Simon Glairy has spent years navigating the complex corridors of insurance and risk management, witnessing firsthand how technology transforms traditional systems. As a recognized expert in Insurtech and AI-driven risk assessment, he offers a grounded perspective on the current technological boom, moving past the hype to analyze the hard data regarding employment and operational efficiency. He brings a unique lens to the table, helping us understand why the “AI revolution” isn’t necessarily synonymous with immediate job losses, but rather a profound shift in what institutions value in their human capital.
Our discussion centers on the counterintuitive relationship between massive AI spending and workforce growth, specifically in the financial and insurance sectors. We delve into why entry-level hiring is actually surging at tech-forward firms and why the banking industry is desperately seeking “users” of AI rather than just the architects of it. Glairy also sheds light on the “efficiency gap,” where the perceived time savings of AI tools often clash with the reality of administrative overhead. Finally, we explore the specific skill sets—from prompt engineering to deep process knowledge—that are commanding significant wage premiums in today’s market.
We often hear that automation is a job killer, yet recent data suggests the biggest spenders on AI are actually expanding their teams. Why do you think we are seeing a double-digit increase in headcount at these tech-heavy firms instead of the expected layoffs?
The narrative that AI immediately translates to a smaller workforce is a bit of a logical trap that doesn’t account for the sheer scale of implementation. When researchers analyzed card and bill pay data from 21,559 U.S. companies, they found that those spending the most on AI per employee actually saw headcounts rise by 10.2% in the two years following adoption. It is particularly fascinating to see that entry-level workers saw a 12% rise, which flies in the face of the idea that junior roles are the first to be automated away. If you look at the information and insurance sectors, these firms are hiring faster because they are discovering that AI creates more work—more data to verify, more systems to monitor, and more use cases to explore. For a young person entering the market, the signal is clear: you want to be at a firm that is aggressively spending on these tools because those are the organizations with the momentum to grow their human teams alongside their digital ones.
The banking sector seems to be in a hiring frenzy for AI talent, but there is a distinction being made between those who build the models and those who use them. What does the current demand for “expert users” tell us about the future of financial services?
Banks are currently desperate for talent, but they aren’t just looking for PhDs to sit in a basement and build new large language models from scratch. According to recent jobs data, AI-related postings in U.S. banks shot up 77.4% over the last year, but a staggering 88% of those roles are for AI “users” rather than developers. This means firms are looking for people who can take a tool like Microsoft Copilot or a custom GPT and integrate it into the messy, daily reality of banking. It’s the difference between being a mechanic who builds the engine and a driver who knows exactly how to navigate a racecourse at top speed. As firms scale, they need people who understand how to maintain and monitor these “agentic” automated businesses to ensure they don’t go off the rails.
There is a lot of talk about “Copilots” saving half the workday, but some experts suggest the real efficiency gains are much lower. In your experience, why is there such a gap between what employees report and the actual impact on the organization?
We have to be very careful with self-reported data because an employee might feel like an AI tool saved them 48% of their day, but when you look at the actual output, the efficiency gain is often closer to 5%. Most of the AI currently deployed is tackling task-based work—simple things like taking notes in a meeting or booking a flight—which only accounts for a tiny fraction of a professional’s total value. If you do twenty things in a day and AI makes two of them 30% better, you haven’t actually transformed the business; you’ve just cleared a little bit of administrative brush. The real, heavy-duty efficiency comes when you move beyond these isolated tasks and start automating entire workflows, like account openings or anti-money-laundering reviews. Until banks master the paperwork, the validation, and the requirements documentation that surrounds the core “task,” we won’t see the massive displacement of labor that the doomsayers are predicting.
We are seeing institutions like Lloyds Banking Group recruiting hundreds of specialized roles for “agentic” AI. Could you walk us through what these teams are actually doing on the ground to support customers?
Lloyds is a great example of a firm moving past the experimental phase and into a dedicated expansion, recruiting for nearly 300 roles including data scientists, engineers, and responsible AI specialists. They already have over 700 employees dedicated to this, and they are using that brainpower to build sophisticated fraud detection agents that analyze payments in real time. This isn’t just a passive filter; it’s an active system designed to identify and prevent scams before the money ever leaves the account. They’ve also rolled out AI financial assistants to half a million customers to provide faster, more personalized support. The goal here is to use the technology to help human colleagues make better decisions, ensuring that the day-to-day interactions feel more effective rather than more robotic.
Given that some AI roles now command a 54% wage premium, what specific skills should a traditional banking professional focus on to remain competitive without having a background in computer science?
You don’t need to be a coding wizard to thrive in this new environment, but you do need to understand your own company’s “arcane” systems better than anyone else. The data shows that firms are willing to pay a 54% wage premium for AI-capable people because they need employees who can design high-quality prompts and engineer better data quality solutions. In many ways, a senior executive who understands the complex requirements of investment banking or commercial workflows is more valuable than a developer from a tech giant who doesn’t know the difference between an S-1 report and a standard IPO filing. Banks need people who can prototype solutions using “vibe coding” and then—crucially—test the heck out of those solutions to ensure they meet strict regulatory controls. The most successful people will be those who can act as the bridge between the raw power of the technology and the strict, procedural needs of the business.
What is your forecast for the banking labor market over the next few years, especially if the current favorable economic conditions shift?
Right now, we are in a bit of a “honeymoon phase” because the growth market provides no real incentive for banks to start aggressively cutting heads; they would rather use AI to grow faster than their competitors. However, I predict that when the next recession eventually hits, we will witness the largest percentage of layoffs the industry has ever seen. During a downturn, organizations will be forced to use AI to drive radical efficiencies, and once they realize they can operate without those human resources for two years, many of those jobs will simply never come back. The “AI button” that everyone is looking for doesn’t exist yet, but a recession will be the catalyst that forces firms to finally integrate AI into their deep workflows. This will move the technology from a helpful “copilot” to a fundamental replacement for high-volume, repetitive professional roles.
