(Guardian Life reception area with views of the High Line at Hudson Yards. Image source: Perkins & Will.)
Insurance technology professionals had little reason to expect the pace of change to slow. Well before the rise of generative AI, IT organizations were already adapting to cloud adoption, product-based delivery, heightened cybersecurity expectations and a growing reliance on data to run the business.
AI has introduced a new degree of acceleration, compressing what had been a steady evolution of skills into a more urgent reconsideration of technology talent, operating models and business accountability. For insurers, that shift is not simply about adding AI specialists. It is about preparing technology teams—and the broader enterprise—to understand where AI can create value, how it can be applied responsibly and how it changes the relationship between technical skill and business expertise.
Steve Rullo, Chief Digital and Technology Officer, Guardian Life.
Steve Rullo, Chief Digital and Technology Officer, Guardian Life Insurance Company of America (New York), acknowledges that the skills required of technology organizations were changing rapidly even before AI became the dominant technology topic. At Guardian, he says, those demands were already evolving “from cloud adoption and a move to product-based delivery, to heightened cybersecurity expectations and a significantly greater reliance on enriched data to run the business.”
“As the work changed, so did the way we described it,” Rullo says. “We intentionally moved from thinking of ourselves as ‘IT’ to ‘Digital & Technology’ to better reflect how our teams partner with the business to build digital experiences, modern platforms, and data and AI capabilities to drive growth.”
That shift was already making Digital & Technology more cross-functional and outcome-driven, with stronger business fluency and clearer ownership required across the organization, according to Rullo. AI has not replaced that evolution so much as intensified it.
Accelerating Pace and Expectations for Skills
“AI is accelerating both the pace and expectations for skills across Technology organizations,” Rullo says. “Over the past decade, skill evolution has been more linear. As cloud, data, security, and modern engineering platforms advanced, teams built deeper expertise within their respective domains.”
The difference now, Rullo suggests, is that AI is causing those domains to converge more quickly. Engineering, data and product skills are coming together in real time, while AI literacy is becoming a broader requirement rather than a specialized competency.
“With AI, that progression is happening much faster and more interconnected,” Rullo says. “Engineering, data, and product skills are converging in real time, and AI literacy is becoming a baseline expectation, not a specialty.”
The implications extend beyond the technical organization’s ability to deploy new tools. AI changes what it means for technology professionals to be effective, in part because new capabilities can be created more easily than in the past. That does not make technical skill irrelevant, but it does raise the importance of business knowledge, judgment and creativity.
“And candidly, that changes what it means to be effective,” Rullo says. “It is actually becoming easier to build new capabilities. We don’t need to be an expert in everything, but we do need to understand what AI can do, how to apply it responsibly, and where it creates business value.”
Rullo adds that technical skills remain important, but he sees business domain knowledge and creativity becoming increasingly valuable as AI changes the way capabilities are built. In that environment, curiosity and willingness to learn become differentiating traits.
“Those who are curious and invest in building those skills will be the ones who thrive,” Rullo says. “The winners will be the teams who can move faster without sacrificing trust, controls, or compliance.”
AI as a Business Portfolio
Guardian’s approach to AI begins with business strategy rather than technology for its own sake, according to Rullo. The company is looking at the business challenges AI can address and the customer experiences it can improve. He identifies transforming workplace processes, elevating customer and advisor interactions, and increasing operational efficiency at scale among the areas of focus.
“At Guardian, our approach begins with our business strategy and how to enable that strategy with AI,” Rullo says. “What are the business challenges that can be addressed and the customer experiences that can be meaningfully enhanced?”
The potential magnitude of AI-driven change is considerable, Rullo says, but he emphasizes that value depends on disciplined execution. In his framing, AI value is realized only when the technology is connected to data governance, business ownership and clear accountability for outcomes.
“While the potential is considerable, value is truly realized only when AI is integrated with disciplined execution, robust data governance, and well-defined business ownership of outcomes,” Rullo says.
Guardian is treating AI as a portfolio that includes broadly deployed productivity tools, targeted use cases and larger business transformations. Rullo cites Copilot and Claude as examples of productivity tools in use, but places them within a broader framework that includes targeted applications and end-to-end transformations supported by a data and AI platform.
