Prosus Leads AI Implementation with Responsible Approach

Businesses looking to harness the power of AI beyond superficial implementations can take valuable lessons from venture investors who are strategically integrating AI across their portfolios. A prime example is Prosus, a major tech investor with a diverse portfolio spanning fintech, edtech, classified ads, and food delivery, among others. Euro Beinat, Prosus’ Global Head of AI and Data Science, underscores three crucial aspects of their AI philosophy:

Firstly, Prosus emphasizes implementing AI by design, prioritizing a customer-centric approach that thoroughly understands the specific use case before selecting and applying AI technologies. This approach ensures that AI solutions are tailored to address real customer needs rather than being technology-driven initiatives.

Secondly, Prosus places a strong emphasis on responsible and ethical use of AI. By setting common guidelines for ethical AI implementation, Prosus aims to mitigate risks associated with AI projects and avoid recurring debates on ethical issues.

Thirdly, Prosus is committed to adopting AI rapidly wherever it can provide tangible benefits. This proactive approach allows the organization to stay ahead of the curve in leveraging AI technologies to drive innovation and efficiency across its portfolio companies.

To facilitate the adoption of AI across its portfolio, Prosus has implemented a well-structured organizational framework. This includes a small central team based in Amsterdam, which is responsible for overseeing AI initiatives at the corporate level, as well as dedicated data science teams within each portfolio company. These data science teams, ranging from small groups to larger organizations, are supported by the central group in several key areas.

For instance, the central group assists in attracting and vetting talent, researching and evaluating emerging AI technologies, and providing educational resources for continuous learning and upskilling. Moreover, the central group offers a common set of tools and resources for experimentation, enabling teams across the portfolio to explore new AI applications and models effectively.

In addition to learning from within the organization, companies seeking to build AI capabilities can draw inspiration from firms in analogous situations. Henning Trill, VP for Innovation Strategy at Bayer, suggests focusing on inspiring people, learning from common methodologies, connecting innovators, and fostering collaboration to drive innovation and AI adoption across the organization.

Beinat also emphasizes the importance of managing expectations when it comes to AI adoption. While AI models in production can deliver incremental benefits, achieving significant results requires a continuous improvement mindset and systematic adoption of AI technologies. He cautions against the misconception that AI tools can instantly solve complex problems, highlighting the need for patience, perseverance, and a strategic approach to building AI capabilities that deliver meaningful and scalable results.