Mucitler Elektrik
Corporate
- Thread Author
- #1
Data maturity enables manufacturers to build a robust data infrastructure, allowing them to adopt AI agents faster and with less risk.
The "future" of artificial intelligence (AI) is constantly changing. A few years ago, the focus was on the potential of large language models (LLMs) and generative chatbots. Before that, machine learning (ML) models were crucial for anomaly detection. In the near future, artificial general intelligence (AGI), which can match or exceed human capabilities, is coming to the forefront. By early 2026, the next tangible evolution in the industrial sector and the general corporate landscape is seen as agent-based AI. According to IDC, 45% of organizations will use AI agents at scale by 2030, with the manufacturing sector being one of the priority areas.
### Impact of Agent-Based AI on Industry
Despite this development, market excitement does not always align with the realities on the ground. According to McKinsey research, 23% of organizations are scaling agent-based AI systems in specific areas, while 39% are experimenting with these solutions. However, less than 10% are using AI agents widely with full functionality.
While the development and implementation of industrial AI agents hold promise in theory, integrating them with legacy infrastructures can be challenging. Success requires not only researching and deploying agents but also addressing data quality, structure, and context issues frequently encountered in industrial environments. AI agents can only create value when manufacturers mature their data architecture.
### The Difference of Industrial AI Agents
Agents, in manufacturing plants, warehouses, distribution centers, and infrastructures, undertake autonomous or semi-autonomous tasks beyond just analytical dashboards. Unlike traditional manufacturing execution systems (MES), data historians, and quality systems, agents operate with data orchestration and context awareness between operational technology (OT) and information technology (IT) systems for specific tasks. These tasks require creating specialized agents for functions such as quality, maintenance, planning, and supply chain management.
However, when these agents are deployed across manufacturing cells, lines, and plants, existing infrastructures struggle to deliver the right data to each agent.
### Lack of Data Maturity Blocks Agents
In modern manufacturing, industrial data is often isolated in OT and IT systems, with inconsistent naming conventions and schemas, lacking context, and managed ad-hoc. This makes it difficult for AI agents to find and use the correct data.
While humans in the past could fill in missing information and make decisions based on experience, AI agents operate at machine speed and can turn small issues in the data infrastructure into major system failures. For example, predictive maintenance agents operating with incorrect asset data or quality agents misinterpreting sensor data without context can halt operations or lead to the shipment of faulty products.
Therefore, the overall health of the data infrastructure becomes not just an IT project but an operational imperative. Increasing data maturity is essential to support the rapid and secure implementation of automation and agent-based workflows.
### How Should Data Be Suitable for AI Agents?
Not all industrial data, but only appropriate and reliable data, should be used for AI agents. The data needed by agents must be contextualized, managed at scale, and task-oriented. Manufacturers must first adopt the following elements:
- Flexible open protocols like MCP are designed to support the data needs of AI agents. MCP and similar protocols, combined with industrial DataOps solutions, collect and contextualize data from various sources and present it specifically to AI agents.
- Robust industrial DataOps practices monitor data pipelines, detect and resolve data quality issues, ensuring that AI agents work with reliable and high-quality data.
- Strong data governance establishes ownership, responsibility, and standard definitions in OT and IT data, enabling agents to act accurately and securely. This prevents errors and incorrect decisions resulting from excessive data exposure.
Increasing data maturity is not about completely replacing systems; it's about progressing step-by-step on existing structures to ensure AI agents operate securely within defined boundaries.
### The Foundation of Agent-Based Success
With proper preparation, agent-based AI will transform industrial automation. Early and disciplined investments in strengthening data infrastructure will enable the robust and reliable use of these technologies in the long term. Maturity and readiness will bring reliable success.
