ChatGPT And GenAI Started It, Agentic AI Kept It Going, Now MCP Can Deliver On The Promise At Scale

The 2025 Verdantix global corporate survey highlights the structural barriers that continue to constrain industrial AI adoption. When asked about the most significant challenges facing analytics and AI initiatives, 70% of respondents indicated that their technology ecosystem does not currently support the use of AI – while 78% answered that poor-quality or incomplete data present a major impediment.

These findings reinforce a consistent theme: the primary obstacle to industrial AI success has not been a lack of tools or algorithms, but the difficulty of deploying AI effectively within complex IT and OT environments. Fragmented architectures, legacy systems and inconsistent data models continue to limit organizations’ ability to translate technical capability into operational value.

This challenge was already evident during the first wave of industrial copilots in 2024, before intensifying in 2025 with the emergence of agentic AI. The sheer number of specialized agents required to automate complex industrial workflows, combined with siloed and unstructured data sources, has stalled many organizations’ attempts to deploy and leverage this next-generation technology. Just 4% of manufacturing firms are experimenting with or piloting AI agents, and only 1% are scaling the technology, according to research by McKinsey & Company; work is needed to allow organizations to create impact through scaling deployments.

Model context protocol (MCP) is the technology now coming to the forefront of this issue. The open-source standard allows AI agents to connect to tools, systems and data sources through standardized interfaces, reducing the need for bespoke integrations, acting in a similar fashion to PROFINET in the automation world. This ease of integration massively improves an organization's ability to field agentic AI, enabling effective switching between LLMs, reducing hallucinations, and improving governance and data security. Major industrial technology vendors such as AWS, Databricks, HighByte, Microsoft and Siemens, are putting significant resources behind expanding MCP use.

By lowering integration friction, MCP enables organizations to deploy contextualized data services that can be consumed by multiple agents simultaneously. This supports faster experimentation, easier scaling and more consistent performance management. In principle, MCP allows firms to move from isolated pilots to repeatable, enterprise-grade AI deployments capable of delivering measurable ROI. However, MCP alone does not resolve the underlying data foundation problem. The effectiveness of agentic systems ultimately depends on the quality and availability of enterprise sources of truth, including ERP, MES/MOM, historians, SCADA and asset management platforms. If these systems remain poorly integrated or inconsistently modelled, MCP-enabled agents will simply automate fragmented decision-making.

The window for ‘first mover’ advantage in industrial AI is closing rapidly. Early adopters are now shifting from experimentation to scaled deployment, embedding agentic capabilities into maintenance planning, production optimization, engineering change management and supply chain coordination. Organizations that fail to modernize their data architectures risk being structurally disadvantaged as competitors institutionalize AI-driven operating models.

Technology providers are increasingly aligning their portfolios to support this transition. MES/MOM vendors such as Aegis Software, Critical Manufacturing, SAP and Siemens are expanding capabilities for contextualization, orchestration and semantic modelling. These platforms are becoming critical intermediaries between operational systems and MCP-enabled agent layers.

The onus is now on industrial and manufacturing organizations to invest in the foundational digital frameworks and technologies if they have previously been reluctant to do so. Having well-integrated enterprise tools across PLC, ERP, MES/MOM, QMS and more is no longer a luxury for forward-looking firms – it is an operational requirement. MCP can and will fuel agentic AI adoption at scale, and organizations should make sure they are not being left behind.