Kelley Kage is the CIO of Employers Insurance, a 113-year-old workers compensation carrier operating nationwide that recently expanded into excess workers compensation. Overseeing technology, data, product management, and cybersecurity, Kelley has led one of the most deliberate and results-driven enterprise AI rollouts featured on this podcast, achieving 77% adoption and 90% training completion companywide within the first two months of deploying Claude across the entire organization. She brings a change management framework to AI adoption that is already delivering measurable business value, and a conviction that the insurance industry is not the laggard in this transition but an opportunity to lead it.
Here’s a glimpse of what you’ll learn:
- Why Kelley frames governance as an on-ramp rather than a speed bump and how that reframe changes every conversation with skeptical stakeholders
- How Employers achieved 77% adoption and 90% training completion companywide within two months and the sequencing that made it possible
- Why the ROI on their Claude rollout is already paying for itself multiple times over and why those ideas came from business leaders, not the tech team
- Why AI adoption is a change management problem rather than a technology problem and what that distinction means for how you plan the rollout
- Why the companies approaching AI purely from a cost savings or headcount reduction mindset are building toward the wrong outcome
- How Kelley thinks about AI as an equalizer for executive coaching and mentorship access across every level of an organization
- Why being at the front of regulatory evolution is a strategic advantage in heavily regulated industries and how Employers is positioning for exactly that
In this episode…
Kelley opens with a framing that separates this episode from most AI adoption conversations immediately: governance is not a speed bump, it is the on-ramp. That single reframe carries significant weight in a 113-year-old insurance company where the instinct to slow down for compliance is deeply ingrained. Kelley's argument is not that governance should be bypassed in the name of speed. It is that governance built correctly and in advance is what allows organizations to move faster once the guardrails are in place. Partnering legal, HR, and cybersecurity into the AI adoption process from the start is not overhead. It is the architecture that makes rapid deployment possible without the expensive course corrections that come from moving first and asking permission later. For a regulated industry operating under ongoing scrutiny from multiple agencies, that distinction matters enormously and she makes it clearly.
The Claude rollout section of this episode is the most detailed and credible enterprise AI deployment case study this podcast has featured. Kelley is specific about the numbers: 77% adoption rate and 90% training completion in the first one to two months, with ROI already paying for itself multiple times over. But what makes the account valuable is not the metrics. It is the sequencing. The rollout started with the executive team and their direct reports, who went through an intensive training program designed not just to teach them how to use the tool but to retrain how they think about their own business processes. The technology deployment followed the mindset shift, not the other way around. The proof that this sequencing worked came at a companywide event at the end of March, where business leaders presented what they had built themselves with Claude and the value they had already generated. It was not the technology team presenting a roadmap. It was the business owners showing what they had made. That distinction is the one that determines whether an AI rollout produces adoption or produces a tool that 85% of the workforce quietly stops using after 90 days.
Kelley closes with an argument about the future of AI in leadership development that is one of the more original takes the podcast has captured this season. Executive coaching has always been costly and difficult to access, which means it has historically been available only to a narrow slice of any organization. AI changes that by giving anyone at any level a tool they can teach who they are, what they are trying to achieve, and what challenges they are working through. Kelley is careful to note that it works best in partnership with human mentors, not as a replacement for them. But the democratization of access to that kind of reflective, feedback-oriented thinking is a genuine shift in what leadership development can look like inside an organization. Paired with her personal board of directors model, which she recommends to everyone she mentors, it represents a framework for continuous growth that does not depend on seniority, budget, or having the right sponsor in the room.
