Six straight years above $100 billion - and the insurance environment is not getting simpler  June 22nd 2026

How Insurers Should Use AI-Created Capacity

Alan Demers

No matter which side of the argument one takes over AI job creation or destruction, there is a looming, widespread AI image crisis. Phrases such as “AI job apocalypse” say it all. Growing negative sentiment about data centers has found its way into political campaigns, with concerted efforts to halt or divert construction. To the surprise of many, the mere raising of the AI topic drew jeers from young graduates at several recent commencement ceremonies. According to Pew Research Center, just 10 percent of Americans say they are more excited than concerned about AI, down from 37 percent when first asked in 2021.

Of late, however, the tenor of the AI job-destruction debate is softening into a discussion of capacity creation—in other words, using new capacity for people to do other work instead of merely cutting jobs.

Instead of mass layoffs, companies must contemplate what to do with new capacity by redirecting employees to focus on more meaningful tasks. Although “more meaningful” and “higher-value” work are loosely defined, the precept of shifting resources to work of higher importance is well suited to the P&C insurance industry, which runs on people and prides itself on doing business through people and relationships.

AI is splitting insurance into two workforces – and the dividing line runs through the claims depart

A landmark PwC study of more than a billion advertisements finds AI is not destroying insurance work - it is creating a stark divergence between the roles it elevates and the roles it quietly erodes

The insurance industry has spent three years asking whether artificial intelligence will take its jobs. PwC's 2026 Global AI Jobs Barometer, published yesterday and based on analysis of more than a billion job advertisements across 27 countries, suggests that is the wrong question. The more important one - and the one with real consequences for every person working in insurance - is which jobs AI will make more valuable, and which it will quietly hollow out.

The answer turns on a distinction PwC calls professionalisation versus democratisation. In professionalised roles, AI strips away routine tasks and leaves the more complex, expert work to people, making those roles harder, better paid and more in demand. In democratised roles, AI absorbs the expert tasks instead, leaving the less demanding work behind and gradually reducing the premium attached to those jobs. The report's data shows professionalised roles growing twice as fast in headcount and seeing 42% faster wage growth since 2021. Democratised roles are growing, but more slowly, and their wages are falling behind.

Insurance sits directly in the path of this divergence.

Insurance Agents Are Using AI Faster Than Their Firms Can Govern It

Despite surging investment in AI tools, relatively few independent agents use AI daily, and most operate without formal governance or training, according to Cake & Arrow.

Insurance organizations are pouring money into artificial intelligence, with AI software spending forecast to reach $297 billion by 2027, but a new report from experience design firm Cake & Arrow finds that investment is failing to reach the agents who need it most.

Only 8% of independent agents are using AI on a daily basis, according to Liberty Mutual data cited in the report. Based on in-depth interviews with 16 agents and brokers across 13 states, the report finds that AI adoption in the agent and broker channel is largely ungoverned, inconsistently implemented, and producing far less value than the technology is capable of delivering.

The picture that emerges is one of improvisation at scale. Most agents interviewed fell somewhere between “ungoverned” and “informal” on a spectrum of AI governance maturity, the report found. In practice, that meant agents were handed access to tools with little guidance, left to learn from peers, and in some cases uncertain whether they were even permitted to use the AI tools already on their desktops.

One participant, a sales manager at a captive agency, told researchers that more training around use cases and prompting “would go a long way.”

Insurers’ AI goals expose the cloud execution gap | NTT DATA

AI is raising the stakes for insurers, exposing the cloud, data and modernization challenges that stand between experimentation and enterprise-scale impact

AI makes it possible for insurance claims adjusters to instantly access every relevant policy detail. Fraud analysts can spot suspicious patterns in real time, and customers can receive personalized answers to complex questions about their cover in seconds rather than days.

To get these results, insurers are investing heavily in AI — only to discover that the biggest obstacle to success is not the technology itself but the foundation underneath it.

This is because AI models depend on something many insurers are still working to modernize: cloud infrastructure and data platforms. When these foundations aren’t in place, even the most ambitious AI initiatives can struggle to move beyond isolated use cases.

Research from our global report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, shows that cloud modernization is the number-one priority for insurers over the next two years. Nearly all insurance respondents (98%) say the rise of AI and agentic AI has increased the need for cloud investment, with 95% acknowledging that their current cloud investment levels put AI, cloud-native and modernization initiatives at risk.

Can they modernize fast enough to operationalize AI securely and efficiently at enterprise scale?