How Moderna Is Using AI To Take On Cancer #Moderna is using #AI to advance personalized #cancer #treatments and transform how its business operates. Its ambitious #strategy offers lessons for leaders looking to accelerate #innovation and achieve more with smaller teams. forbes.com/sites/bernardmarr…

🤖 Goldman Sachs is embracing AI agents. Could this change the future of banking? Artificial intelligence is moving beyond chatbots and simple productivity tools. We're entering an era where AI agents can take on increasingly complex tasks and support entire business processes. 🚀 And when major financial institutions such as Goldman Sachs explore this technology, it's a sign of just how significant this transformation could become. From analysing financial information and supporting research to streamlining operations and automating repetitive workflows, AI agents have the potential to fundamentally change how financial services operate. 📊 But this isn't just about replacing manual tasks. It's about rethinking how work gets done, how employees collaborate with intelligent systems and where human expertise delivers the greatest value. Of course, in an industry built on trust, security and accountability, human oversight remains essential. The big question is no longer whether AI agents will enter the workplace, but how organisations will manage them effectively. 💬 Would you trust an AI agent to handle important financial tasks? 👇 #AI #GoldmanSachs #AIAgents #AgenticAI #ArtificialIntelligence #FutureOfBanking #FinancialServices #FinTech #FutureOfWork #AITransformation #BusinessAI #Automation

A wrong answer from a chatbot is annoying. A wrong decision from an autonomous AI agent could trigger thousands of actions before anyone spots the error. 🤖 #Sponsored And as businesses increasingly rely on AI agents to make decisions, execute tasks and work with other agents, this becomes a serious concern. I recently had a fascinating conversation with Stijn (Stan) Christiaens, Co-founder and Chief Data Citizen at Collibra, about what it really takes to make autonomous AI work reliably at scale. One topic we explored was the hidden "hallucination tax", the financial and operational costs businesses incur when AI systems make mistakes or act on unreliable information. And there's a real challenge here. 📊 Collibra's research found that 76% of surveyed data and AI decision-makers encountered critical roadblocks when moving AI agents from pilots into production. Two critical issues are emerging: 🔹 Context: Do AI agents have access to the right business data, knowledge and policies to make informed decisions? 🔹 Control: Do organizations know what their agents are doing, what information they're accessing and which actions they're allowed to take? I also came across a fascinating example from Helvetia Baloise, which is using an air traffic control approach to oversee its growing portfolio of AI initiatives, supported by Collibra. But I believe there's an even bigger opportunity here. Most organizations are still focused on adding AI agents to existing business processes. What happens when we fundamentally rethink those processes around the capabilities of autonomous AI? That's where I believe some of the most significant business opportunities will emerge. However, achieving this requires trustworthy data, effective governance and a willingness to rethink how organizations operate. In this article, I explore the challenges of scaling autonomous AI, what we can learn from businesses already tackling them, and what leaders should be doing differently. linkedin.com/pulse/ai-agent-… 💬 Here's a question for business leaders: If your organization deployed 100 autonomous AI agents tomorrow, would you know exactly what each one was doing and what decisions it was making? For more information about Collibra and to request a demo, visit their website. Paid partnership with Collibra