Industrial AI transformation demands a data cleanup – Etteplan addresses a hidden bottleneck

Etteplan Oyj, Web Release, March 23, 2026

Industrial companies are accelerating their investments in artificial intelligence, automation, and machine learning. Technology service company Etteplan is concerned about a critical yet often overlooked bottleneck that slows down the full-scale adoption of AI: the quality of a company’s core information, its master data, and its level of digital maturity. If these are lacking, AI cannot generate value in factories, production lines, or maintenance operations. Compliance with new regulations also becomes more difficult if the required information is scattered.

The effective utilization of master data is the foundation of industrial performance and business success. Master data includes the company’s essential information, such as product structure data, material item data, and equipment hierarchies that describe what the company owns, manufactures, or maintains.

Without consistent, well-governed, and reliable core data, processes, reporting, and AI simply cannot function reliably.

“AI in particular requires a unified, trustworthy, and well-governed data foundation to work. Without it, AI cannot understand context, identify failure patterns, or optimize production. Poor-quality data is an invisible barrier to industrial competitiveness, and one that has received far too little attention, ” says Eric Tengstrand, Senior Vice President, Solutions and Technologies at Etteplan.

Data determines the return on AI investments
Etteplan has observed a recurring pattern in industrial AI development: the technology is advanced, but the data is fragmented. AI does not fix bad data – it amplifies its weaknesses. When product information, item data, product structures, and technical hierarchies are inconsistent, AI models cannot make reliable conclusions, leading to flawed analyses and unsuccessful pilots.

Etteplan has developed AI-powered data management solutions for both product companies and asset-intensive companies. The Master Data Services for Product Companies (MDS) solution supports the data management needs of product manufacturers, while the Asset Data Services for Asset Companies (ADS) solution addresses the needs of the asset owners in the process industry. With AI-driven solutions, companies can rapidly and cost-effectively improve data governance and the full utilization of data, enabling greater value from AI and stronger returns on AI investments.

A typical example of poor-quality master data is a component that appears under five different item codes in the company’s systems. AI cannot recognize these as the same part, causing predictive maintenance to fail.

“In reality, a company’s data maturity determines whether an AI investment delivers results or whether valuable euros go to waste, ” Tengstrand notes.

“Industrial companies have clearly woken up to this. Many master data initiatives are already underway. When data quality and digital maturity are strengthened, companies in both manufacturing and process industries can not only improve operational efficiency but also develop entirely new business models.”

Regulatory pressure is increasing – and quality requirements are rising
Strong master data management is also essential as regulatory requirements tighten. For example, the EU’s Digital Product Passport (DPP) and the NIS2 cybersecurity directive push companies toward more transparent and traceable reporting. Without solid core data, companies cannot meet these requirements efficiently. Significant time and resources are wasted gathering fragmented, low-quality data from multiple sources.

Etteplan strengthens industrial data readiness and AI maturity by combining process expertise, system understanding, and master data management. The company has strong competence in data harmonization, applying standards and taxonomies, building AI-ready data pipelines and integrations, and developing organizational digital capabilities.

Achieving full data value requires a systematic, well-managed project, which rarely succeeds with internal resources alone. Building a functional data management model and practices demands broad experience that individual organizations often lack. A seasoned, professional partner accelerates progress and significantly improves outcomes.

“Companies that get their data foundation in order now will be better positioned both to leverage AI and to comply with evolving regulations. In practice, this can translate to a competitive advantage and stronger business performance, ” says Tengstrand.

Further information:
Eric Tengstrand, SVP, Solutions and Technologies, Tel. +46 7 0757 7440
Outi Torniainen, SVP, Marketing and Communications, Tel. +358 10 307 3302