When Leadership Means Learning: Inside Twilio’s People Strategy

In this edition of Egon Zehnder’s CHRO Voice series, Christy Lake, Chief Administrative Officer of Twilio, shares what it takes to lead through uncertainty, from building trust in a remote-first world to navigating AI adoption and shifting career expectations. Her perspective is clear: Discomfort often signals growth. In times of rapid change, trust becomes the foundation for experimentation, learning, and resilience.

Through the years leadership has evolved from command-and-control to empowerment and collaboration to now deeply human and empathetic management. The rise of AI in our work means that as CHROs, we’re not only expected to lead, guide, support, and develop people through these new ways of working; we also need to upskill, retool, and leverage the same technologies to excel at what we do.

It feels like a brave new world where many things can, should, and are being done very differently, which can be deeply uncomfortable. This shift coincides with a period of historically low attrition rates, whether due to “job hugging” or other factors. So, there’s this tension: We know we need to embrace new ways of working, but honestly, it feels daunting.

We went remote during the pandemic and decided to remain committed to that path. One of the pillars of our approach, which we call Open Work, is connection. We believe that focusing on building and sustaining connections in a remote world is the only way to create the trusted relationships needed to get work done. We emphasize this constantly, even surveying employees quarterly on whether they feel connected at the team level, with favorability scores consistently being in 80-90th percentiles.

Our focus has been on creating a culture that supports creativity, experimentation, and innovation in a high-trust environment, while ensuring people feel supported. We also focused on democratizing AI learning and reducing fear, making it fun through things like hackathons where all teams, including nontechnical roles, can participate. We provide safe parameters, training, and a learning community to lower the barrier to experimentation and shift mindsets from “I want nothing to change” to “I’m willing to try.”

For me, the clearest indicator is intentionality. Developmental activities, giving feedback, spending time with new hires and tenured employees are tangible signals. I’m fortunate to work with a CEO who truly lives this. The say-do ratio is evident in the time he invests in talent reviews, individual career conversations, as well as in our ongoing discussions about culture. These aren’t annual check-the-box exercises; they’re part of the natural course of business.

The deeper impact happens at the philosophical level: aligning on big questions: Are we building talent internally or buying externally? How do we think about mobility, time-in-role, and culture—who we are today and who we want to become? Everything flows from those conversations—budget, programs, policies. That’s where the most meaningful (and fun) work happens.

HR absolutely can lead, but it should be a partnership. Enterprise tech often owns systems and platforms, so decisions about investments need to be made collectively, and our legal team has developed guidelines for using AI responsibly. From an enablement perspective, this is both change management and reskilling, which is our bread and butter in HR. We can drive cultural aspects, like making adoption feel safe, and lead the skilling piece across the organization.

There are several areas. First, AI embedded in our products has driven great success with customers and how we position our platform. Internally, the biggest wins have been in highly repeatable, knowledge-based work, such as customer support. Our internal chatbots effectively answer employee questions and suggest next best actions. We also have an AI agent handling 95% of our inbound customer leads successfully. Other strong use cases include procurement and contract redlining, where we train LLMs on a knowledge base and apply them to repetitive tasks. These applications have unlocked significant productivity for individuals and teams.

Early on, the biggest challenge was figuring out what we could and couldn’t do, and where to engage responsibly. Once that hurdle passed, the next challenge was data. AI is only as good as the data it’s trained on, and if your data is scattered, outdated, or inconsistent, results suffer. Many organizations, including ours, have had to clean up old policies, consolidate repositories, and ensure data is well-organized and tagged.

For at least the next two years, the external market is not very robust, and low attrition means tenure is increasing. That brings benefits, such as deep knowledge and relationships, but also challenges. Most of us grew up expecting that learning new skills leads to a promotion or role change, yet I see a pause. For now, simply keeping your job often means upskilling because expectations are rising, companies grow but headcount stays flat because AI boosts productivity. That tension will need to break. What happens next? New jobs will emerge as they always do in these cycles. Roles we can’t fully imagine today, like “agent managers” overseeing AI systems, will create fresh opportunities and unlock growth. I can’t predict exactly what they’ll be, but a shift is coming.

Last year, we invested in building a skills infrastructure to support our career growth and development framework. We implemented an AI-powered platform to identify existing skills within the organization and track emerging skills in real time. I’ve seen data suggesting the shelf life of skills is now closer to 14 months. Having this infrastructure helps us understand what skills are needed, what we have, where the gaps are, and how to close them. AI plays a big role in surfacing that insight. This investment is foundational: first, put the systems in place; then learn what we have and what’s happening in the market. From there, we can connect gaps to learning and create a data-driven approach to upskilling.

My biggest advice is to identify the critical few priorities that deserve your attention and separate them from the longer wish list. My CEO often says, “What needs to be an A, and what’s OK as a C?” If you inventory all the work in the function, you’ll find a long list of things you’d love to improve. But chasing the long tail can distract from what matters most for the business, culture, and employees. There’s an art to focusing on high-impact priorities and putting others in maintenance mode. We love all our programs and policies, but sometimes it’s okay if they don’t get all your attention. That’s a skill you learn over time. If I could give myself advice starting out, it would be: don’t chase everything equally; focus on what truly drives impact.