How are insurance carriers recruiting talent to work with AI? What do these departments look like and how are they structured?
The following responses have been lightly edited for clarity.
Richard Wiedenbeck, chief AI officer at Ameritas and Heidi McMillan, manager of talent acquisition at Ameritas
Q: What does the team structure and department for AI look like within the company?
Wiedenbeck: [I'm] focused on capturing the value from AI, ensuring we govern it responsibly, and work to prepare employees and leaders for the future of AI.
The team structure we are using at this time, mainly due to its startup-like nature, is a centralized center of excellence (COE) with working liaisons from each business area and shared services functions. Our COE is closely integrated with IT and most of our delivery teams are a blend of technology, AI, and business.
Q: What does the future of AI-related roles within the company look like?
Wiedenbeck: We are working on a framework and game plan for how to build, develop, and mature the workforce of the future in step with our maturity of understanding and use of AI.
However, we are seeing an immediate need for AI engineers, which may not be the best word to use, but is what we are calling them. This new role is basically the AI expert that can understand how the AI works, can help business leaders extract value from it, can work with IT and others to design the optimal solution for achieving the outcomes and benefits we are looking for and ensure we are delivering on that outcome. We have defined this profession in a comprehensive way, so that we can build skill variations and emphasize the different skill and competency profiles we will need. A close example of this would be something like a business analyst who can have deeper skills in data analysis, process analysis or requirements analysis, etc. The AI engineer role is different from the traditional data scientist role, in that it has more of a consulting emphasis that pure data science work would not have.
Q: Can you explain the strategy and overall vision for hiring AI talent?
McMillan: We use a similar strategy to attract this talent as the other talent. It is important to share that we provide meaningful work that ties back to our purpose-driven company. This area will help to catapult our organization into the future and is helping to reshape the future workforce for the organization in the digital worker space.
Q: Where do you find new candidates for tech-related roles?
McMillan: We find our new talent from a variety of areas. When hiring early career talent, we leverage our university partnership and our strong internship program to bring in top talent. For higher level niche talent, we will leverage sourcing, networking, niche job boards and networks to identify the needed talent.
Q: What is the hiring process and how do you assess technical skills?
McMillan: We conduct a thorough vetting process to assess talent and skills when hiring, leveraging performance and competency-based interviewing along with assessments to identify top talent for the role.
Bob Bastian, chief information officer, head of data, service & technology, U.S. businesses at Prudential Financial, Inc.
Q: Can you explain the strategy and overall vision for hiring AI talent?
Prudential's strategy for hiring AI talent focuses on both upskilling our existing workforce and attracting new experts. We believe our success depends on the collective capability of our people; by investing in our current employees, we enhance their AI capabilities and foster innovation within the company. At the same time, we seek to bring in new talent with specialized AI skills to drive our strategies forward, while providing an external perspective. This balanced approach ensures we build a dynamic AI talent pool that supports our strategic goals and delivers exceptional value to our customers.
Q: Where do you find new candidates for tech-related roles?
We look for candidates for tech-related roles through a multi-faceted approach. First, we identify and nurture talent within our current employee population, recognizing the value of their institutional knowledge and commitment to our company's vision. We also leverage technology in our recruiting practices, using a variety of AI and analytics tools to better search for and engage talent.
Lastly, we have built a robust early talent pipeline through our internship and rotational program, driven by strong relationships with colleges and universities. This allows us to tap into the innovative ideas and cutting-edge skills of recent graduates. This comprehensive strategy ensures that we attract diverse and highly skilled people to drive our technological advancements.
Q: What does the team structure and department for AI look like within the company?
Within our U.S. Businesses, most AI experts are part of our cross-functional Agile development teams. These teams foster collaboration, flexibility, and rapid adaptability. They break down silos, enhance problem-solving, and deliver comprehensive solutions, fostering continuous learning and improving our agility to respond quickly to market changes and customer needs. This integrated approach helps us achieve our strategic goals and deliver exceptional value to our customers
AI roles are increasingly important. Data scientists and machine learning engineers remain pivotal, developing complex solutions and integrating AI into broader strategies. New roles like Prompt Engineers, Responsible AI Experts, and AI Integration Specialists will ensure ethical and efficient AI adoption. We also upskill our workforce to democratize AI-related tasks and foster innovation.
