Designing for machines: A McKinsey partner on the future of AI-driven product experiences
I first fell in love with technology and innovation while pursuing my graduate studies. What began as a course in entrepreneurship and emerging technologies ultimately evolved into a PhD focused on organizational behavior, technology management, and AI-powered business models. My curiosity around how innovation moves from idea to impact has defined every step of my career since.
After college, I joined a big tech player based in Ireland. It was my first real immersion inside a global tech leader, and it confirmed what I already suspected: I wanted to work where technology was being built, scaled, and optimized for digital experiences. I loved my time in Ireland, but eventually I realized I wanted to expand my tool kit beyond a single organization and gain exposure to a broader set of leading consumer technology players. I was eager to widen my “n” from one to many and to accelerate that learning curve. That ambition led me to my PhD, ultimately to McKinsey—and to the Bay Area.
Rethinking how we build with AI
When I joined McKinsey, I worked across sectors and different geographies, but my center of gravity was always technology. In Europe, for example, that meant deep exposure to enterprise-scale platforms, which gave me a strong foundation in how complex systems, data, and organizations really work at scale. Today, my work lives at the intersection of technology, AI, and consumer experiences.
I do two things, primarily.
First, I work with leading tech and marketplace companies on product and growth topics—the kinds of organizations where digital experiences are the business. These are highly consumer-facing environments, where product velocity, hyperpersonalization, and data-driven decision-making are critical.
Second, I partner with more traditional consumer and retail organizations—brick-and-mortar players that are serious about becoming AI-forward. In these cases, my primary counterparts are CTOs and CDOs with an innovation-first mandate: leaders who aren’t just modernizing systems, but rethinking how technology creates competitive advantage—for example through new tech-driven business models. These leaders are asking fundamental questions:
- How (fast) can we create and ship differentiated digital experiences now?
- What does this mean for our talent and team structure, operating model, and ways of working?
- Who do we actually need to hire in an AI-native world?
One of the most consequential shifts I see right now is how AI is transforming the software product development life cycle.
Traditionally, digital product teams were built around specialized roles—product managers, front-end engineers, back-end engineers, designers, QA. With AI, that entire model is being reconfigured. This isn’t just about code generation—though leading tech companies are already saying 30 percent, sometimes more than 50 percent, of their code is machine generated. The real change happens before and after that step.
In the most advanced organizations, long product requirements documents (PRDs) are disappearing. Instead of writing exhaustive PRDs, product managers can move directly to prototypes. AI enables rapid mockups, fast iteration, and real-time testing—often without waiting on a full design or engineering cycle. The result is dramatically faster learning and delivery.
This changes team structures, too. We’re seeing the rise of the “product builder”—a more empowered role that orchestrates specialized AI agents across design, engineering, testing, and deployment. That allows teams to move faster—and deliver exponentially more output.
Engineering a future for human and AI shoppers
One of the latest examples of what these AI-native product models can build is agentic commerce solutions.
Until recently, digital commerce experiences were built for humans only—apps, websites, interfaces optimized for human behavior and conversion. That’s changing. While consumers can already research, compare, and purchase products directly through large language models (LLM), transactions are now also increasingly initiated, evaluated, and executed by AI agents.
In this new environment, retailers, marketplaces, brands, and payment providers aren’t simply optimizing for human shoppers. They’re optimizing for agents that crawl, compare, negotiate, and purpose on behalf of users.
That means marketplaces face disintermediation risk, search becomes more horizontal with your LLM of choice functioning as your portal into the shopping world, and new winners will emerge based on how well they integrate into agent-driven journeys.
For some companies, this shift is a growth opportunity. For others, it’s existential.
Getting to work alongside leaders as they figure out what this means for their business—often in real time—is one of the parts of the job I love most. It’s a privilege to help clients write the playbook in this moment, and it also connects directly to the research I’ve been doing at the firm on frontier technologies and AI adoption in the workplace.
Whether it’s agentic commerce, AI-native product teams, or the ways intelligent systems reshape how we discover, decide, and interact every day—from what we buy to what shows up in our feeds—I get to partner with my clients at the forefront of that change. That breadth of exposure, across the biggest innovators and the boldest questions, is what makes the work both uniquely energizing and deeply consequential.