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How is AI currently being used within Moody’s Analytics, and what impact has that had on the day-to-day experience of employees?
AI is here, it’s accelerating, and it’s fundamentally changing how work gets done. If we don’t adapt quickly, we’ll risk falling behind. At Moody’s Analytics, I think about AI in two lanes: how we use it internally to improve how work gets done, and how we use it to enhance the products and services we deliver to customers.
Internally, the focus has been on reducing friction in how products are built and delivered - fewer handoffs, earlier quality checks, and clearer ownership, while maintaining the analytical rigor and context that define our work. At a high level, we’re modernizing how teams work, and early results show significantly faster product development cycles and fewer defects.
For employees, that often means less re-work and more time spent on higher‑value problem solving, with AI supporting - not replacing - human judgment. That makes day‑to‑day work more efficient and more rewarding. This isn’t a one-off effort; the expectation is that we continue to build momentum.
The competitive window for getting this right is narrowing. Organizations that treat AI adoption as optional will find themselves structurally disadvantaged within a few years, not a few decades.
Organizations that treat AI adoption as optional will find themselves structurally disadvantaged within a few years, not a few decades
From a people team perspective, how are you balancing AI adoption with employee engagement and trust?
For me, trust comes from transparency and investment. Employees don’t want vague reassurance - they want clarity about what’s changing and confidence that the company is investing in their growth and development, not just new technology.
We focus on building capability through training, being open about how roles may evolve, and setting clear guardrails around responsible use. We’re explicit that AI outputs must be accurate, explainable, and governed, and that human judgment remains essential. I’m very candid that change is part of the future, but I’m equally clear that when AI is grounded in trusted data and strong governance, it can create meaningful opportunity for employees.
Have you seen any measurable impact of AI tools on retention, particularly among early-career or highly technical talent?
It’s still early, and our overall attrition is already low, so it’s hard to isolate AI as a single driver. What we do see is that employees care about working in environments that are future-focused, using the newest tools and technology and that we are invested in their development. AI is part of that signal, but it’s the broader commitment to growth that really matters.
I’ll be direct with employees: those who engage early and build these capabilities now will have a meaningful advantage over those who wait
What steps are you taking to ensure employees feel confident and supported as AI becomes more embedded in workflows?
I take a role-based approach. Not everyone needs the same depth of training, so support is tailored based on how closely a role intersects with AI. Everyone at the company receives foundational learning on what AI is and how we expect it to be used responsibly.
For teams more directly involved - particularly in product and engineering - training goes deeper and is very hands on. Confidence comes from clarity and practice, not one-off training sessions, and we’re continuing to build on this as AI becomes more integrated into daily workflows. So, I’ll be direct with employees: those who engage early and build these capabilities now will have a meaningful advantage over those who wait.
How is AI influencing career paths and internal mobility?
It’s early, but I’m already seeing roles start to blend. Product roles becoming more technical, technical roles requiring more business context. Over time, I expect this to support more flexible, skills-based career paths rather than rigid, linear ones, which should expand internal mobility and provide far greater opportunities for our employees.
Are you finding that AI adoption is changing the skills profile you look for when hiring, and how does that affect workforce planning?
Yes, and this is an area where speed and adaptability really matter because the landscape is constantly changing. In some jobs, AI literacy is the expectation; in others, deep AI expertise is the role itself. But that bar is only rising.
From a planning perspective, we’re constantly assessing which skills we should build internally, where we need to hire externally, and how fast we need to move. I see this as evolving roles thoughtfully, not replacing them.
There’s a real expectation to lean into change, but that must be balanced with sustainable workloads, prioritization, and strong people leadership
How do you approach wellbeing in a workplace where AI is accelerating the pace of work and expectations around productivity?
This is something I care a lot about. AI can reduce repetitive work but it can also create pressure if expectations aren’t managed carefully – especially as the pace of change continues to accelerate.
There’s a real expectation to lean into change, but that must be balanced with sustainable workloads, prioritization, and strong people leadership. Productivity matters, but so does how people experience their work.
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What concerns are employees raising about AI, and how are you addressing those through communication and leadership?
The most common concerns are job security and how decisions will be made as AI becomes more embedded. I’ve found that avoiding those conversations creates more anxiety, not less.
We address concerns directly by being transparent about role evolution, emphasizing reskilling and redeployment where appropriate, and reinforcing that responsible use and data protection are nonnegotiable. Overall, the sentiment of our employees that are fully engaged with AI is overwhelmingly positive. They are energized by using the newest tools and technology and are excited for what the future will bring.
How are managers being equipped to lead teams through AI-driven change while maintaining engagement and performance?
As with any change, managers are critical. We invest in helping them understand the business strategy and imperative behind AI adoption and how to talk about it with their teams.
That includes change-leadership training, clear messaging, and reinforcing that the expectation is that people will lean into this opportunity, but of course, within clear guardrails. Managers need to be honest that this is moving fast, and everyone has a role to play. When managers are confident and aligned, teams are more engaged.
Looking ahead, what does responsible AI usage mean for Moody’s Analytics from a people, culture and governance standpoint?
Responsible AI starts with trust in our data, our models, and our decisions. That means protecting information, ensuring outputs are accurate and well-sourced, and being clear about when human judgment is required.
