What do leaders need to do right now to build trust in AI?

They need to be very direct and transparent with their workforce. Employees have a right to understand where AI is being used, what it is intended to do, what data it relies on, and where human judgment remains essential.

We also have to acknowledge the uncertainty. AI will change work. Employees know that, and leaders know that. To build trust, we have to be open about that reality rather than simply reassuring people that AI will only make them more efficient or help them do their jobs better. There will be impacts.

At the same time, AI can be a force multiplier. It can help people spend less time on repetitive tasks and more time applying their expertise: serving clients, solving problems, and building relationships. It is about creating opportunity while also setting realistic expectations.

Communication has to be consistent and constant. At TIAA, it isn’t something that sits only with the technology organization. It is part of the enterprise conversation and how leaders talk about investments, technology, workflows, and the organization’s future.

How is AI changing the role of the CHRO?

In some ways, it builds on the strengths of the CHRO role: judgment, critical thinking, and an understanding of workforce dynamics. But in other ways, it has changed tremendously.

The role is increasingly about building enterprise capability, not simply managing traditional talent programs or workforce processes such as hiring and performance management.

I think about the workforce as a combination of skills, experience, technology, expertise, and human judgment. The question I ask leaders isn’t simply, “How many people do we need?” It is, “What capabilities do we need, and how do people and AI work together to deliver them?”

Then we have to determine whether we already have those capabilities, whether we can upskill or reskill people to develop them, or whether we need to bring them in from outside.

That also makes the CHRO an essential partner not only to the business leader, but to technology, risk, and legal. We have to think about governance, accessibility, and whether everyone has the opportunity to develop the skills they are going to need.

How has AI changed TIAA as an organization?

AI has changed TIAA, and it will continue to do so. Broadly speaking, AI can help an organization like ours deliver more personalized, responsive, and efficient experiences for clients, participants, and colleagues.

It can reduce the time people spend searching for information, summarizing complex materials, or drafting routine communications. But ours is a business centered on financial well-being and retirement, and the human connection remains incredibly important.

AI can help our colleagues be better prepared and more efficient, but it doesn’t replace the trust that is built through a thoughtful human conversation.

I think the greatest value of AI is not simply in automating work, but in redesigning it, using AI to remove friction and allow employees to focus on the moments where they can make the greatest difference.

That means looking at the workflow itself and asking how AI can improve it. It also means building confidence by giving employees the learning and training they need to understand the tools they are working with. Increasingly, AI fluency will be part of staying current and expanding people’s career possibilities.

How do you design AI around people rather than asking people to adapt to AI?

You start with the work, not the tool.

Ask employees: What creates problems? Where is there friction in the process? What consumes unnecessary time? What prevents you from delivering your best work?

Then you determine whether AI can help solve that problem.

Bringing employees into the design process early is incredibly important because they understand the practical realities, the exceptions, and the client needs that may not be obvious in a technology design session.

The goal should be to design AI to augment judgment, not eliminate it. People need enough context to understand what the system is recommending rather than simply taking an output at face value.

What does human-centered transformation look like in practice?

It begins with listening.

Before implementing new technology, leaders need to understand how work actually gets done and what employees and clients need most.

It also means including employees in the change rather than treating them as recipients. People are much more likely to embrace transformation when they have a voice in shaping it.

When you’re having a design session, don’t begin with, “Here is the tool. How are we going to use it?” Begin with the problem you’re trying to solve. Where could the technology augment the process? Where could it speed something up? Is there something we should eliminate altogether?

We sometimes call that clean-sheet thinking; rethinking how something should work from beginning to end.

You’re leveraging employees’ expertise and experience so they help shape the solution, rather than simply being told this is the new way they are going to work.

What has to be in place for AI adoption to really take hold?

First, you need a trusted data foundation. The data conversation sometimes gets lost when everyone is talking about AI, but AI is only as useful as the data, processes, and controls behind it.

We also need responsible AI governance that is clear, practical, and embedded in how people work. Employees need to know what is permitted, what requires review, and when they should escalate a concern.

Then we need broad AI literacy. AI isn’t just for technologists. Every employee should understand the basics of what AI can do, where it can be unreliable, how to protect sensitive information, and how to use it responsibly.

Managers are also critical because they translate enterprise transformation into day-to-day priorities, expectations, and career conversations. We need to equip them to lead people through this change.

And our measures of performance and value will need to evolve. As AI takes on more routine work, collaboration, judgment, innovation, and relationships become increasingly important. These aren’t simply “soft skills.” They move closer to the center of how the human workforce creates value.

Finally, learning has to become continuous. Employees have to own their learning journeys and be willing to become beginners again.

What will the future workforce look like?

We don’t have all the answers yet. We’re learning and ideating as we go.

But I do think the workforce will become more skills-based, more fluid, and centered around continuous learning. Job titles will still matter, but skills will matter even more.

I also think we’ll see new ways of working. People will move more readily across projects, teams, and roles based on the capabilities they have developed and the problems they can solve, rather than necessarily remaining within the same part of the business.

The employees who are most successful will have what I call an expansive mindset. They’ll continually ask: What else can I learn? How can I use these tools to improve my work? What capabilities will make me valuable in the next part of my career?

What capabilities become more valuable as AI takes on more work?

Judgment and critical thinking are essential. I also think curiosity becomes a competitive advantage.

Communication remains incredibly important because people need to translate complex ideas into clear language. Whatever output you get from AI, you still have to turn it into something meaningful for clients, colleagues, and leaders.

Empathy, relationship-building and connection matter. People may have more time for work that is creative, innovative and deeply human.

Resilience and adaptability are also critical because the pace of change isn’t slowing down. Employees who can learn, unlearn, and adjust will be in a much stronger position than those who become fixed in how they work.

I also think coaching and mentoring become more important, particularly for early-career employees. People will still need leaders and peers who can help them build confidence, identify their strengths, and interpret what AI is giving them so that they learn how to apply judgment.

Ethical reasoning matters too. Technology can accelerate decisions, but people still need to think about the consequences, fairness, and human impact of those decisions.

As organizations become faster and more technologically enabled, what do we need to deliberately protect for people?

Time to think.

Speed is valuable, but people still need time to reflect. Are we applying sound judgment? Are we being creative? Is there a better answer than the one AI is giving us?

AI can be the starting point or the jumping-off point for thinking. That doesn’t mean its answer is the right answer. People need the space to question it, disagree with it, and reconsider.

We also need to protect fair access to opportunity. AI can widen gaps if only some employees have the time, tools, sponsorship, or confidence to develop new skills. Leaders have to make learning and advancement accessible across the workforce.

And finally, we have to protect meaning in the work.

As automation increases, people still need to stay connected to the mission, to the impact they have on clients, and to the uniquely human value they bring every day.