Plant operations managers often hear that adopting manufacturing AI means a costly overhaul of the MES or historian. In reality, AI capabilities can often be introduced alongside an existing MES and historian, provided the underlying production data is accurate, detailed, and accessible. In the sections below, we debunk the rip-and-replace myth, explain how to strengthen current systems as an AI-ready data foundation, and show a real-world example of improving data fidelity without replacing existing systems.

The Myth of Full MES Replacement

Many manufacturers assume that a modern AI solution requires a brand-new MES or historian platform. However, the rip-and-replace narrative often overlooks costs such as extended downtime, integration complexity, and the loss of proven operational logic. By focusing on the underlying data foundation rather than replacing the software itself, organizations can preserve existing process knowledge while still preparing for manufacturing AI.

When the data pipeline is already in place, the effort to add AI models can focus on assessing data quality, filling gaps, preparing data, and deploying models rather than a wholesale system swap. For teams that want to avoid the financial and operational impact of a full replacement, the first step is to reassess the capabilities of the current MES and historian and identify where data quality or context needs improvement.

Learn how an MES can support  AI transformation initiatives

Consider a plant with an established MES. The operations team might inventory the data fields already being logged—machine start-up times, batch identifiers, and defect codes—to identify which are suitable for an initial AI use case. That inventory may show that some needed information is already available, while other inputs, such as detailed machine-state changes or accurately attributed downtime, must be added or improved.

Another practical tip is to involve the MES configuration specialists early in the AI discussion. Their knowledge of tag naming conventions, alarm hierarchies, and data retention policies can help translate AI requirements into concrete configuration changes. For example, adjusting a tag’s sampling rate or enabling an additional quality event log may improve the usefulness of data without altering the core MES code.

Leveraging Existing MES and Historian as an AI-Ready Data Foundation

MES and historian systems can capture production data, equipment status, and quality events. When that data is accurate, sufficiently detailed, well-structured, and consistently timestamped, it can contribute to a reliable foundation for manufacturing AI. The key is to identify missing machine-level context and make the data securely available for analysis.

First, they may avoid the cost of replacing existing platforms, although additional software, licensing, or infrastructure may still be needed. Second, engineering effort can be directed toward refining data quality and integration rather than rebuilding an entire system.

Once the necessary data gaps are addressed, AI applications may be introduced for use cases such as predictive maintenance, quality anomaly detection, and production scheduling optimization, depending on the data available and the needs of the operation.

Where the MES and historian already enforce role-based access controls, organizations can apply appropriate permissions to the AI extraction layer, ensuring that only authorized users or applications can access sensitive production streams. Audit logging and data lineage can add traceability, which is especially important for regulated industries where data provenance must be demonstrable.

Real-World Proof and an Actionable AI-Readiness Checklist

A deployment of SmartSights ABLE and OneView across four G&J Pepsi manufacturing sites demonstrated how better production data can strengthen existing manufacturing systems. The project achieved a 70% reduction in engineering time, a 5× improvement in data fidelity, and approximately four weeks to value per site, alongside cost savings and capital avoidance. This example demonstrates the benefits of improving production data and visibility without replacing established systems; it was not presented as an AI model deployment.

Based on that approach, the following checklist helps plant leaders evaluate AI readiness:

1. Audit current MES and historian data for completeness, accuracy, and machine-level context.
2. Define a data governance model that includes version control and access permissions.
3. Identify pilot use cases where AI could add value, such as equipment health monitoring or yield prediction.
4. Develop an integration approach that can provide the required data at an appropriate frequency.
5. Validate model outputs against known performance metrics before scaling.

A focused pilot helps teams test feasibility before scaling.

See the G&J Pepsi case study for detailed results

Once a pilot shows value, teams can extend it to other lines or sites. This scaling process can build on the integration approach used in the pilot, although additional tag mapping, data validation, and model adjustments may be required.

Conclusion

By assessing and improving the existing data foundation, organizations may avoid large MES replacement expenditures, focus engineering effort, and advance digital / AI transformation. Plant operations leaders who focus on data readiness and incremental integration can build a stronger foundation for advanced analytics and future AI applications.

Talk to an Expert

FAQs

1. How can I assess whether my current MES data is ready for AI?

Start with a data readiness audit that checks for completeness, consistent timestamps, machine-level context, and error handling. Ensure that data ownership and governance policies are in place so that AI models can reliably use the information.

2. Will adding AI affect the performance of my existing historian?

An appropriately designed AI integration can read data without modifying the historian’s core functions. However, extraction frequency, query loads, and infrastructure capacity should be evaluated to limit performance impacts.

3. What are the most common AI use cases that work with existing MES data?

Predictive maintenance, quality anomaly detection, production scheduling optimization, and yield forecasting are potential use cases. Their feasibility depends on whether the existing MES, historian, and related systems provide sufficiently accurate and detailed data.

4. How does the incremental approach impact project timelines?

Without a full system replacement, projects can focus on assessing data gaps, preparing information, integrating sources, and validating models. This can shorten implementation timelines, although the actual schedule depends on the data and integration requirements.

5. What steps should I take after a successful AI pilot?

After validating the pilot’s results, scale the solution by extending data pipelines to additional production lines, refining models based on new data, and integrating insights into existing operator workflows.