5 min Reading Time
AI initiatives in manufacturing companies typically don’t fail due to a lack of technology, but because of a structural weakness rarely discussed openly: fragmented information processes. Strategy documents are written, roadmaps defined, pilot projects launched – and yet, initiatives stall at the proof-of-concept stage.
TL;DR
- 🔍 Many AI and digitalization initiatives in manufacturing fail not due to technology, but because of fragmented, inconsistent information processes.
- 🏗️ An information backbone connects physical and digital sources, standardizes metadata, and ensures end-to-end governance – without replacing existing systems.
- 🚀 Three proven entry scenarios – digital mailroom, digital HR files, and governance/AI-readiness – show how to build this backbone step by step.
- 📊 According to Gartner, 85 percent of all AI models fail due to poor data quality or missing relevant data (Gartner, 2025).
- ⚙️ Platforms like InSight DXP consolidate physical and digital information, orchestrate workflows, and enable AI-powered classification and enrichment.
The root cause runs deeper: invoices, quality documents, HR records, technical files, and supplier correspondence are scattered across locations, departments, and countries. Paper meets file shares, emails collide with local niche systems, individual workarounds contradict global standards. A reliable, unified information foundation is missing.
For digitalization, IT, and governance leaders, this creates a structural dilemma: processes need to be automated, data made usable, and AI projects scaled – yet it remains unclear where information resides, who is allowed to access it, and which rules apply.
Daily Operations as a Roadblock
Many industrial companies have heavily invested in ERP, MES, or modern production systems. What’s often missing, however, is a connecting layer for standardized information and documents – precisely the level that business units, IT, and governance teams all depend on.
An audit, a customer complaint, or a plant renovation quickly reveals how fragmented the information landscape truly is. Required documents may be “somewhere available,” but they are not consistently findable. Responsibilities are unclear, document versions contradict each other, and retention and deletion periods are interpreted locally.
What is compensated for daily through high manual effort becomes a real risk when scaling and automation are attempted. The result: even ambitious digitalization initiatives remain piecemeal because the foundation is missing.
What an Information Backbone Delivers
We’re not talking about yet another isolated solution, but a comprehensive structure that brings together physical and digital information, organizes it, and makes it controllable. Documents from diverse sources become centrally visible, metadata is standardized, and governance rules are consistently enforced across locations and systems.
A well-designed approach bridges the gap between physical archives and digital systems: even legacy records are made available for automated processes through intelligent scanning. Only when these historical data sources are unlocked can manufacturing access a complete, continuous information foundation.
Existing applications such as ERP, HR, or production systems remain in place but are enhanced with an information layer that connects processes. Platforms like InSight DXP from Iron Mountain act as intermediaries: they consolidate information, manage document-based workflows, and enable AI-powered classification and enrichment. For readers interested in the link between data sovereignty and AI adoption, our article on Private Cloud for AI in Regulated Industries offers complementary insights.
Three Entry Scenarios That Work in Manufacturing
How an information backbone becomes tangible in practice is illustrated by three typical use cases from industry. They exemplify document-intensive processes and can serve as starting points for a scalable roadmap.
“Without active involvement from business departments, even the best technical concept remains ineffective.”
Digital Mailroom and Invoice Processing
In many manufacturing companies, incoming mail is distributed across plants and departments. Invoices take long routes, approvals are delayed, and early-payment discounts are missed. A centralized digital mailroom – often combined with business process outsourcing for scanning and data capture – creates transparency and clear responsibilities.
The impact is immediate: shorter processing times, better control, and significantly less manual rework. For companies with multiple locations, this is often the simplest and most effective first step.
HR Processes and Digital Personnel Files
Globally operating industrial companies – such as automotive groups – often work with fragmented document archives. Digital HR files simplify access for distributed teams, speed up audits, and reduce compliance risks.
At the same time, HR processes become more resilient – even during site relocations or organizational changes. Standardizing personnel files also creates a solid foundation for automated onboarding and offboarding processes.
Governance and AI-Readiness
Many AI initiatives fail not due to technical shortcomings, but due to uncertainty – a challenge also central to Change Management in AI Transformation: Which data may be used? Which documents are complete, current, and correctly classified? This is a well-known pattern – as shown in our report on AI Vulnerabilities and the Real Business Risk.
The information backbone helps make information inventories more transparent, identify legacy data, and securely clean them up. This creates a reliable foundation for AI-driven analysis and decision-making – without initiatives being built on faulty or incomplete data.
From Standalone Project to Scalable Structure
Successful industrial companies don’t treat information processes as isolated optimization measures. They start with a clearly defined use case – such as the digital mailroom – and gradually build a scalable structure from there.
Compliance, change management, and business unit involvement are key success factors. Without active participation from business departments, even the best technical concept remains ineffective.
Companies that systematically organize their information processes and establish a robust information backbone reduce operational friction, increase governance security, and lay the groundwork for sustainable automation and AI adoption. A structured entry point is offered by the practical guide from Iron Mountain, featuring concrete use cases for redefining information processes in manufacturing.
Frequently Asked Questions
Why do AI initiatives in manufacturing so often fail?
The most common reason isn’t missing technology, but a fragmented information foundation. When it’s unclear where documents are located, who is allowed to access them, and which version is current, AI models cannot be meaningfully trained or deployed.
What is an information backbone, and how is it different from a DMS?
An information backbone is not another isolated solution, but a comprehensive structure that integrates physical and digital information, standardizes metadata, and enforces governance rules across systems. A traditional DMS typically covers only digital documents within a limited scope.
How do I start building an information backbone in my company?
The recommended starting point is a clearly defined use case with measurable benefits – such as a digital mailroom or personnel file digitization. From there, the structure can be gradually expanded to other areas.
How are physical archives integrated into a digital information backbone?
Through intelligent scanning and automated classification, even legacy documents can be integrated into digital workflows. Providers like Iron Mountain combine physical archiving services with digital processes into a seamless approach.
What role does compliance play in building an information backbone?
Compliance is a central success factor. An information backbone ensures that retention periods, deletion rules, and access rights are consistently and uniformly enforced across systems – rather than being interpreted differently at local levels.
What exactly does Iron Mountain’s InSight DXP deliver?
InSight DXP is a platform that consolidates information from various sources, manages document-based workflows, and enables AI-powered classification and enrichment. It connects digital workflows with physical services such as scanning, archiving, and governance.
Do existing ERP or MES systems need to be replaced?
No. An information backbone does not replace existing systems but enhances them with a connecting information layer. ERP, HR systems, and production solutions remain in operation and are better interconnected through the new structure.
Further Reading
- Private Cloud for AI: Why Regulated Industries Are Betting on On-Premises – cloudmagazin
- 87 Percent See AI Vulnerabilities as the Biggest Risk – cloudmagazin
- Change Management in AI Transformation – Digital Chiefs
More from the MBF Media Network
- Bringing Production Back to Europe: Restructuring Supply Chains – MyBusinessFuture
- EU AI Act 2026: What Companies Need to Implement Now – Digital Chiefs
- Cybersecurity Trends 2026: The 7 Key Developments – SecurityToday
Header Image Source: Adobe Stock / Wahib Khan
