ARTIFICIAL INTELLIGENCE
our links to organisations with expert insights. These include the Competition and Markets Authority, the Financial Conduct Authority, Ofcom and the European Commission, alongside other international bodies. Bringing real-world context in early, through case material, guest teaching and the option of an internship or research project, gives students from both sides of the cohort a shared, concrete problem to orient around. It also helps dissolve the “two tracks” dynamic faster than abstract curriculum design alone. Closing the gap If you’re a business school or university thinking about building something similar, my advice is not to underestimate how much genuine, sustained collaboration is required at the design stage. In addition, don’t assume a mixed cohort will integrate itself just because the curriculum is integrated on paper. While these two aspects both take deliberate structural work, they are both worth it. AI is reshaping industries faster than the frameworks meant to govern it are keeping pace and that gap isn’t going to close through technical expertise or policy expertise alone. It also isn’t going to be solved by companies simply hiring more technologists. Organisations need people who can understand the technology and at the same time, what it means for their business, markets, employees and society more broadly. Imperial is fortunate to have strengths in computing, economics and business within the university, while also being able to draw on leading external expertise in technology law. However, proximity alone doesn’t produce integration, it just makes it possible. Making a possibility a reality revolves around the harder, slower work of building a shared curriculum and shared assessment thinking, as well as a cohort experience where students from very different starting points learn to speak each other’s language by the time they graduate.
expertise in both AI systems and economic policy – very few people at this career stage do. Instead, we’re looking for candidates who show clear evidence that they can move comfortably between technical and analytical ways of thinking and who are motivated by the programme’s interdisciplinary premise rather than treating it as a specialism. That means our assessment of applications looks at how someone writes about a technical problem, how they reason through a policy question and whether they show curiosity about the side of the field that isn’t their academic background. A strong computing graduate who has never touched economics and a strong economics graduate with little coding background can both be excellent fits – provided they’re coming in ready to build the half of the picture they don’t yet have. This brings me to the question I think matters most for other institutions considering similar programmes: “How do you stop a mixed cohort from splitting into two groups by week three, with technical students disengaging from the economics content and vice versa?” We’ve built the programme with this risk in mind, introducing structured foundational content early on. This is designed to get students with insufficient technical backgrounds up to speed on the AI systems side and students without an economics or policy background up to speed on the markets and wider economic and policy context side. Crucially, this happens before the two strands need to work together on the same assessed material. The aim isn’t to turn every economist into a computer scientist or every engineer into an economist. It’s to make sure every student has enough fluency in both languages that they can engage properly with the integrated teaching that follows and can hold their own, both in group work and discussions alongside peers from a different background. On this last point, it’s worth noting that we’ve leaned heavily on peer learning by design. Small-group and project-based work is structured to mix students across academic backgrounds, so that a technically strong student and a policy-strong student are working the same problem from different angles. In our experience, that structured peer exchange does as much to close skills gaps as any formal teaching does. Moreover, students learning to explain their own strengths to each other sits at the very heart of the core skillset the programme is trying to develop. Engagement with external partners plays a role here too, with the programme closely informed by
IMPERIAL BUSINESS SCHOOL
Tommaso Valletti is a professor of economics at Imperial Business School, as well as the academic director of the newly launched Master’s in AI, Economics & Policy. Valletti is also director of the Research & Policy Network on Competition Policy at the Centre for Economic Policy Research (CEPR)
Business Impact • ISSUE 5 • 2026
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