Resources mentioned in this episode
CyberLynx Website
Kelley Kage on LinkedIn
Employer's Insurance Website
Darktrace Website
Sentinel One Website
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Check out previous episodes:
Machine Speed Attacks, Voice Agents, and Why Bad AI Excuses Fail with Keith Trawick - Ep 218
Why the Credit Union Peer Network Is a Security Advantage Banks Cannot Buy with Nico Stein - Ep 217
Deepfakes, Demos, and the Real Cost of a False Sense of Security with Chris Pacifico - Ep 216
Transcript:
Kelley Kage
CIO
Employers Insurance
Matthew Connor: Matthew Connor here, host of the Cyber Business Podcast. Today we're joined by Kelley Kage, CIO at Employers Insurance. Kelley, welcome to the show.
Kelley Kage: Thanks for having me.
Matthew Connor: Thanks for joining us. Before we get too far in, a quick word from our sponsors. Hackers are getting smarter — is your security keeping up? Cyberlynx sells industry-leading, AI-powered cybersecurity solutions that detect threats in real time, so you know about an attack before the damage is done, not after. Learn more at cyberlynx.com. And now back to our show.
Kelley, for those who aren't familiar, can you tell us about Employers and your role there as CIO?
Kelley Kage: Sure. Employers is a 113-year-old workers' compensation company operating nationwide. They most recently added a line of excess workers' compensation business as well. In my role as CIO, I oversee everything related to technology — data, product management, a lot of the operational execution and delivery of technology initiatives, and most recently a significant focus on AI, which is really exciting. And of course cybersecurity as well.
Matthew Connor: You said all of my favorite things in one sentence. Let's start with the intersection of AI and cybersecurity, because as an insurer, security is clearly top of mind — money draws hackers the way blood draws sharks. When I look at what's happening on the product side — things like Darktrace using machine learning in a very deliberate way, or CrowdStrike and SentinelOne integrating LLM-based AI into their SIEM products — I see some really exciting glimpses of where security is heading. As an insurer, how are you thinking about AI and cybersecurity together?
Kelley Kage: I love how you framed that. AI and cybersecurity absolutely have to go hand in hand as we figure out how we work going forward. The threat actors are already taking advantage of AI — attacks are moving faster and becoming more complex than anything we've seen before. But we have the opportunity to leverage AI on our side as well: in threat monitoring, in resolving vulnerabilities, in detection. And things like Claude's Metapath capability coming from Anthropic — exciting for us, but also sobering, because the same level of intelligence becomes available to the other side as well.
When I think about what this looks like longer term, AI governance plays a critical role. Right now, AI governance and cybersecurity are often managed as two parallel tracks. I think they converge. And I know governance isn't a popular word — people feel like it slows things down. But the framing I try to set is this: governance is not a speed bump. It is the on-ramp to where we need to go with AI. Building that governance in partnership with cybersecurity, legal, and HR — making sure we're thinking through all the implications, keeping our code and our company data safe — that is what allows us to adopt and deploy new capabilities much more quickly. We're not putting up guardrails for their own sake. We're doing it so we can move faster with confidence.
And that's where cybersecurity leaders play such a critical role — showing up not just from a vulnerability management perspective, but as active partners in how the business operates with AI. Technical and cybersecurity leaders have to be in lockstep with business partners.
Matthew Connor: When I think about the contrast between something like OpenAI's open agent framework and something like Claude's enterprise tools — it's almost a perfect illustration of your point. One is bleeding edge with no guardrails unless you build them yourself, and we've seen how dangerous that can be. The other has governance built in by design. Without that, the vulnerabilities are enormous. And if we're not using AI on the security side, we're bringing a knife to a gunfight — the bad guys are absolutely using it, and traditional tools alone won't hold.
Kelley Kage: Exactly. And I think the threat that gets me most focused is the zero-day attack problem. The bad guys are using AI to find new vulnerabilities faster than we can patch. You can't patch your way through AI-accelerated exploitation. The defense has to be behavioral — AI that understands what normal looks like in your environment and stops anything that deviates. When something unusual happens on the network, when an application starts doing something it's never done before — we need agents with the autonomy to say "this is abnormal behavior, I'm shutting it off until someone reviews it." That shift — giving agents that autonomous authority — can feel uncomfortable at first. But it's where we have to go, because human-speed response will never be fast enough for machine-speed attacks.
