“Everybody wants to be in this sector now, but when I first started Aigency things were still in their early stage. I could see all of this potential and I couldn’t help but start to question what it meant for business, and what it means for society,” says Stolze. “I decided to quit everything that I did. I fired my clients, I left my work as ambassador for Ted.com here in Europe, and I began figuring out how to match algorithms with businesses.”
Stolze spent a month speaking to corporations and discovered that while most of them were data-rich, they were also information poor. He moved into Amsterdam Startup Village, a ramshackle campus of repurposed shipping containers that’s tucked behind the University of Amsterdam, and began dropping into parties held by researchers at the School of Informatics.
The researchers told him that they could work for years on a particular problem, but still needed more data to improve their results. Stolze realised that what they needed was somebody to bridge the two worlds to help researchers put their algorithms to work. One of Aigency’s biggest challenges was to evaluate how machine learning could be used to streamline the operations of one of the world’s leading beer companies.
“We’re not talking about the sexy side of Heineken — all of the marketing or branding. No, we were involved in procurement, which for a lot of international companies is hell,” says Stolze. “It’s become normal for corporates to have offices all around the world, speaking different languages and using different CRM systems. That means there’s a lot of opportunity to simplify things with the right technology.”
Aigency worked with Heineken to overhaul how invoices are tracked and processed during its immense procurement operations. The firm has also enabled researchers to mine the sensor data from Tesla’s self-driving vehicles and, most recently, has been working with Holland’s largest commercial radio network to better understand the listening habits of their audience. Each project Aigency takes on requires the client to be realistic about the data they have access to and the problem they’re trying to solve.
“There’s so much hype and nonsense saying that AI will solve everything, but it’s not magic, it’s just math. When we talk to clients, we first try to fully understand the problem we’re solving. From there we can look at the available data and start finding the value that can be brought out by machine learning algorithms,” says Stolze. “Until the problem is properly defined, it can be really difficult to have down-to-earth conversations and explain what is and isn’t a problem AI can solve.”
“Aigency acts as a matchmaker between startups, students and the corporates, but the more I talk to students the clearer it is that it’s not enough for them to just work with AI. They want to actually do something meaningful with it. So, we’ve started looking at how we can work with more NGOs,” says Stolze. “AI is too important to leave to the tech companies in Silicon Valley and we believe that NGOs and municipalities also deserve this super power.”