AI and Next-Gen MES Decision-Making: How Manufacturers Are Closing the Insight-to-Action Gap

Manufacturing relies on the vigilance of the people on the floor. Operators and engineers are often the first to spot drift or trace the root cause of an issue. However, relying solely on human observation creates a critical vulnerability. When a team misses the early signs of process drift, from subtle changes in machine performance to operator shortcuts that compound over time, small variances turn into significant losses.

The reality is that some problems are impossible for humans to catch on their own. Lines run faster than ever, and the volume of data generated exceeds what any supervisor can track. Without a system to augment their capabilities, teams are forced to react to failures instead of preventing them.

This dynamic has trapped many manufacturers in a state of DRIP (Data Rich, Information Poor), widening the insight-to-Action gap. Legacy manufacturing execution systems (MES) excel at producing data (for example, logging transactions and tracking compliance). But unlocking insights remains a manual, retroactive challenge. Worse still, quickly taking action on those insights has been historically impossible.

Predictive AI has promised to solve this for decades, but it has largely remained trapped in silos. It could flag a potential bearing failure, but that insight often lived on a disconnected dashboard, adding to the noise rather than resolving it.

What changes the equation today is agentic AI embedded within a Composable MES. We finally have the ability to close the loop, transforming data into insight, and insight into immediate action.

This combination allows operations to solve a new domain of problems, drastically improving speed, accuracy, and autonomy in shop-floor and operational decision-making. Because it is composable, the AI can listen to data across systems, understand the context of the shop floor, and empower teams to act instantly (or even allow the AI to take action autonomously).

This is the world shaping the next generation of MES: not a system that just logs what happened yesterday, but a real-time decision engine that helps people act before small issues turn into losses.

MES took shape in the 1990s when manufacturers needed a bridge between ERP planning and the shop floor. Systems from vendors like GE Digital, Rockwell, Siemens, and Honeywell solved real problems by enforcing workflows and capturing production data without forcing a risky overhaul of the core ERP.

However, because these systems were built as rigid monoliths (designed for strict hierarchical control rather than open connectivity), they struggled to adapt. To fill the gaps, manufacturers added separate point solutions for quality, maintenance, and analytics. This led to systems sprawl, creating a fragmented landscape where vast amounts of data were produced but locked away in disconnected silos.

This architecture is the structural driver of the DRIP paradox. Every new point solution adds more data, but because that data cannot be easily correlated, the operation remains information poor. This forces humans to bridge the gap manually, leading teams to spend hours aggregating reports and interpreting conflicting signals instead of focusing on high-value problem solving.

Today’s factory floor is defined by volatility. Production schedules are no longer static plans but fluid targets that shift hour by hour. Material availability fluctuates, forcing teams to reconfigure lines on the fly. Product variants continue to multiply, adding layers of complexity to every shift.

The workforce dynamic presents its own set of immediate challenges. High turnover rates mean factories can no longer rely on deep tribal knowledge to bridge process gaps. As Deloitte notes, the new generation of workers expects intuitive, responsive tools similar to the consumer technology they use in their daily lives.

Meanwhile, the velocity of data has outpaced the ability to process it. Machines and sensors generate a constant stream of signals, but without the right architecture, this data creates noise rather than clarity. In this environment, the primary challenge is not just execution. It’s adaptability.

The structural fragmentation of legacy systems combined with the high velocity of modern operations creates a paralyzing problem: latency.

While the data exists, the signal-to-noise ratio is unmanageable for humans alone. Critical insights are trapped in passive dashboards that wait for someone to interpret them. To understand a sudden throughput drop, an engineer often has to check the MES for downtime codes, the QMS for recent defect logs, and the ERP for material batch details. They are forced to act as human middleware, bouncing between screens and manually correlating timestamps to piece together a story.

This manual synthesis is where the insight-to-action gap widens. The time spent hunting for context across these silos is time not spent fixing the problem. By the moment the root cause is identified, the shift is often over, or the scrap pile has already grown.

Agentic AI changes this dynamic fundamentally. Instead of expecting teams to hunt for answers, it continuously listens across systems, identifies the signals that matter, and surfaces the right insight at the right moment.

Next-generation MES platforms don’t just support operations; they fundamentally drive them. By embedding Agentic AI directly into the workflow, the system connects disparate data sources and listens to them continuously. It identifies the signal in the noise before a problem occurs, surfacing priority actions to the right user at the right time.

This shifts the role of the MES from a passive system of record to an active partner in continuous transformation.

These capabilities turn MES into a decision engine rather than a database.

Realizing this vision of an AI-driven decision engine requires an operational foundation that is as dynamic as the factory floor itself. You cannot bolt an agile AI onto a rigid monolith and expect real-time results. This is where composable MES becomes essential.

Tulip gives teams this foundation by adapting to workflows rather than forcing rigid structures. Instead of requiring a team of data scientists to restructure systems, Tulip connects to the data manufacturers already have (like production records, machine signals, quality systems, and existing documentation) without fragile middleware.

Because the architecture is composable, built from modular, flexible components, manufacturers can deploy AI through focused, task-specific apps. This allows teams to solve specific problems without the risk of overhauling the entire system, making the technology natural, accessible, and scalable:

• Modular Scalability: Instead of a risky rip-and-replace, teams can integrate AI into specific apps where it delivers the most value. Improvements happen module by module, ensuring value is delivered without disrupting the wider operation.

• Agile Adaptability: Low-code tools let engineers modify these components directly. When production needs to shift, the app interface changes with it, ensuring the AI remains aligned with the reality on the floor.

• Contextual Intelligence: An open architecture connects data from across the entire operation (machine logs, ERP schedules, and quality records) giving the AI full operational context. It doesn't just see a data point; it understands the complete picture needed to drive accurate decisions.

This foundation transforms AI from a passive analytics tool into an active operational partner. By embedding purpose-built agents directly into the apps operators use every day, you ensure they have the full context needed to be effective.

These agents don't just flag data points; they understand the process. They can detect anomalies in real time, suggest specific next steps, and trigger workflows instantly. This capability closes the final mile between insight and action.

As Mike Rousch at TICO said, “We could use AI to research data and build tables, but we couldn’t act on it until agents came out. Seeing what they could do… that changes everything.”

The factory of the future will not be defined by the volume of data it generates, but by the velocity and quality of its decisions.

For too long, manufacturers have accepted a trade-off between control and agility, trapped by rigid architectures that turned data into a burden rather than an asset. The combination of agentic AI and composable MES breaks that cycle. It effectively ends the era of DRIP, replacing passive dashboards with an active, intelligent nervous system.

This shift closes the insight-to-action gap for good. It liberates operators and engineers from the role of "human middleware", allowing them to stop hunting for information and start solving problems. Whether through predictive maintenance, intelligent scheduling, or automated quality control, the goal is the same: an operation that doesn't just react to volatility, but thrives on it.

Tulip provides the foundation for this new reality, giving manufacturers the power to listen to their operation, understand the context, and act with unprecedented speed and precision. If you're interested in seeing how Tulip AI can help you turn data into actionable insights, reach out to a member of our team today!