Ten years ago, QuantumBlack (QB) joined McKinsey as a 45-person start-up in London’s Shoreditch. “At the time of the acquisition, we had just 11 data scientists and a few data engineers,” recalls Alex Singla, senior partner and global QB coleader. “We were just starting to figure out what it means to deliver machine-learning projects.”
What began as a small team experimenting with advanced analytics has since grown into McKinsey’s center of excellence for AI, gen AI, and agentic AI—an organization that blends data science, engineering, product development, design, talent, and AI strategy at a global scale. Now spanning more than 40 offices and supporting hundreds of client deployments each year, QB has become a key driver of how McKinsey helps organizations capture sustainable value through AI.
Building foundations: The early years
The early years of QuantumBlack were defined by experimentation, craftsmanship, and a culture of relentless curiosity. Teams took on some of the world’s hardest data problems—from optimizing elite athletic performance to transforming clinical trial operations—establishing QB’s reputation for bringing scientific rigor to real-world complexity.
As the team expanded, so did its ambition. QB’s multidisciplinary approach—pairing hands-on engineering with deep industry insight—laid the foundation for the distinctive way it delivers impact today.
“What started as a handful of high-impact projects has evolved into a platform for enterprise transformation across sectors. QuantumBlack’s increasing scale means we’re now shaping how whole industries harness data and AI—not just individual clients,” says Lieven Van der Veken, senior partner and global coleader of QB.
Open-source breakthroughs and the rise of QuantumBlack Labs
As QB grew, it developed frameworks, tools, and repeatable patterns that could scale across teams. This effort led to the creation of Kedro, an open-source machine-learning framework that quickly became the backbone of QB’s data engineering practice. When Kedro was released publicly in 2019, it signaled McKinsey’s commitment to openness, engineering excellence, and community-led innovation.
Kedro was soon joined by CausalNex, an open-source library for cause-and-effect modeling, and Vizro, a framework that helps teams build robust data-experience applications. These tools are now widely used by engineers, researchers, and organizations worldwide.
To accelerate innovation further, QB launched QuantumBlack Labs, an R&D and product hub where more than 200 specialists develop advanced tooling, domain-specific assets, and next-generation AI capabilities. In recent years, Labs has led McKinsey’s work in AI agents—designing domain-specific, autonomous systems that can reason, decide, and act on behalf of users. These agents now support clients in areas such as supply chain optimization, software development, customer service, and financial operations.
The magic of QuantumBlack is the combination of rigorous science and real software craftsmanship
Labs has also driven innovation in agentic infrastructure, including ARK (Agentic Runtime Kubernetes), which provides orchestration for large-scale agent deployments and complex reasoning workflows. We even helped the Emirates Team New Zealand defend its America’s Cup title with an AI-powered sailor bot.
This engineering-first mindset has made QB a vibrant talent engine as well, much in the way McKinsey has acted as a leadership factory. Over the years, QuantumBlack has elevated colleagues who are now shaping the firm and produced alumni who have gone on to found start-ups, lead global data and AI organizations, and hold senior technical roles across industry, academia, and the broader AI ecosystem—reflecting the strength of its culture and the caliber of its people.
Scaling AI globally—and redefining industry impact
QuantumBlack’s growth has been fueled by its ability to combine horizontal innovation—shared platforms, architecture, and engineering standards—with deep industry expertise across life sciences, manufacturing, energy, financial services, and more.
From accelerating biopharma research to enabling predictive maintenance in heavy industry to deploying enterprise-grade gen-AI copilots, QB has become the engine behind many of McKinsey’s most transformative programs. The team’s approach has consistently emphasized not just model performance, but real-world integration, adoption, and measurable business value.
“The magic of QuantumBlack is the combination of rigorous science and real software craftsmanship,” says Tomás Lajous, senior partner and global leader of QuantumBlack Labs. “Our people think like researchers and build like product engineers—so the solutions we create don’t just work in theory; they stand up in complex, real-world environments.”
Looking ahead: Transforming McKinsey itself
The next decade isn’t only about client impact. It’s about reshaping how McKinsey works.
“What I’m looking forward to is a world where QuantumBlack is not only providing a new set of services to our clients, but actually reengineering McKinsey as a whole, and the way McKinsey works and functions,” Tomás says.
This view is shared across the firm.
“QuantumBlack is no longer just a boutique insight team—it’s becoming a global force,” says Alex. “As we scale up, we’re taking sophisticated AI capabilities into entire enterprise systems, delivering impact at an order of magnitude that few teams in our industry can. The first decade of QuantumBlack proved what’s possible. The next will redefine what’s imaginable. We’re just getting started.”