Bosch Connected Industry

Successful AI pilots in manufacturing are everywhere. But what comes next? How do you roll out a promising solution across the entire business? Suddenly, the conversation isn't just about the model anymore. It's about integrating AI into complex IT and OT environments. It's about ensuring security, managing governance, and supporting users across multiple plants. It's about adding new use cases without rebuilding the foundation every single time. A successful pilot proves that AI can work. It doesn't prove it can scale. This is the moment many organizations realize they are no longer building a simple application, they are building an entire AI platform. The hidden costs of AI don't show up in the pilot. They appear when you try to scale your second, third, and fourth use case. 👉 Read our blog to explore the hidden economics of AI in manufacturing: https://lnkd.in/gxdYv9DM What has been your biggest challenge when moving from AI pilots to AI at scale? #ManufacturingCoIntelligence #AgenticAI #AIAgents #AIatScale

More value, lower cost, and faster time to value. It sounds counterintuitive, but buying a ready-made AI platform is often more economical than building one from scratch. While creating an AI agent has never been easier, a simple prototype doesn't run a factory. Running AI in production demands robust security, governance, monitoring, and seamless integrations. These are the operational costs that are easy to underestimate because they only become visible after deployment. For a typical manufacturing plant, our business case shows that a purpose-built AI platform delivers higher value at a significantly lower annual cost than an internally developed solution. And there's another advantage that deserves more attention: time. A specialized platform can be deployed in about three months, while building an enterprise-ready equivalent often takes a year or more. That's nine months of additional production improvements, nine months of operational learning, and nine months of scaling successful AI use cases while others are still building the foundation. The "Build vs. Buy" debate is about far more than development costs. It's about how quickly your AI starts delivering measurable business value. 👉 Learn more in our blog article: https://lnkd.in/gxdYv9DM #ManufacturingCoIntelligence #AgenticAI #AIAgents #AIatScale

Every successful AI project creates demand for more: another production line, another plant, another use case. This is exactly what manufacturers want. What shouldn't grow at the same pace is the engineering effort required to support it. If every new rollout demands new integrations, new infrastructure, and more people just to keep the platform running, scaling quickly becomes unsustainable. Manufacturers don't create a competitive advantage by operating AI platforms; they create it by improving production. By reducing downtime, increasing quality, and helping operators make better decisions. That’s why the "Build vs. Buy" discussion isn't ultimately about software. It’s about deciding where your engineering talent creates the greatest value. Building AI infrastructure? Or building better manufacturing operations? The companies that win with Industrial AI won't just be the ones that build the most applications. They'll be the ones that scale them fastest, all while keeping their engineering teams focused on what truly matters. 👉 Get the full perspective on our blog: https://lnkd.in/gxdYv9DM Where do you believe engineering teams create the most value today? #ManufacturingCoIntelligence #AgenticAI #AIAgents #AIatScale

Building an AI agent is easy. Turning it into business value is the hard part. Too many AI initiatives never make it beyond the pilot stage. According to the MIT NANDA study, around 95% of AI initiatives still fail to deliver measurable business outcomes. The reason isn't the AI itself. Too many projects start with the technology instead of the business problem. They solve a single task but never improve a measurable KPI or become part of an end-to-end production process. And that's where the real challenge begins. Industrial data is often spread across countless systems. Without a shared understanding of that data, every new AI use case requires new integration work. The result? Promising pilots that never make it beyond a single production line. Scaling Agentic AI requires a different approach. Agentic AI isn't about replacing people on the shopfloor. It's about empowering operators, maintenance teams and quality experts with the right information at the right moment, enabling better decisions and faster action. That's the idea behind Manufacturing Co-Intelligence®. By combining people, machines and Agentic AI on a shared semantic foundation, AI can understand industrial context instead of isolated data points. Rather than creating isolated AI use cases, we focus on recurring Performance Domains such as maintenance, quality and production performance. By solving recurring operational challenges instead of one-off problems, AI becomes reusable, repeatable and scalable across manufacturing networks, creating measurable business value far beyond a successful pilot. Real improvement doesn't happen at a single point. It scales from the shopfloor to the value chain. 👉 Discover why most Agentic AI projects stall and what it really takes to scale Industrial AI: https://lnkd.in/giKeb9zh Where is your organization today? Are you still experimenting with Agentic AI, or are you already scaling it across your operations? #ManufacturingCoIntelligence #AgenticAI #IndustrialAI #AIatScale #SemanticDataManagement #AIAgents

Everybody is talking about the Battery Passport. And rightly so. But if you're thinking about scaling, you need to look one step further. The Battery Passport won't be the last product passport. More regulations will follow, and most manufacturers won't have to manage just one compliance requirement. They'll have to manage many of them across different products and markets. The question is no longer how to build a Battery Passport. The question is how to scale product compliance. Here's where many companies get it wrong. Every new regulation becomes: • a new data model • a new data structure • a new integration • another implementation project The result? More complexity. More manual work. More bureaucracy. Less scalability. A better approach is to build one semantic product data foundation and reuse it across product passports and future compliance requirements. That doesn't just accelerate future rollouts. It creates a scalable foundation for everything that comes next. Built on the Bosch Semantic Stack, this foundation goes far beyond today's regulations. Once it's in place, it enables much more than compliance: 🔋 Battery health monitoring ♻️ Second-life applications 📊 Product lifecycle transparency 🤖 AI-powered services Scaling isn't about managing one regulation after another. It's about building one product data foundation that supports all of them. Because the Battery Passport is only the beginning. 👉 Learn how Bosch helps manufacturers build a scalable product compliance foundation: https://lnkd.in/g_4r_zVd #BatteryPassport #Scaling #DigitalProductPassport #SemanticDataManagement #DigitalTwins #ManufacturingCoIntelligence

Join our CEO, Norbert Jung, and Deb Cupp from Microsoft for a compelling fireside chat. They will explore how Industrial AI is revolutionizing manufacturing and shaping the future of the industry. Don't miss the session: “Microsoft C-Suite Day: A fireside chat between Deb Cupp and Norbert Jung” 🗓️ Date: April 20, 2026 ⏰ Time: 10:30 - 10:45 AM (CEST) 📍 Location: HANNOVER MESSE, hall 17, booth G06 https://lnkd.in/dfZ4_RZH We look forward to seeing you there for this insightful discussion! #HM26 #IndustrialAI #Microsoft #ManufacturingCoIntelligence #AgenticAI