“That’s why we operate AI as a portfolio, from broadly deployed productivity tools like Copilot and Claude, to targeted use cases and end-to-end business transformations, underpinned by a strong data and AI platform,” Rullo says.
The company has drawn several operating principles from its experience so far, according to Rullo: focusing on value, ensuring business ownership of outcomes, empowering small teams and iterating quickly through experimentation. In the near to medium term, he sees the greatest impact in reimagining service, underwriting and claims to support growth, improve customer experience and increase operating efficiency.
Building AI Capability Through People
Guardian’s preparation for AI is also a talent-development effort. Rullo says the company is applying the same discipline to people development that it applies to the technology itself, with an emphasis on practical AI literacy, data fluency, engineering fundamentals, product thinking and knowing when human involvement is required.
“To get maximum value from AI, we’re investing in our people with the same discipline we apply to the technology itself,” Rullo says. “Our focus is on building the skills that matter most in an AI-enabled enterprise, including practical AI literacy, strong data fluency, engineering fundamentals, product thinking, and knowing when to involve humans.”
Within Digital & Technology, Guardian is developing those capabilities through early career programs, enterprise-wide AI literacy efforts and targeted upskilling tied to real work, according to Rullo. He stresses practical experience rather than abstract training alone.
“Just as importantly, we emphasize learning by doing,” Rullo says. “AI is embedded into everyday workflows so teams build confidence and fluency through practical application.”
That workforce strategy also requires collaboration beyond Digital & Technology. Rullo says Guardian works closely with human resources because AI-related talent development is an enterprise issue, not only a technology issue.
“We partner closely with HR because this isn’t just a technology talent conversation,” Rullo says. “It’s an enterprise capability shift.”
HR is helping shape how leadership, the workforce and Guardian’s operating model evolve with AI, Rullo says. Together, HR and Digital & Technology are working to update role expectations, skills and development paths in ways that are practical and grounded in the company’s operating needs.
Within Digital & Technology, that work includes clarifying which roles remain critical, which activities can be augmented with automation and where upskilling is most needed. Rullo also points to Guardian’s operating model, which embeds Digital & Technology teams directly with the businesses as a way to support more intentional lateral movement across the enterprise.
“Because our operating model embeds D&T teams directly with the businesses, we’re enabling more intentional lateral movement across the enterprise,” Rullo says. “That allows skills and knowledge to transfer naturally, helping us build capability at scale without creating new silos.”
More Versatile Technologists
Looking over the next five years, Rullo expects technology skills to evolve in several directions at once. Technical foundations will remain important, but business translation will become stronger and AI literacy will become a common requirement across roles.
“We’ll see deeper technical foundations and stronger business translation, as AI literacy becomes table stakes across roles rather than a niche specialty,” Rullo says.
Teams will also need to become comfortable building and operating products with AI embedded throughout the full lifecycle, from engineering and testing to support and operations. At the same time, Rullo expects roles and skills to continue converging. Engineers, for example, are developing stronger architecture and system-design capabilities that traditionally were associated with separate roles.
“The expectation is shifting toward more versatile technologists who can think end to end about how systems are designed, built, and scaled,” Rullo says.
The associates who stand out, in Rullo’s view, will be those who keep learning, frame the right problems and connect technology to measurable business value, while remaining attentive to risk, controls and responsible use. That combination reflects the broader shift he sees in enterprise technology: AI increases the importance of speed and adaptability, but not at the expense of governance or trust.
As Guardian adopts more AI and modern technology, Rullo expects the company to become faster, more digitally integrated and more data-driven. He also expects the company to continue moving toward more agile, technology-enabled ways of working and more seamless digital-first experiences for customers, producers and colleagues.
Opportunism—and Restraint
“At the same time, scaling AI the right way means strong governance, cyber controls, and a clear focus on the few areas where AI truly differentiates,” Rullo says. “Better decisioning, speed, and personalization.”
Restraint will be an important aspect of that focus. Rullo’s view of AI adoption is not that insurers should chase every new tool, but that they should identify the areas where AI can create lasting advantage and scale them with the right controls.
“The goal isn’t to chase every new tool, it’s to focus on a small number of scalable bets that create durable advantage, with the right governance and cyber controls built in,” Rullo says.