When people talk about “cleanliness” in a company, many first think of data quality. What is almost always forgotten is that most business-critical decisions are not based on databases, but on documents, such as contracts, presentations, guidelines, protocols, or concepts.
For CEOs and CIOs, the question is therefore not only: How good is our data? But also:
How good are our documents, and can we even use them reliably, including for AI?
What does "clean documents" mean?
Clean documents do not refer to nicely formatted files, but rather to documents that:
- are easily findable (anyone who needs something can find it quickly and clearly),
- are current and valid (clearly recognizable what the current status is),
- are consistent with other documents,
- are clearly structured and understandably formulated,
- have a defined status (draft, reviewed, released).
In short: Clean documents are reliable building blocks of knowledge on which your business, your compliance, and your future use of AI in the company can be built.
Typical problems in everyday document management
Almost all organizations exhibit similar patterns in document management:
- Discoverability
- Multiple storage locations (file server, SharePoint, Teams, local drives)
- Multiple versions with similar file names
- Knowledge resides in people’s minds; you just have to ask the “right person”.
- Contradictory content
- Different versions of contracts, guidelines, or product descriptions
- Old presentations with outdated figures are still circulating.
- Teams work based on different levels of information.
- Legal risks and compliance gaps
- Incorrect or outdated contract templates are being used.
- It is not entirely clear which version was actually released.
- Documents with regulatory relevance are incomplete or difficult to locate.
- Inefficiencies
- Time wasted searching, asking questions and “rewriting”
- Duplication of effort because nobody knows that a document already exists.
- Delayed decisions due to missing or unclear documents
Each of these weaknesses has a direct cost impact, even if it rarely appears explicitly on a cost center.
The business case: What bad documents really cost
Two simple examples illustrate the scale:
- Incorrect contract:
An outdated contract template is being used. An important clause regarding liability or notice periods is missing or no longer valid. This only becomes apparent in the event of a dispute, and then it becomes expensive: legal battles, renegotiations, and reputational damage. - Incorrect Information in Management Reporting:
A reporting deck is compiled from various documents. An Excel spreadsheet from the last quarter and a presentation from an old project folder are included. The figures are incorrect, trends are misinterpreted, and wrong priorities are set. Decisions are based on seemingly plausible, but simply false, documents.
If you extrapolate these cases, including time expenditure, opportunity costs and legal risk, it quickly becomes clear how relevant the topic is.
A simple calculation example:
- On average, 50 managers spend only 2 hours per week searching for or requesting documents.
- Internal hourly rate: 100 euros.
- 46 working weeks per year.
Calculation:
50 people × 2 hours per week × 100 euros × 46 weeks
= 460,000 euros per year, solely due to search efforts and inefficiencies in handling documents.
This doesn’t even include wrong decisions, project delays, or legal risks.
Document quality is a business issue, not an IT detail.
Clean documents are a prerequisite for using AI
Many companies are currently investing in AI, generative AI, and large language models to extract knowledge from documents: automatic summaries, contract analyses, policy questions and answers, and much more.
What is often overlooked is
that AI amplifies the condition of your documents, for better or for worse.
- If documents are outdated, AI generates outdated answers.
- If documents are contradictory, AI cannot provide a consistent view.
- If documents are poorly structured, meaningful extraction and evaluation become more difficult.
AI can do a lot, but it cannot replace a lack of governance. “Garbage in, garbage out” applies to documents just as much as it does to data quality.
Anyone who wants to successfully use AI in their company therefore first needs a clear answer to two questions:
- Which documents are truly critical for our business?
- What condition are they in today, and can we entrust them to an AI with a clear conscience?
What clean documents mean from an organizational perspective
Clean documents are not created simply by using a new tool or platform. They are the result of clear responsibilities and rules in document management.
- Ownership : Who is professionally responsible for specific types of documents, for example contracts, guidelines, product documentation or reporting documents?
- Life cycle : How are documents created, reviewed, approved, versioned, and archived?
- Structure and storage : Where are they stored, how are they named, what metadata is mandatory?
- Use of AI : Which documents may be processed by AI systems and under which security and compliance requirements?
This makes document quality a leadership responsibility, especially for CEO and CIO:
- The CEO ensures that governance and culture reflect the importance of clean documentation.
- The CIO ensures that platforms, access models and processes support this, also with regard to AI in the company.
Checklist for C-level executives: How mature is your organization when it comes to documentation?
Use this short checklist as a starting point:
- Have we defined critical document types?
Do we have a clear list of document types that are business and risk-critical, for example, contracts, policies, product documentation, or reporting documents? - Are responsibilities clarified?
Is there a clear professional responsibility (owner) for each of these document types? - Standardized templates and processes?
Do we work with centrally maintained templates and defined approval processes? - Central, unified storage?
Is there a clearly defined location for approved documents, or are there multiple parallel, competing storage locations? - Are versions under control?
Is it clearly identifiable which is the valid version?
Are old versions properly archived? - Has the findability been tested from the user’s perspective?
Can employees find typical documents in under 30 seconds, without insider knowledge? - Is there regular quality assurance?
Are there reviews and checks for critical document types, for example, annual reviews of templates and guidelines? - Have we assessed AI suitability?
Have we evaluated which documents are suitable for AI applications, considering aspects such as structure, quality, and legal considerations?
Are there guidelines on how AI may interact with our documents? - Is there awareness of document quality in management?
Is document quality a regular topic of discussion in management, or only when problems arise?
If you answer “No” or “Unclear” to several of these questions, it’s a clear signal:
your documents are probably not ready for the next step, neither for efficient collaboration nor for the productive use of AI in the company.
Make document quality a top priority!
Start with the checklist – and lay the foundation for reliable decisions and AI-enabled knowledge processes.
Dategro supports you in your document quality analysis.
Dategro partners with mid-sized industrial companies to transform disconnected commercial data into unified performance dashboards—without replacing core systems or creating IT headaches.
