Artificial intelligence is rapidly moving beyond experimentation to become a foundational capability shaping how organisations innovate, compete, and grow. For technology companies operating at global scale, AI is no longer simply another productivity tool, it is influencing how products are built, how decisions are made, how teams collaborate, and how talent is deployed. As organisations navigate this shift, long-standing assumptions about organisational design, workforce planning, leadership, and performance are being challenged by a world where learning cycles are faster, skills evolve continuously, and human-machine collaboration becomes central to value creation.
In this conversation with Laura Faith, Vice President, Human Resources, Uber Technologies, we explore what it takes to build an organisation that is truly AI-ready. Drawing on her experience supporting more than 9,000 engineers, product leaders, data scientists, and AI specialists globally, Laura shares her perspective on dynamic workforce planning, the growing strategic influence of HR in engineering-led environments, sustaining high performance without burnout, and the critical differences between organisations that simply adopt AI and those that become genuinely AI-native.
AI is both incredibly exciting
and, at times, unsettling. It opens up entirely new possibilities for how we
build and operate, but it also introduces a pace of change that can feel
unprecedented. That tension is important—it forces organisations to be very
intentional about how they adopt AI.
One of the most critical choices is being clear about what should be
machine-led and what should remain human-led. The real opportunity isn’t just
automation; it’s designing systems where human judgment and machine intelligence
complement each other in a way that elevates both.
From an organisational design perspective, one of the most fundamental shifts is in how teams work together. AI is collapsing what used to be sequential workflows into more integrated, real-time collaboration. Product, design, data science, and engineering teams now have the ability to prototype and iterate together, in the moment, rather than handing work off across stages. This changes the rhythm of work. It accelerates understanding, shortens feedback loops, and allows teams to align much earlier in the process. As a result, decision-making becomes faster, learning cycles tighten, and innovation speeds up. In an AI-first world, the organisation is no longer just structured for execution—it’s designed for continuous, collaborative learning at pace.
You lead people strategy for over 9,000 engineers, AI specialists, product and data talent globally. What does disciplined workforce planning look like at that scale, especially when skills are evolving faster than job titles?
The best tech organisations have always aspired to be flexible—able to move talent fluidly to where it’s needed most, matching critical capabilities with the highest-priority work. Historically, though, that’s been a largely manual exercise, often accompanied by friction, churn, and disruption for both teams and individuals.
What’s changing now is that AI gives us the opportunity to finally do this well—and at scale. We can move toward a model where workforce planning is dynamic and skills-based in real time, not something that happens in periodic cycles. With better visibility into both business needs and individual capabilities, we can more precisely match the right talent to the right problems as they emerge. That has the potential to unlock a step change in innovation and growth. Importantly, this isn’t just about organisational efficiency—it should also create a meaningfully better experience for employees. My aspiration is that this leads to a much smoother, more transparent system where people can more easily discover and move into their next growth opportunity, rather than navigating complex or opaque processes.
To make this work, though, organisations have to invest just as intentionally in continuous learning. If skills are evolving rapidly, then support for reskilling and upskilling has to be always-on—embedded into the flow of work, not treated as a one-time intervention. Ultimately, disciplined workforce planning in an AI-first world is about building a system that is both highly adaptive and deeply human—aligning business needs with talent in a way that drives performance while continuously expanding opportunity.
As you partner closely with CTO and CPO, how is the role of HR shifting from a support function to a strategic architect of business outcomes in engineering-led environments?
HR is increasingly becoming a co-architect of how the business operates, not just how it is staffed. In engineering-led environments, talent is the strategy—so decisions about skills, structure, and culture directly shape product outcomes. This requires HR to be deeply embedded in the business: understanding the technology roadmap, the product lifecycle, and the constraints teams face. It also means bringing a systems perspective—designing organisations that can scale innovation, not just execution.
For example, how you structure teams can determine speed to market. How you design career paths can influence whether critical AI talent stays and grows. And how you shape performance systems can either encourage experimentation or unintentionally penalise it. The shift is from enabling the business to actively designing for outcomes like speed, quality, and innovation—using talent as the primary lever.
High-performance tech cultures are often intense by design. How do you sustain velocity and ambition without creating burnout, especially in teams building and deploying AI at scale?
At Uber, our ambition is rooted in our Mission—To Reimagine the way the world moves for the better. In an AI-driven world, that ambition only increases, given the pace of innovation and change. The key is making that intensity sustainable.
Our values help anchor this. “Go Get It ” reflects a champion mindset—resilience and ownership in the face of rapid change—while “Build With Heart” ensures we stay grounded in care for our customers, communities, and each other.
We also focus on delivering a strong employee value proposition. That means creating an environment where people are surrounded by exceptional talent, working on real-world problems with meaningful impact, and operating at a pace that is energising. Just as importantly, we ensure there are opportunities for continuous growth, an inclusive culture where people can be themselves, and benefits that support the whole person.
In an AI world, where speed can easily become overwhelming, these elements matter even more. AI can accelerate how we work, but sustaining performance comes down to pairing that speed with purpose, support, and growth—so teams remain energised, not exhausted.
AI is transforming every function. How is it reshaping HR decision-making itself, particularly in areas like workforce planning, talent assessment and performance insights?
AI is moving HR from retrospective reporting to predictive and proactive decision-making. For example, in workforce planning, we can now model future skill needs with much greater precision, identifying gaps before they become constraints. In talent assessment, AI helps us move beyond static credentials toward a more nuanced understanding of skills, potential, and adjacent capabilities.
Perhaps most importantly, in performance and engagement, AI allows us to detect patterns that were previously invisible—whether it’s early signs of burnout, collaboration bottlenecks, or uneven workload distribution across teams.
That said, the role of judgment becomes even more important. AI augments decision-making, but it doesn’t replace the need for context, ethics, and human insight. The goal is not to automate HR, but to make it more precise, proactive, and impactful.
Looking ahead three to five years, what will differentiate organisations that truly become AI-native from those that simply layer AI onto existing structures?
The biggest difference will be whether AI is treated as a tool or as a foundational capability. Organisations that simply layer AI onto existing structures will see incremental gains—but they’ll be constrained by legacy processes, siloed data, and traditional ways of working. In contrast, AI-native organisations will redesign themselves around learning, adaptability, and scale.They will have deeply integrated data ecosystems, continuous feedback loops between humans and machines, and talent models built around skills and adaptability rather than fixed roles. Decision-making will be faster, more distributed, and more evidence-based.
Perhaps most importantly, AI-native organisations will cultivate a mindset shift. They won’t just ask, “How do we use AI?” but “How do we become better at learning with AI over time?” That ability to continuously evolve, technologically and organisationally, will be the true differentiator.