At the first edition of the event AI Founders Night in Amsterdam, Slimmer AI and ai.nl brought together a group of ambitious entrepreneurs and seasoned investors. With talks from AI experts like Daniel Gebler (Picnic) and various startup founders, it was time to get insights into how these promising companies can acquire funding to scale their ideas.
The second round of the event started with a panel discussion with investors. They were asked what their favourite AI start-ups are. Kyang Yung, a partner at INKEF Capital, a venture capital firm that focuses on European technology and healthcare, said that Sentinel is his favourite AI start-up because they worked together for a couple of AI-based deals.
Lisa Brouwer an investor at Curiosity VC, a venture capital firm that focuses on early-stage investments in diverse teams to build the next generation of AI-driven global software companies. Her favourite AI start-up is Deeploy. Ilan Goudsmit, a partner at Endeit Capital, a venture that invests in software, fintech and AI companies, said that (3D) Hubs is his favourite AI start-up. Tamara Obradov is a partner at Tablomonto Venture Capital, a company that invests in early-stage start-up teams as an idea stage. Her favourite AI start-up is Scalelab.
Secondly, the investors were asked what their critical assessments were when looking for AI start-ups. Tamara Obradov from Tablomonto talked about how you must invest in the seed phase. “We see ourselves as early-stage specialists. In the last years, we’ve learned that regardless of the sector, start-ups always have the same issues. It’s around how to build your team, how to create fundamental technology that’s scaleable and how to growth hack, sales, and marketing, and to find the right pricing. These themes are quite similar for a lot of start-ups. We look at the team structure and their ambition, and do we believe that this team have what it takes to reach the next phase?”
Lisa Brouwer from Curiosity VC focuses on early-stage investments. “It’s important to consider problem solutions, especially when you run an AI product. You need to take a good look and ask yourself if you really need AI to solve this type of problem. It happens quite often that start-ups over-engineer a problem. Other than that, you need to understand your customer. Is this solution the right one for this customer? You must ensure the product is the right price for the right customer.”
Last, the investors were asked about their perspective from a growth investment perspective and if they’re looking for some aspects in the team. Kyang Yung from INKEF capital said, “I would say that you must look if this AI-powered company have unique data access. Secondly, it’s about data network effects. Furthermore, you must look at the data engineering part of the AI-powered company. I often get founders telling me that they have the best algorithms, but if they don’t understand the whole cycle from engineering to the customer, then your data pipeline is unfinished.”
Ilan Goudsmit, a partner at Endeit capital, agrees with Kyang. “It’s very important if the team has an effective AI strategy to build better models over time. The margins will increase thanks to this because they will price better and better over time. Furthermore, it’s important to show how you scale a research and development team, especially a machine learning and data science team. How are they inspired and taught by the mentors within the team during the growth stage?”
A start-up doesn’t have a lot of data and AI available from an early stage, so the investors were asked how they thought of a long-term strategy. Tamara Obradov said, “We start looking at what issue we will solve and try to explain this in an easy language. If AI fails, is it still possible for the start-up to have a business? The second thing is that some companies don’t have AI, but they have a lot of available data. With this data, they can build a good data strategy and maybe even a good AI. From this point, you can build a roadmap and develop a long-term strategy.
Lastly, the investors were asked about the biggest challenges for an AI start-up. All the investors agreed that hiring is the biggest challenge of them all. “Hiring senior people in this industry is a huge challenge in the European market. For us to access it well, we have our expert network, but we hired two managers who have expertise in data science. With them, we’ll make less expensive mistakes.”
In the end, the investors were asked to give a tip to the audience. They said the following:
- Make sure you have a clear understanding of what AI can and can’t do. There is a lot of hype around AI, and it’s essential to be realistic about its capabilities.
- Focus on the business problem you’re trying to solve. AI can be used to solve a variety of problems, but it’s essential to focus on the one that is most relevant to your business.
- Build a strong team of AI experts. This includes data scientists, engineers, and business experts who can help you navigate the AI landscape.
- Collect high-quality data. This is a critical ingredient for training AI models and ensuring they perform well.
- Start small, experiment, and learn. AI is a rapidly evolving field, and it’s essential to keep up with the latest advancements. Try out new techniques and learn from your successes and failures.