Operations Calling is Tulip’s flagship event for the global manufacturing community — a space where leaders, engineers, and innovators come together to exchange ideas and see the future of operations unfold. The 2025 edition gathered more than 750 attendees from across industries for two days of keynotes, workshops, and live demos exploring how AI, composability, and human ingenuity are defining a new era of connected, intelligent operations.
One of the most forward-looking sessions, The Next Shift: AI-Driven Transformation of the Connected Factory, brought together three leaders shaping the industrial technology landscape: Natan Linder, Co-founder and CEO of Tulip; Tom Bianculli, Chief Technology Officer at Zebra Technologies; and Alexandra François-Saint-Cyr, Business Development Executive for Industrials at Amazon Web Services.
Their discussion explored what it takes to connect people, systems, and intelligence across every layer of manufacturing — from the edge to the cloud. Together, they examined how data context, interoperability, and human-in-the-loop design are reshaping the way factories think, learn, and improve.
The session also reflected the growing partnership between Tulip and Zebra Technologies, an initiative that combines Zebra’s edge intelligence with Tulip’s composable platform to help manufacturers bridge physical and digital operations. Through the Connected Factory Framework, the two companies are helping enterprises accelerate digital transformation by unifying contextual data, flexible infrastructure, and real-time visibility.
That collaboration formed the backdrop for a broader conversation about transformation — not as a technology upgrade, but as a new model for how manufacturing evolves. Across five themes, the discussion revealed what it truly means to build an AI-driven, connected factory in practice.
Connectivity has evolved from linking machines to linking intelligence. A connected factory is no longer a set of integrated systems but a living network where data, people, and AI collaborate in real time. Zebra’s edge technologies, AWS’s cloud infrastructure, and Tulip’s composable platform illustrate how each layer contributes to a continuous feedback loop between production and insight.
The value of connection lies in context — ensuring that every sensor reading, operator action, or production event has meaning. When data travels freely but carries context, it enables smarter automation and faster learning. The connected factory becomes less about connectivity itself and more about coherence: everything working together toward operational understanding.
This holistic view represents the next step in manufacturing intelligence — one where visibility and adaptability merge into a single, dynamic system of work.
Across manufacturing, enthusiasm for AI outpaces readiness. Many organizations have pilot projects but few have achieved production-scale results. The challenge isn’t capability; it’s culture. Teams need to learn how to trust AI as part of their daily work, and that trust starts with clarity around governance, transparency, and shared responsibility.
AI adoption succeeds when it grows from the ground up. Factories that treat AI as a collaborative tool for operators and engineers — not as an external system imposed from above — achieve faster, safer outcomes. The leap from experimentation to execution is as much about confidence and understanding as it is about algorithms.
The organizations making progress are those building new forms of literacy around AI: how to evaluate it, challenge it, and apply it meaningfully. Technology maturity matters, but cultural readiness determines how far it can go.
Manufacturers have spent years chasing more data, but the real advantage comes from better data. Raw information has limited value until it’s framed by operational meaning — machine state, quality outcomes, environmental conditions, and human context.
Context transforms data into intelligence. Edge systems capture what’s happening, cloud infrastructure interprets why, and composable applications translate that understanding into action. A reading on a temperature sensor becomes powerful only when tied to process conditions and timing; otherwise, it’s just noise.
AI thrives on this structured context. When systems understand not just what occurred but why, they can make smarter recommendations and anticipate change. The focus is shifting from data accumulation to data coherence — from collecting everything to connecting what matters.
Modern manufacturing is inherently hybrid. Real-time responsiveness requires local processing, while strategic visibility depends on the cloud. Balancing those layers determines how intelligence scales across an enterprise.
Hybrid architectures make it possible to decide dynamically where computation happens — at the machine, across a line, or within global analytics systems. Each choice depends on latency, security, and the sensitivity of the task. The goal isn’t centralization, but harmony between systems: data processed where it’s most efficient, insights shared wherever they add the most value.
Composability provides the foundation for that balance. By designing systems that can compose, orchestrate, and evolve, manufacturers ensure that AI grows responsibly. “Composing” creates modular workflows, “orchestrating” governs and coordinates them securely, and “evolving” keeps them aligned as context changes. Together, these principles describe a cycle of continuous learning — the mechanism through which AI becomes part of the operational fabric.
Generic AI models rarely grasp the nuance of production environments. Manufacturing requires domain-specific intelligence — systems trained on real operational data, designed to reason within process constraints. These models understand variability, context, and the consequences of decisions in ways general-purpose AI cannot.
Equally important, AI must remain human-centered. The goal isn’t full autonomy; it’s augmentation. Assistive tools that summarize data, guide troubleshooting, or automate documentation give operators more bandwidth for creative and analytical work. They build trust by demonstrating that AI can simplify complexity rather than obscure it.
Industrial AI is most powerful when it mirrors how manufacturing teams operate: collaboratively, incrementally, and with purpose. The outcome isn’t a hands-off factory but a smarter partnership between people and intelligent systems.
The Next Shift
The Next Shift captured a turning point in manufacturing’s evolution. Connectivity, composability, and AI are no longer abstract concepts — they are becoming the shared language of modern production. The factories leading this transformation are defined not by what technologies they deploy, but by how they connect systems, empower people, and create continuous feedback between the two.
This shift is both architectural and cultural. It depends on open collaboration between infrastructure providers, technology platforms, and the manufacturers who bring these systems to life. As the partnership between Tulip, Zebra, and AWS demonstrates, the connected factory of the future is already being built — one context-rich decision at a time.
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