AI and Sustainability: Insights from Futurism Speaker Birju Shah

When you think about the future of AI, most headlines point to generative text, image models, or the fear of automation. But for Birju Shah, the story is bigger; it’s about food on our tables, the sustainability of our planet, and the practical tools leaders can use today to prepare for tomorrow.

A clinical assistant professor at Northwestern University’s Kellogg School of Management, Birju Shah is both an accomplished speaker on artificial intelligence and a leading voice in product innovation.

Also, recognised as a futurism speaker, Birju guides leaders through the evolving landscape of AI, product strategy, and digital responsibility.

With over 15 years of experience leading AI and ML platforms, he’s taken AI from the boardrooms of Silicon Valley to the farmlands that grow our food.

As Uber’s former Head of AI, he led the development of products used by billions worldwide. Later, with his venture Loam, he turned to agriculture, leveraging AI to enhance global food systems.

In this conversation, Shah speaks candidly about how AI is reshaping industries, from food production to sustainability, and what businesses and individuals can learn from scaling technologies globally.

Professor Shah, let’s start with one of the most human challenges of all, food. Everyone’s talking about how AI is changing business and creativity, but how do you actually see AI transforming the future of food production?

Professor Birju Shah: AI will be one of the most significant opportunities for value creation we have, particularly in automating research and development (R&D).

Traditionally, R&D occurs at a chemical or biological level, inventing new ways to produce food, develop drugs, or enhance farming processes. Research labs worldwide often operate in silos and rarely share data, resulting in progress that can take years.

AI is breaking down those walls. By utilizing decades of data, it can simulate a wider range of compounds, seed genetics, and new farming methods. While we’re not yet at the stage of “creative AI”, an Einstein of food production, machine learning already makes testing and predicting far more efficient.

It can evaluate thousands of drug molecules, analyse failed seed genetics, and develop new pathways for personalised nutrition. In the future, AI could enable tailored food recommendations based on an individual’s genetics and metabolism, while also making food production more sustainable to feed a growing global population.

That’s fascinating. You’re linking AI not just to efficiency, but to sustainability. What, in your view, makes the connection between AI and sustainability so powerful?

Professor Shah: AI has the potential to simulate scenarios where humans currently underperform, from how we extract oil to how we manage methane emissions from livestock. It can help invent new methods to reduce environmental impact.

For the first time, even large energy companies like ExxonMobil and Shell are exploring how AI could make their operations more sustainable.

Another exciting area is agriculture. With sensors on tractors and innovative farming tools, data on soil health, biodiversity, and carbon storage can feed into AI systems that automatically assess improvements and even file for carbon credits. This creates an incentive engine for sustainable practices without forcing significant behaviour changes.

Of course, AI itself consumes a lot of energy. However, advancements in chip design, data center efficiency, and responsible resource management are also helping to ensure the technology evolves in a sustainable way.

You also bring a global operator’s perspective, especially from your time scaling Uber. Having taken the company to more than 100 countries, what lessons do you think other businesses, especially in AI, should take from that experience?

Professor Shah: At Uber, our mantra was ‘think global, build local. Transportation is a universal need, but every city has distinct needs, challenges, and socioeconomic realities. We built a platform with global principles but adapted it locally, whether in Chicago, Delhi, or São Paulo.

Scaling followed three phases: first, achieving adoption and reliability; second, optimizing the service for trust, safety, and efficiency; and third, addressing gaps such as different modes of transport or access to healthier food options through Uber Eats.

The same applies to AI. Algorithms can be built for global use, but human intervention is essential to understand local contexts, regulations, and cultural norms. The lesson is to balance scalability with adaptability, and always keep people at the centre.

Let’s shift gears to you as a futurism speaker. When you step on stage, you’re not just theorising; you’ve built and scaled products. What do you hope audiences walk away with after hearing you speak?

Professor Shah: When I speak, I don’t aim to give a lecture. I want audiences to leave with practical steps they can apply immediately, backed by frameworks that also stand the test of time.

My background is as a builder and operator; I’ve launched companies and worked inside global giants. I love experimenting with new technologies, but more importantly, I focus on how to implement them in real-world settings. That’s what I try to share: actionable insights grounded in experience.

Ultimately, I want people to leave thinking, ‘I know exactly what to do next, and I also understand the long-term strategy behind it.’ If they can achieve both the tactical and the strategic after hearing me speak, then I’ve done my job.

Professor Shah, this has been a wide-ranging conversation, from AI in food systems to sustainability, scaling lessons, and what you hope the audience will take away. Before we close, I want to thank you for sharing your time and insights so candidly.

Professor Shah: Thank you. I really enjoyed the conversation. These are big topics, but also very human ones: how we eat, how we live, how we adapt to change. If today’s discussion sparks even a few leaders or young builders to start experimenting and thinking differently about AI, then that’s time well spent.