Solutions Overview
From capture to governance, InfoConnect helps you streamline document workflows, automate processes, and deliver trusted information across your organisation—securely and at scale.
Why is getting an answer from company information so hard?
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InfoConnect Marketing
Short answer
Finding company information is still hard because most of it sits in documents spread across shared drives, email, business systems and archives, and much of it was never prepared to be found. Scanned pages are often stored as images, files carry little metadata, several versions compete, and keyword search matches words rather than meaning. Adding systems adds places to look. The fix starts with the information, not with another search box.
Key takeaways
- In Microsoft’s 2023 Work Trend Index, 62% of respondents said they struggle with too much time spent searching for information, a global figure across 31 markets, Malaysia among them.
- Atlassian’s State of Teams 2025 found leaders and teams waste 25% of their time searching for answers.
- Many failed searches trace back to the documents: scattered repositories, image-only scans, missing metadata and competing versions.
- Keyword and semantic search solve different problems; hybrid search combines them.
- An AI assistant answering from the same documents can inherit the same gaps.
How much time do people lose looking for information?
In Microsoft’s 2023 Work Trend Index, 62% of respondents said they struggle with too much time spent searching for information in their workday. The survey covered 31,000 full-time employed or self-employed workers across 31 markets, Malaysia among them, between 1 February and 14 March 2023, so the figure is global rather than Malaysian.
Atlassian’s State of Teams 2025, based on 12,000 knowledge workers and 200 executives, found that leaders and teams waste 25% of their time searching for answers. People want relief: in the Work Trend Index, 86% said they are looking for AI to help them find the right information and answers they need. For a COO or department head, that is the request behind many AI pilots: help us find what we already have.
Why does more information make answers harder to find?
More information can make answers harder to find when volume grows faster than order. In our work, information is often scattered across information silos: systems, emails, shared drives and legacy applications. MIT Sloan reports that, by multiple analyst estimates, 80% to 90% of data is unstructured: text, video, audio and similar content.
Six common causes of a failed search, and what each looks like to the people searching:
| Cause | What your people see | What fixes it |
|---|---|---|
| Information spread across many repositories | Staff must guess where a file lives before they can look for it | One search across connected repositories, or one governed repository for the records that matter |
| Scanned pages stored as images | A search for words clearly on the page returns nothing | Converting scans to text-searchable documents at capture |
| Missing or inconsistent metadata | Results cannot be filtered by customer, case, document type or date | Classification and metadata indexing to an agreed scheme |
| Several versions of one file | Nobody is sure which copy is current, so people ask a colleague | Version control and version history in one place |
| Words that do not match | The procedure says “client onboarding”; staff type “new customer setup” | Search that understands meaning as well as exact words |
| Archived files that drop out of sight | Older records exist but no longer appear in any search | Archiving that keeps documents and their metadata searchable |
The international standard ISO 23081-1:2017 covers the principles that underpin and govern records management metadata.
Is keyword search or semantic search the answer?
Neither on its own: the two solve different problems, which is why hybrid search combines them. Keyword search, as Google Cloud puts it, “focuses more so on finding exact matches between the keywords in a query and the keywords in a document.” That makes it strong for policy numbers, invoice references and exact names, and weak when the writer and the reader of a document use different words for the same thing.
Elastic defines semantic search as a search engine technology that interprets the meaning of words and phrases. In Elastic’s words, “Hybrid search mitigates the weaknesses of individual retrieval methods while leveraging their strengths in a single pipeline.” Our older guide on the benefits of enterprise search covers what unified search does for a business; the point here is that no search method finds a document that was never made searchable.
What changes when an AI assistant answers from the same documents?
An AI assistant that answers from company documents can inherit the gaps in them. If the current policy is buried under old versions, it can quote an old one. If a scan was never converted to text, it may not be able to read it. If permissions were never applied properly, it may surface what a user should not see. AI is only as useful as the enterprise information it can access, understand and trust.
OWASP’s Top 10 for LLM applications (LLM08:2025, Vector and Embedding Weaknesses) warns that inadequate or misaligned access controls can lead to unauthorised access to embeddings containing sensitive information, which the model could then retrieve and disclose. Its recommended mitigation is fine-grained access controls and permission-aware vector and embedding stores. So organise and govern the documents first, then add the AI layer: our guide on making enterprise information ready for AI sets out that foundation, and our approach to trusted AI starts from the same place.
How can you find where your answers get stuck?
Follow five real questions through your own systems rather than auditing every repository at once. Noting where people look, what they find, and whether it is current, searchable and correctly permissioned shows which of the six causes recurs most in a department, and so which one to fix first.
- Pick five questions your team asks every week, such as the latest approved procedure, a customer’s signed form or the last decision on a recurring exception.
- Ask two or three people to find each answer and note every place they look. Many places means scattered information.
- Check what they found. Was it current, readable by search, and found by search rather than by asking a colleague?
- Check who could see it. Could someone open a document they should not, or miss one they are entitled to?
- Fix the cause that recurs most before adding any AI tool: name an owner and one approved home for each document type, then let indexing, version control or connected search enforce it.
Where do Docuflo and Docuflo Inventa fit?
Our Docuflo document management system addresses the document-side causes in one centralised repository. Documents are captured and indexed, and document classification and metadata indexing organise them with searchable metadata for easy retrieval; version control and version history deal with competing copies; keyword, full-text and semantic AI search cover mismatched wording; and inactive documents moved to secondary storage, where supported, stay searchable, with their metadata, access permissions and audit history kept.
Role-based and folder- and document-level access controls, encryption and audit trails govern who sees what. Docuflo works on its own, with APIs to connect other business systems. See what Docuflo does.
We have worked in information and content management since 1994, and our view is simple: AI is the interaction and intelligence layer, and the trusted information foundation behind it decides how useful its answers can be.
Frequently asked questions
Is enterprise search the same as a document management system?
No. A document management system stores and governs documents: classification, metadata, versions, retention, access and audit trails. Enterprise search finds information across many repositories at once. Our Docuflo Inventa enterprise search is a separate product from our Docuflo document management system.
Do we need to move everything into one repository first?
Not always. Federated search can search connected repositories at once, subject to configured permissions, so information can stay where it is. Documents that must be controlled, such as approved procedures, signed forms and records under retention, are better kept in a governed repository with version control, audit trails and retention policies.
Will an AI assistant fix our search problem?
Not on its own. In Microsoft’s 2023 Work Trend Index, 86% said they are looking for AI to help find the right information and answers they need. But an assistant answers from the same documents your staff search today, so scattered, unversioned or badly permissioned files give it the same trouble, and OWASP warns weak access controls can let a model disclose sensitive content.
Sources
- Microsoft WorkLab, Work Trend Index 2023: Will AI Fix Work? (9 May 2023).
- Atlassian, State of Teams 2025 (7 March 2025).
- MIT Sloan, Tapping the power of unstructured data.
- Elastic, What is semantic search? and What is hybrid search?
- Google Cloud, What is semantic search?
- ISO, ISO 23081-1:2017 Metadata for records, Part 1: Principles.
- OWASP Gen AI Security Project, LLM08:2025 Vector and Embedding Weaknesses.
Talk to InfoConnect
We have provided information and content management solutions since 1994, from our head office in Petaling Jaya, Selangor. To talk about document management, workflow or data governance for your organisation, contact our sales team.
Email sales@infoconnect.com.my Call +603-7722 2001 Contact page
Existing customers can reach our support team at support@infoconnect.com.my.