The transition to proactive, agentic AI is rapidly reshaping enterprise operations, with Gartner predicting that by 2028, 15% of work decisions will be autonomously made by such AI. Unlike traditional AI, these systems can set goals and execute complex tasks with minimal human intervention, relying heavily on high-quality, real-time data. Successful implementations, like Genentech’s automated drug discovery, highlight the need for organizations to evolve their data strategies. Critical shifts include moving from static to dynamic data systems for real-time decision-making, breaking down data silos for orchestrated access across departments, and transitioning to proactive management that anticipates challenges. Emphasizing seamless integration of data and strong governance frameworks will enable businesses to fully leverage agentic AI’s potential, ensuring enhanced efficiency and security. As industries adopt these technologies, companies must rethink their data architectures to stay competitive in an increasingly AI-driven market.
This post was created by AWS with Insider Studios.
Source link
