In this installment of the IT Leaders Fast-5— InformationWeek’s column for IT professionals to gain peer insights — Ryan Downing, vice president and CIO of enterprise business solutions at Principal Financial Group, describes how AI is reshaping the way work gets done across IT, how Principal is guiding employees through the AI-driven change, and how technology strategies must evolve as a result.
As Downing explains, success now — and into the future — depends on organizational resilience and the ability to reevaluate and pivot as AI tools mature. He also shares his thoughts on a future that may include placing data centers in space, which he views as a reminder that “technology is all about working within constraints — and once you hit a constraint, asking whether you can work with it or need to find a way around it.”
Downing joined Principal Financial Group in 2004 as an IT application associate and has since held multiple positions at the company, including CTO and IT director. He has served in two CIO positions at the company and has been in his current role since 2021.
The Decision That Mattered
What recent decision — technical or organizational — has made the biggest difference, and why?
We recently finalized a decision that was a substantial pivot for one of the AI solutions we had delivered to the business over the last year. It signaled a really healthy debate and dialogue about how we constantly go back and reevaluate what we’ve built to make sure we’re looking at what’s the next best thing.
And in doing so, it uncovered important [insights] into how we help our teams through change management. Our teams have done great work here — it’s not that anything was done wrong — but [AI solutions] have to evolve, which means we have to be able to figure out what the next best thing is.
For me, this was a big decision we worked through that will be impactful in a very positive way. It reflects a healthy organizational maturity in how we think about AI and how AI is causing the lifecycle of solutions to get much shorter.
The Hard-Won Lesson
What didn’t go as planned recently — and what did it force you to rethink?
We’ve started layering AI solutions into [IT] teams to help transform the way they work; what that’s uncovered is that there are uneven fundamentals across teams.
And what we’re seeing is when teams don’t have strong fundamentals in their practices, SDLC, in how they measure efficiency and how they think about delivering value, the lift they get from AI is inconsistent.
[As a result], we’re rethinking how we evaluate teams’ fundamental practices as we introduce more AI capabilities. This is specifically in IT, but we have seen it in other teams as well.
As we look at how we’re bringing AI solutions into [other areas of work], we’re working through how we find better ways to objectively assess where teams are at in their practices, so we can get the right training, the right coaching and the right tools to personalize the learning journey for each team.
It’s still in the works, and we’ll iterate on it as we learn.
The Talent Trade-Off
Where are you investing in talent right now — and what are you consciously not investing in?
One of the main things we’re investing in is enterprise-wide AI literacy, so every employee — not just our technical teams — gains skills in how to use generative AI, how to do prompt engineering and how to build their data literacy. We really believe that AI is going to be fundamental to everybody’s work going forward.
The other place we are investing is in change management: building resilience within our teams and helping people understand how their work will change. People have been doing these jobs for decades in some cases, and they’ve been very good at them, yet now we’re asking them to change. AI is changing how they add value, and the way they add value in the future is also going to change — so how do we help them through that?
On the non-investing side, we’re not investing in a separate AI team. Instead, we’re really thinking about how we build those capabilities throughout the organization. While we certainly are building expertise to help different parts of teams, we’re trying to avoid creating bottlenecks for getting AI work done. AI skills need to be embedded in all the capabilities across the teams.
The External Signal
What external development this week is most likely to change how your organization operates, even indirectly?
There’s a lot of reporting recently around Apple’s reengineering of Siri to use Google Gemini, which I think was a really fascinating development. To me, it’s a pretty strong signal that we’re starting to see the commoditization of the AI models.
It suggests Apple is really focused on how it builds and maintains a strong platform — with a huge customer base and tons of loyalty — architecting it as such that they can use some of those back-end AI capabilities but still own that user experience. So what it draws in my mind is the growing importance of the platforms that users engage with, regardless of what might be engaging them.
These platforms are already well established and have excellent capabilities for their users. [The opportunity for] their owners — including financial services companies — is to think about how to bring AI into the [existing] platforms, not about building new platforms.
That’s sparked a lot of thought about how we architect platforms so that if the model evolution continues to happen, and we have model A right now but in six to nine months decide the best is actually model B, we can decide how quickly we want to respond to that.
The Perspective Shift
What have you read, watched or listened to recently that changed how you think about leadership or technology — even slightly?
I’ve been reading a lot about data centers in space. You think, “That’s wild,” but then you start to look at it and think, OK, is that an answer or a potential solution to the long-term environmental impact of the compute needs of AI? And what does that mean from an energy perspective, and what does that mean from a water consumption perspective? Does that create a way that is less impactful going forward?
Of course, you’ve got to ask about the long-term effects of putting data centers in space, and those would have to be explored as well. But for me, it’s a good reminder that technology is all about working within constraints — and once you hit a constraint, asking whether you can work with it or need to find a way around it.
Data centers in space represent a pretty radical way to address some of the environmental constraints we could face on Earth. And it’s inspiring when you think about the complexity of doing that and how engineers can come up with different ways of thinking that’s completely outside the box.
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