The human in the loop doesn't disappear; it just changes. Instead of humans doing all the initial research and detection, they're brought in to validate and act on what the agent has already flagged and stopped. We might hit some bumps as we adopt this new approach, but it keeps the company safer long term. And when we're talking to boards and stakeholders, that has to be framed in business terms — here's the potential dollar impact of not having this protection. It's effectively an insurance policy for your technology and your IP.
Matthew Connor: And I think using AI in security actually opens the door to broader AI adoption across the business, because it gives people something tangible and easy to understand. The ROI on AI in general has been a question mark for a lot of companies — they had high expectations, some even laid people off prematurely, and the results weren't what they imagined. But in security, the case is clean: we're fighting AI-powered attacks, we need AI-powered defenses, and when it works, you can point to specific threats that were stopped. That builds confidence across the organization. I think that's where the smart money is right now.
Kelley Kage: I completely agree. And beyond security, I think one of the mistakes companies make is treating AI as purely a technology initiative. At Employers, we are now fully rolled out with Claude across the entire organization. Every individual has access, has completed training, and has hands-on ways to leverage AI in their day-to-day work. But the reason it's working is that we didn't treat it as a tech rollout. We treated it as a change management and organizational transformation challenge.
We're about one to two months in, and we have 77% adoption across the entire organization. 90% of our people have completed the AI training we built and deployed. That didn't happen by accident. We started with our executive team and their direct reports, running them through an intensive program that didn't just teach them how to use AI — it taught them how to think differently about their business processes and how to bring AI back to their own teams. And then we capped it with a company-wide event in late March where business leaders presented what they had built themselves using Claude, and the value they were already seeing. It wasn't my team presenting — it was their leaders saying, "here's what I did with it, and here's what it means for how we work."
One of the most memorable moments from that event: someone stood up and said that when they first started, they thought AI was terrible — they'd ask a question and get an answer that was sort of right but not quite what they needed. Then they realized the problem was how they were prompting and the context they were providing. When they improved that, Claude started not just answering questions but pointing out things they hadn't thought to ask, suggesting angles they hadn't considered. They said they felt like their own knowledge was growing because of the feedback loop. That's the outcome you're going for.
Matthew Connor: That is spectacular. And it maps exactly to what Microsoft found when they rolled out Copilot internally — only 15% were still heavy users after 90 days. The ones who stuck with it were the ones treating it like a new employee or intern: bringing leadership and communication skills to the interaction rather than just treating it as a task-completion tool. But I'd also say Claude today is in a completely different league from where Copilot was when Microsoft ran that experiment. So your timing is really fortuitous — you're rolling out a tool that's powerful enough to reward that kind of engagement much more quickly and reliably.
Kelley Kage: That's a really good point, and it's part of why we chose Claude. Longer term, we want to route to the right model for the right type of work — we don't necessarily want our people to need to care which model is running in the background. But right now, Claude is the right tool for where we are, and the quality of what it produces has been a significant factor in that adoption rate.
What I keep coming back to is that the companies that are going to win with AI are the ones that don't look at it as a headcount reduction opportunity. So many organizations have massive backlogs of work, people maxed out at capacity. If you can use AI to create more capacity — to do the strategic things you've never had time for — the company grows. That's very different from cutting people and staying flat. I genuinely question the long-term future of organizations that approach AI purely as a cost-cutting tool. They're optimizing for the wrong thing.
And in a heavily regulated industry like insurance, a lot of people assume that means you're always going to be behind. I see it differently. This is an opportunity to move to the front and partner with regulators to build what the future looks like rather than waiting for them to mandate it.
Matthew Connor: Let me ask you about AI and leadership specifically — and mentorship. Because as AI takes over more cognitive tasks, the human premium shifts toward things like strategic thinking, creativity, and leading people. Where do you see AI's role in leadership development going forward?
