AI-based underwriting can strengthen customer retention and profitability simultaneously. Still, some insurers cling to rigid rules and outdated actuarial models.
Then, there's customer engagement. Most customers don't want to call an agent or visit a branch; they want clear answers, instantly. AI-powered virtual agents, trained on vast troves of customer interactions, can now handle routine queries, claims updates, and policy recommendations with human-like fluency. Companies like Ethos Life are already deploying chatbots that ease buying and servicing, but this remains the exception rather than the norm.
Why Insurers Keep Missing the Point
Why aren't these obvious opportunities scaling? The barriers are depressingly familiar: legacy systems, fragmented data, and AI projects being treated as "IT experiments" rather than strategic business priorities. Too often, CIOs chase broad AI roadmaps without tying them to the insurer's growth strategy or customer value. The result? AI stuck in pilot purgatory.
The industry's fixation on futuristic models like embedded insurance or speculative AI-powered products, while exciting, is diverting focus from fixing the foundations. Without streamlined underwriting, efficient claims, and responsive service, even the most innovative product ideas will fall flat.
A Smarter Path Forward
Insurance leaders need to reframe their AI agenda from "what's possible" to "what matters now." Here are practical steps to make that shift:
- Start with process groups, not isolated tasks. Automating only one step — say, claims registration — simply creates bottlenecks downstream. Automate the entire claims intake-to-validation flow to see measurable improvements in turnaround time and customer satisfaction.
- Invest in data readiness. AI is only as good as the data feeding it. That means provisioning high-quality structured and unstructured data, harmonizing it across legacy systems, and setting up robust data governance. Think of this as the fuel that powers underwriting precision and claims accuracy.
- Deploy agentic AI for operations. Instead of static bots, use AI agents that can orchestrate multistep workflows across policy servicing, fraud checks, or compliance reporting. These agents don't just respond; they act, making operations faster and more adaptive.
- Tie AI use cases directly to revenue and retention. A chatbot experiment might look modern, but unless it reduces call center costs or improves cross-sell rates, it's wasted effort. CIOs should partner with business leaders to pick use cases that have measurable business outcomes like higher premium conversions, faster claims closures, or lower churn.
- Monitor and adapt continuously. AI models drift, customer behaviors shift, and regulations evolve. Establish practices to measure AI's effectiveness in production and make course corrections before the business impact erodes.
The Payoff: Efficiency That Fuels Growth
The real story isn't about AI as a futuristic add-on to insurance. It's about AI as a force multiplier for the basics: assessing risk, handling claims, serving customers. Get that right, and growth follows naturally through reduced costs, improved customer trust, and faster product innovation.
Insurers that stop chasing shadows and start applying AI where it matters most will not just improve efficiency; they'll reset the industry's playbook for profitable growth. The question is simple: Will your organization keep watching from the sidelines, or will you step onto the field where the real gains are waiting?
