The recent 2024 edition of Datavore once again highlighted the growing importance of data in technological innovation. But despite the hype surrounding AI, can it really disrupt companies' business and operational models?
The opening conference, focusing on investment in customer data development, was categorical: AI is not revolutionary. It's crucial to distinguish between the notions of revolution (more radical and fundamental) and innovation (more gradual and progressive). Revolution challenges existing norms and paradigms, while innovation introduces something new, incrementally. For example, aviation and the automobile revolutionized transportation, while the Internet revolutionized information transfer.
Certainly, AI is a tool for improving business models by reducing costs through the predictive analysis of vast data sets. It represents a definite innovation and incremental optimization, but not a revolutionary invention. The customer case of collaboration with robots, a long-standing interaction, is a case in point. So, a business strategy should focus more on market needs and productivity than on digital or AI. For example, Facebook connects people to monetize data, Netflix reinvents the video store, and Amazon focuses on online shopping. These 3 flagship companies have been using AI for many years now. They are to the automotive revolution what AI is to its engine.
What about cybersecurity? It has to manage the opportunities associated with digital strategies, while minimizing the threats. AI, while not revolutionary, is a lever for growth and an incredible innovation, which however implies increased data management and security challenges. Paradoxically, data protection also requires AI, an ideal tool for analyzing human behavior and various processes. Clearly, cybersecurity needs AI, which needs cybersecurity. If the automobile embodies a revolution, and AI is its driving force, then cybersecurity is the technical control, monitoring all the components of the whole.
It's crucial for organizations to put in place robust data management strategies and appropriate cybersecurity to protect their AI-based data from potential threats. AI-based data can also be used to proactively detect and respond to threats, automating certain security tasks and enabling faster anomaly detection.