Kelley Kage: This is a topic I'm genuinely passionate about. I have a deep commitment to mentoring and coaching the leaders around me, and I think AI has created something we haven't had available before: it makes coaching and mentoring accessible to everyone. Executive coaching is expensive and hard to access. Now anyone can have a tool at their fingertips that learns who they are, what they're trying to accomplish, and can offer thoughtful perspective and best practices.
That said — don't take what AI gives you and run with it uncritically. It's best used as a complement to human mentorship, not a replacement. What it does beautifully is help you approach situations differently, think through challenges from a new angle, or surface questions you hadn't thought to ask.
I tell people I have a personal board of directors — individuals across different industries and roles I can go to with real questions and get genuinely different perspectives. AI gets plugged into that board as another voice. It's not the same as a human mentor with twenty years of experience and real stakes in the outcome, but it's a valuable additional perspective that's always available.
And I think the opportunity here is similar to what the internet created. When the internet first rolled out, it was uncharted territory and we all got to help design what that future looked like. We're in that same moment again. The leaders who will be most successful are the ones who embrace that, think strategically about what they want to build, and help their teams move in that direction with intention.
Matthew Connor: Someone raised the question of brain rot — that as AI does more of the thinking for us, we atrophy the skills we were training. We've seen it with smartphones and the decline in remembering phone numbers. Does more AI mean more cognitive atrophy, or does it free us up to focus on genuinely human capabilities?
Kelley Kage: It depends entirely on how you engage with it. When people first discover what AI can do — creating slide decks in minutes, summarizing hours of content, drafting things that used to take an afternoon — it's easy to get over-stimulated and let the tool do all the thinking. That is where AI slop comes from: the 20-page document that should have been a two-sentence email. If you stay at that level, yes, you can drift into brain rot.
But that's the learning phase. Just like learning to drive — nobody's an expert the first day they sit behind the wheel. You have to keep practicing, develop your judgment, learn the nuances. The same is true with AI. If you're just using it to generate outputs without critically engaging with what it produces, you're not developing anything. If you're using it to think harder — to challenge your own assumptions, pressure-test your reasoning, explore angles you wouldn't have considered — then it makes you sharper, not softer. The goal is getting people to that second level, where they're genuinely partnering with the tool rather than just offloading to it.
Matthew Connor: Where do you see all of this going for Employers over the next few years?
Kelley Kage: My goal is for Employers to be AI-native. Right now we're working toward AI-first — and that takes time to truly embed into daily operations. Just like a Tesla can't do those evasive maneuvers on day one, a lot of what we're building with AI needs to train on the right data and the right outcomes before it performs at that level consistently. The governance piece is critical to getting there — making sure the accuracy and quality are there before we hand over more autonomy.
What I see in the one-to-two-year horizon is models that have learned what Employers specifically needs — not just generic large language model capability, but something that understands our business, our customers, our agents, and our injured workers. Over time, I want AI embedded into every aspect of how we work: improving the customer experience, improving the employee experience, and personalizing interactions in a way that meets people where they actually are.
Workers' compensation is deeply personal for the people involved. When someone is injured and navigating that process, meeting them with the right information at the right moment — with real empathy built into the interaction — that's where AI can genuinely make a difference in people's lives, not just in operational efficiency. That's the version of this I'm most excited to build.
Matthew Connor: That is a perfect note to end on. Kelley, this has been a fantastic conversation. Before we go, can you tell everyone where they can find out more about you and Employers Insurance?
Kelley Kage: Absolutely. employers.com is a great place to learn more about Employers Insurance, our workers' compensation products, and our excess workers' compensation offerings. And please feel free to reach out to me on LinkedIn — I'd love to connect and hear other people's thoughts on what we talked about today.
Matthew Connor: Fantastic. Thanks so much for coming on, Kelley. Until next time.