Insurance companies face a long-running workforce shortage and struggle to attract new workers to a business often seen as boring. Artificial intelligence could both worsen and relieve that tension, reshaping fraud detection, risk assessment, and the skills employees need. The technology is forcing firms to rethink how they develop talent and manage teams.
Fraud and risk detection
AI is already improving the ability to spot organized fraud rings. Pete Miller, CEO of the Institutes Risk and Insurance Knowledge Group, said sophisticated fraud networks involving doctors, attorneys, and even foreign governments are becoming harder to catch without advanced pattern analysis. "There are some nation states that are supporting hackers that might set up fraudulent websites," Miller said. "They can make search engine optimization such that it goes to this fraudulent website, and the person actually thinks it's their insurance company."
At the same time, AI can also generate fake images and exaggerated damage claims. Gunratan Lonare, a professor at Illinois State University's Katie School of Insurance and Risk Management, described it as an arms race between "good AI" and "bad AI." He said insurers need better AI than fraudsters to stay ahead.
Beyond fraud, AI is making risk assessment more granular. Telematics that monitor driving habits become more powerful with AI. Home risk profiles can now include simulations of wildfire impact, showing how clearing vegetation could reduce damage. Miller noted that such tools not only price risk more accurately but also help prevent losses.
Workforce and talent development
The industry's hiring challenge is compounded by competition for data scientists and IT professionals who can easily move to other sectors. Julia Lamm, a principal at PwC focused on insurance, said early adopters of AI will have an edge in attracting that talent. On the flip side, Miller said AI may make insurance less boring by eliminating repetitive tasks and replacing them with more interesting work like fraud analysis and software oversight.
That shift changes how companies develop expertise. Traditionally, employees learned by spending years in claims before moving on. Lamm said no client wants to abandon that apprenticeship model, but firms are looking at faster learning paths. "Rather than learn on the job being your primary and, in some cases, almost sole way to learn, they're thinking about a lot more creative learning paths, simulations, sharing lessons learned in a different way," she said. AI can generate training modules quickly and push them to workers who need them.
Miller said companies must be intentional about building "data fluency" and "AI literacy" across the workforce. He emphasized that critical thinking and problem-solving are now essential, not just valued: "Do you have data fluency? Can you understand and look at that data and sort of say, here's signal, here's noise?"
Gunratan Lonare said many universities still teach insurance, coding, and finance in isolation, which he called "concerning." He is developing what may be the first AI-centered course in the nation for finance, risk management, and insurance, but it is about a year from rollout.
Management and middle managers
Middle managers face particular upheaval. Lamm predicted a need for a larger layer of generalists overseeing broader processes. Miller said management will change because AI handles routine decisions accurately and quickly, leaving managers to use discretion on exceptions. "Management is going to change quite a bit. It really collapses layers in an organization," Miller said. "The stuff in the middle, it's good, it's accurate, it's very fast. Most of that stuff goes through, but the manager has to use discretion and judgment on the exceptions."
Lamm noted that people skills still matter because coaching critical thinking takes time. She added that managers are now overseeing more employees aided by AI agents, making the role "harder in some cases."
Who wins
Miller said AI is different from past technological shifts because it replaces human cognition, not just physical labor. The firms that can leverage massive data sets for prediction and pricing will have a significant advantage. He predicted consolidation, with big insurers getting bigger and crowding out midsize and small carriers. "To the extent that that causes consolidation, I think that is very significant," he said.
Lamm said the industry is in the middle of a hype cycle. Early excitement has given way to doubts because many companies see uneven results. "If you're thinking just about use cases, you're not thinking about how processes change. You're not thinking about how rules change. You're not thinking about the change management and how people are going to come up this learning curve," she said. "Those companies are being heavily underwhelmed by their results." She said the technology is moving fast; it is the people who cannot keep up.
AI is also reshaping the way insurers approach management and workforce strategy, creating new demands for generalists who understand both people and technology.
Why this matters for insurance professionals
The shift to AI will not eliminate jobs, but it will change what those jobs require. Professionals who develop data analysis skills, learn to work alongside AI tools, and adapt to faster learning cycles will be better positioned. Firms that fail to invest in both technology and people will struggle to keep talent or compete on price. For workers, the message is clear: critical thinking and AI literacy are no longer optional - they are the foundation of career resilience in insurance.
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