5 min read

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AI projects in manufacturing companies typically fail not because of missing technology, but due to a structural weakness rarely discussed openly: fragmented information processes. Strategy papers are written, roadmaps defined, pilot projects launched – and yet initiatives stall at the proof-of-concept stage.

Key Takeaways

• Many AI and digitalization initiatives in manufacturing fail not because of technology, but because of fragmented, inconsistent information processes.
• An information backbone connects physical and digital sources, standardizes metadata, and ensures consistent governance – without replacing existing systems.
• Three field-tested entry scenarios – digital inbox, digital HR files, and governance/AI readiness – show how to build incrementally.
• 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 files, technical manuals, and supplier correspondence are scattered across sites, departments, and countries. Paper meets file shares, emails meet local special solutions, ad-hoc workarounds meet global mandates. A reliable, unified information foundation is missing.

For digitalization, IT, and governance leaders, this creates a structural dilemma: processes are to be automated, data made usable, and AI projects scaled – while it remains unclear where information resides, who may access it, and which rules apply.

Daily operations as the hidden brake

Many industrial companies have invested heavily in ERP, MES, or modern production systems. What is often missing is a connecting layer for unified information and documents – the very layer on which specialist departments, IT, and governance all depend.

An audit, a complaint, or a plant upgrade quickly reveals how fragmented the information landscape truly is. Required documents are “somewhere available,” yet not consistently retrievable. Responsibilities are unclear, document versions contradict one another, retention and deletion periods are interpreted locally.

What is compensated with high manual effort in day-to-day business becomes a genuine risk when scaling and automating. 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 siloed solution, but rather a cross-cutting structure that consolidates physical and digital information, structures it, and makes it controllable. Documents from different 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 unlocked for automated processes through intelligent scanning. Only when these historical data sources are tapped into does manufacturing have a seamless foundation of information at its disposal.

Existing applications such as ERP, HR, or production systems remain in place but are augmented by an information layer that connects processes. Platforms like InSight DXP from Iron Mountain take on a mediating role: they consolidate information, orchestrate document-based workflows, and enable AI-powered classification and enrichment. If you’re curious about the link between data sovereignty and AI adoption, our article on private cloud for AI in regulated industries offers additional insights.

Three entry scenarios that work in manufacturing

How an information backbone becomes tangible in practice is illustrated by three typical industrial use cases. They exemplify document-intensive processes and can serve as the starting point for a scalable roadmap.

“Without active involvement from the specialist departments, even the best technical concept remains ineffective.”

Digital inbox and invoice processing

In many manufacturing companies, incoming mail is scattered across plants and departments. Invoices take circuitous routes, approvals stall, and cash-discount deadlines are missed. A centralized digital inbox – often paired with business-process outsourcing for scanning and capture – creates transparency and clear accountability.

The impact is immediate: shorter cycle times, better controllability, and far less manual rework. For multi-site operations, this is often the simplest and most effective first step.

HR processes and digital personnel files

Globally operating industrial companies – such as automotive corporations – often work with fragmented file archives. Digital HR files simplify access for distributed teams, accelerate audits, and reduce compliance risks.

At the same time, HR processes become more robust – even during site relocations or organizational changes. Standardizing personnel files also creates a solid foundation for automated onboarding and offboarding workflows.

Governance and AI readiness

Many AI initiatives fail not because of technical prerequisites, but due to uncertainty – a challenge central to change management in AI transformation: Which data can be used? Which documents are complete, up-to-date, and correctly classified? This is a familiar pattern – as our report on AI vulnerabilities and real corporate risk demonstrates.

The information backbone helps make data holdings more transparent, identify legacy records, and clean them up in a legally compliant manner. This creates a reliable foundation for AI-driven analytics and decisions – without initiatives being built on flawed or incomplete data.

From pilot 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 digital mailroom – and gradually develop a scalable structure from there.

Compliance, change management, and cross-departmental collaboration are key success factors. Without active involvement from specialist departments, even the best technical concept remains ineffective.

Companies that organize their information processes in this way and build a robust information backbone reduce operational friction, enhance governance security, and lay the groundwork for sustainable automation and AI adoption. A practical starting point is offered by Iron Mountain’s hands-on guide with concrete use cases for realigning information processes in manufacturing.

Frequently Asked Questions

Why do AI initiatives in manufacturing fail so often?

The most common reason isn’t a lack of technology, but a fragmented information foundation. When it’s unclear where documents are stored, who can access them, or which version is current, AI models cannot be trained or deployed effectively.

What is an information backbone – and how does it differ from a DMS?

An information backbone isn’t another siloed solution; it’s an overarching structure that unifies 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 entry point is a clearly defined use case with measurable benefits – such as digitizing incoming mail or employee records. From there, the structure can be gradually expanded to additional areas.

How are physical archives integrated into a digital information backbone?

Intelligent scanning and automated classification can convert legacy documents into digital workflows. Providers like Iron Mountain merge physical archiving with digital processes into a seamless approach.

What role does compliance play in building an information backbone?

Compliance is a critical success factor. An information backbone ensures retention periods, deletion rules, and access rights are applied consistently across systems – rather than interpreted differently in each location.

What does Iron Mountain’s InSight DXP actually deliver?

InSight DXP is a platform that consolidates information from multiple sources, orchestrates document-based workflows, and enables AI-driven classification and enrichment. It bridges 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 doesn’t replace existing systems; it adds a connecting information layer. ERP, HR, and production solutions remain operational and become better integrated through the new structure.

Source of the header image: Adobe Stock / Wahib Khan