Modern MES vs. Legacy MES: Why Tulip Leads in Embedded AI, IIoT, and Low-Code Manufacturing

AI is no longer confined to analytics teams or post-process dashboards. Over the past year, AI has begun moving directly onto the shop floor, supporting processes like real-time quality inspections, adaptive work instructions, and machine-aware workflows, often in environments defined by high mix, manual labor, and constant change.

As this shift accelerates, it is exposing the limitations of systems designed primarily for static processes and after-the-fact reporting. To support AI operating in real-time, alongside operators and equipment, the systems managing production must be able to adapt just as quickly as the work itself. This is fundamentally changing what manufacturers expect from their Manufacturing Execution Systems (MES).

While AI, Industrial IoT (IIoT), and low-code development are no longer emerging technologies, not all MES solutions are equipped to support them in a meaningful way. The difference between legacy MES and modern, AI-native platforms is architectural.

Platforms like Tulip exemplify this next generation of MES. Rather than retrofitting AI and IIoT into rigid, decades-old systems, Tulip was built from the ground up as a low-code, composable MES designed to embed intelligence directly into shop floor workflows.

This architectural foundation is what enables AI, computer vision, and real-time machine data to operate as native elements of production – not external tools bolted on after the fact.

Many manufacturers still rely on traditional MES solutions originally designed for stable, linear production environments. Over time, vendors have attempted to modernize these platforms by adding IIoT connectors, analytics modules, or AI toolkits. However, these capabilities are often layered on top of architectures that were never designed to support them.

This "frankensteined" approach results in complexity without agility. Deployments frequently stretch over months or years, requiring extensive customization, specialized system integrators, and costly long-term maintenance. Even small changes, such as modifying a workflow, adding a data capture point, or integrating a new device, can become major IT projects.

This rigidity directly limits the ability to operationalize AI on the shop floor. Legacy MES vendors struggle to deliver real-time contextual data, integrate seamlessly with modern devices, or adapt workflows dynamically based on AI insights. Manufacturers often describe feeling constrained by systems that dictate how work must be done, rather than supporting how work should evolve.

Tulip takes a fundamentally different approach. Instead of assuming that complexity and customization are unavoidable, Tulip provides a low-code composable platform built around agile shop floor applications. These apps run directly where work happens, allowing manufacturing teams to design, deploy, and iterate workflows without custom code or external development cycles.

For operations leaders, this means digital transformation can move at the speed of the business. New workflows can be piloted quickly, refined based on real-world feedback, and scaled across sites using a repeatable architecture. Time-to-value is dramatically reduced, and total cost of ownership declines as reliance on custom integrations disappears.

For IT, OT, and engineering teams, Tulip offers an open, flexible environment that works alongside existing systems. Integrations with ERP, PLM, QMS, and data platforms are straightforward through connectors. Workflows can evolve continuously as processes change. Most importantly, teams can embed AI and IIoT capabilities directly into production logic without waiting on vendor roadmaps or development backlogs.

What truly differentiates Tulip from both legacy and “modernized” MES platforms is how deeply AI and IIoT are embedded into its core architecture. In Tulip, these capabilities are not external services or ad-hoc extensions. They are designed to operate directly within shop floor applications, alongside operators, machines, and processes.

This distinction is critical. AI delivers value in manufacturing only when it is tightly integrated with real production workflows and Tulip is built specifically to make that possible.

Embedded AI Vision Designed for Frontline Workflows

Tulip’s Vision capabilities are natively integrated into shop floor applications, allowing manufacturers to deploy computer vision directly at the point of work. Vision checks can be configured without specialized machine learning expertise, proprietary hardware, or complex deployment pipelines. Cameras connect directly into Tulip apps, where vision results become first-class data, just like operator inputs or machine signals.

This enables manufacturers to perform real-time inspections such as assembly verification, presence or absence checks, defect detection, and tool usage validation within manual and semi-automated processes. Because AI Vision is embedded in the workflow itself, results can immediately trigger actions: guiding operators, blocking progression, logging quality events, or escalating issues in real-time.

Unlike traditional MES or standalone vision systems, Tulip’s approach supports high-mix, variable production environments where human work is central. AI becomes an always-on quality layer that operates naturally within production.

LLM-Powered Application Building and In-Workflow Guidance

Tulip extends embedded intelligence beyond inspection through native large language model (LLM) capabilities that support both application creation and frontline execution.

With AI-powered tools such as Tulip AI and AI Composer, engineers can generate shop floor applications directly from standard operating procedures, work instructions, or plain-language prompts. This dramatically reduces the time required to digitize processes and ensures that frontline workflows reflect the latest operational knowledge.

On the shop floor, LLMs enable contextual, adaptive guidance within applications themselves. Instead of static SOPs or external documentation systems, operators receive step-by-step instructions, explanations, and troubleshooting support tailored to the task, product, and current conditions. Knowledge is delivered exactly when and where it is needed, improving consistency, reducing errors, and accelerating training.

By embedding LLMs directly into production workflows, Tulip transforms AI into a collaborative tool that supports operators and engineers in real-time rather than existing as a reporting or analytics layer.

Native IIoT Connectivity Through Edge Drivers and Open Integrations

Tulip’s IIoT architecture is purpose-built to connect machines, devices, and systems directly into shop floor applications with minimal friction. Through native connectors, open APIs, and Edge Drivers, manufacturers can integrate PLCs, sensors, tools, vision systems, and other equipment without custom middleware or complex data pipelines.

Edge Drivers allow Tulip to communicate directly with industrial devices at the edge, bringing real-time machine data into workflows with low latency and high reliability. This data can be linked to operators, products, steps, and quality events, rather than stored in disconnected historians or raw tag structures, to contextualize it.

Because Tulip’s IIoT data model is human-readable and application-centric, both engineers and AI systems can work with the same information seamlessly. Machine signals can trigger workflow logic, inform AI models, populate dashboards, or initiate automated responses, all within a single platform.

A Unified, AI-Ready Operational Data Layer

Together, Tulip’s embedded AI and native IIoT capabilities create a unified operational data layer that bridges human and machine activity. Data flows directly into production applications, where it can drive decisions, automate quality checks, and enable real-time continuous improvement.

This is a fundamentally different approach from traditional MES architectures, where AI and IIoT data often live in parallel systems with limited operational impact. With Tulip, intelligence is embedded where work happens to make AI practical, scalable, and actionable on the shop floor.

The difference between legacy MES and Tulip is not a matter of features. It is the foundational architecture upon which these systems are built. Traditional MES are monolithic, rigid, and difficult to evolve. Even when AI and IIoT capabilities are added, they remain disconnected from actual workflows, limiting their impact on day-to-day operations.

Tulip’s composable platform is built on the opposite foundation. AI Vision, LLM-powered guidance, and native IIoT connectivity are embedded directly into shop floor applications, where they can act in real-time and in context. Modular, low-code building blocks allow frontline teams to design, adapt, and scale solutions without long deployment cycles or heavy customization.

Manufacturers adopting Tulip are already seeing measurable results: higher throughput, improved quality, faster training, and greater visibility across operations. These outcomes underscore the critical truth that successful digital transformation does not require more complexity or more code. It requires a platform designed to embed intelligence where work actually happens.

As manufacturing continues to evolve, the choice of MES will define how effectively organizations can operationalize AI on the shop floor. Tulip represents the next generation of MES: embedded, intelligent, and built for continuous improvement. The future of MES is already here. If you're ready to explore our AI capabilities, reach out to a member of our team today!