Q: What does the future of AI-related roles within the company look like?
The future of AI-related roles at Prudential involves expansion, evolution, and collaboration. We anticipate a growing demand for specialized AI talent, transforming existing roles to incorporate AI tools, and a workforce skilled in leveraging AI for better decision-making and enhanced customer experiences. By investing in both people and technology, we aim to lead in an AI-driven financial services landscape, delivering greater value to our customers while staying agile in a rapidly changing environment. Additionally, our teams share new AI technology and build reusable AI solutions, ensuring AI readiness, embedding AI into design, building AI applications, and fostering AI-driven thinking.
AI is a seismic shift redefining financial services, and we're investing to lead this transformation. Our talent is catalyzed through upskilling, where they innovate alongside AI. We're embedding AI into our core to free our teams for high-value challenges, making our business smarter, faster, and more human-centric. We're not just reacting to the future; we're creating it, leveraging AI to amplify human potential while upholding ethical standards.
Darryl Siry, chief operating officer, CoverWhale
Q: How is AI embedded in the hiring strategy at the company?
It was kind of obvious that we are going to invest in AI of all kinds because, like we do predictive analytics, machine learning, generative AI, we do all of it. The thing that makes us different is our ability to use analytics to underwrite and manage risk better, so that we can get a better loss ratio. So, getting top talent in analytics and AI is critical for us. The advantage that I had was because I had started in the industry doing analytics, I had a pretty good network, and Karthik Balakrishnan, the chief AI & analytics officer at CoverWhale, was one of my machine learning modelers at Fireman's Fund Insurance Company when I was building that group. He has had a full career in various roles and so I reconnected with him because I thought he'd be the perfect person.
I think the mistake some people make is that they go try to hire for the skillset and not the industry domain knowledge. So they'll go and they'll say, I need to hire a PhD in AI. But if they don't understand insurance, they're going to spin their wheels there.
For me, someone like Karthik, with various insurance domain expertise and the analytical skillsets and expertise, can actually tackle the problems more effectively. Because I think with all analytics, and this has been true for the decades that I've been involved in it, a big part of it is really data, and the quality of data, having, having data itself available to you, and then setting up the problem, and then the actual running the analysis and building the models is really, I wouldn't say, the easy part, but it's after you've kind of done the hard work of data. And so, as people go out there, you really do need to find people who understand insurance or can learn it quickly.
This industry has been looking at risk and loss data for hundreds of years. Statistics have existed for a long time but computing power has recently exploded in scale. So, I think that's what's changed the game. The actual techniques and the actual kind of statistical understanding of data has not evolved so rapidly.
Q: How do you assess whether someone has the technical skills and insurance experience?
You need to ask people questions about what problems they've solved, not what tools they have used. I think the tools have become somewhat commoditized, and anyone can learn how to use ChatGPT or Claude.
The question is, what problem did you solve, and how did you solve it? If you're talking to somebody who says that they are a data miner, or an analytics person, what problem did they solve? If they say fraud detection, then tell me about that. The story has to always start with the data, so what data did you use? How did you define fraud? How do you train a model on something that is not an objective variable?
Q: Do you anticipate as technology becomes more accessible there will be a shift in the industry?
I think it's generational. I think that the senior executives, the C-level people, the CEOs grew up in an industry or their careers developed at a time when technology was not as critical to the business strategy. But when you have people who are in their 30s or 40s, who grew up with the internet, who grew up as technology natives starting to to be the C-level people. I think that they will hire people around them on the business side that make technology an integral part of the business, and not just a service provider.
I've had a lot of conversations with colleagues of mine and the reason they're skeptical about technology is because so many of the technology providers have been so ineffective. You're rightfully going to have a healthy dose of skepticism as to the value and the payback of technology.
Managing general agencies (MGAs) tend to be these places where the industry is highly innovative, and they're highly innovative from an underwriting standpoint, from a sales and distribution standpoint, and now more so than the big enterprises, in technology. I think that you have MGAs being formed, being bought, being absorbed into the cultures and operations of large insurance companies, and then those entrepreneurs will start a new MGA. And so I think it's almost like the DNA of innovation in the insurance industry comes from that dynamic, and it's a healthy thing. I think that dynamic has been playing out in an evergreen way for as long as I've been in the industry.
