Knowledge management with AI: How companies secure their corporate knowledge

A strategic asset under pressure

Knowledge is an invisible asset in every organization - it is contained in documents, tools, processes and, above all, in the minds of employees. But this capital is under threat: according to a study by McKinsey & Company, employees spend an average of 20-30% of their working time just looking for information or trying to find it again. At the same time, Switzerland is facing a huge demographic challenge: over the next few years, more than one million baby boomers will retire and leave their companies. With them comes the threat of a loss of knowledge built up over decades - context-laden but barely documented.

For Swiss SMEs, knowledge management is therefore no longer just an IT issue. It is becoming a strategic necessity for the long-term success of the company - especially in an environment characterized by accelerated change, a shortage of skilled workers and ever-increasing compliance requirements.

Knowledge silos, search frustration and data protection pressure: where SMEs stand today

In practice, many companies struggle with outdated or incomplete knowledge processes. A typical diagnosis in Swiss SMEs looks something like this:

• Information overload & data silos: Many organizations use ten or more parallel systems - project management tools, local servers, cloud storage, email inboxes and more. The result: fragmented knowledge, duplicate data storage and wasted time due to inefficient searching.
• Undocumented empirical knowledge: Long-serving employees in particular have a deep understanding of processes and context. However, this is often lost if it is not recorded in a structured way - a ticking time bomb in view of the impending wave of retirements.
• Inefficient search processes: If you need 15 minutes to find an old log or a specific piece of customer information, you not only lose productivity, but also the quality of your decisions.
• Compliance pressure: Data protection laws such as the revised DPA and industry-specific requirements (e.g. FINMA, MDR) make it necessary to manage knowledge in a traceable and controlled manner.

Modern AI changes everything

In this mixed situation, artificial intelligence offers concrete solutions - provided it is used correctly. The following technologies are examples of how AI is transforming knowledge management today:

1st Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) combines the strengths of large language models with the precision of internal knowledge databases. Unlike traditional chatbots, which "hallucinate" answers or draw them from publicly accessible sources, a RAG system draws specifically on internal company documents - such as contracts, tickets, emails or SOPs. The generated answers automatically contain the appropriate source references, which massively increases traceability and technical security.

What this looks like in practice: A technician is standing in front of a complex device at the customer's premises and needs the specific maintenance log for this particular model. Instead of laboriously searching through PDFs or contacting colleagues by phone, he asks a short question in the company's internal AI chatbot - and receives the relevant document within seconds, including the highlighted text passage and original source. This not only saves time, but also increases the first-time resolution rate in service.

The advantages at a glance:

• Time saving: Information that used to take 10-20 minutes to find is available immediately.
• Reliability: Instead of relying on memories or outdated filings, the user receives an objectively verifiable answer.
• Productivity levers: knowledge is activated in a context-related manner - exactly where it is needed, e.g. in the field, in meetings or during onboarding.

2. AI agents for recurring tasks

While RAG systems react to specific queries, AI agents are proactive: they take over defined, repetitive knowledge processes completely independently. These include classifying new incoming documents, setting metadata or summarizing longer content for specific target groups.

What this looks like in practice: A project assistant uploads a new request for quotation to the data room. The AI agent automatically recognizes what type of request it is, classifies it as a "B2B offer", adds the appropriate customer master data from the CRM, adds a timestamp to the document and automatically informs the responsible sales team - including a summarized abstract in the email text.

The advantages for SMEs:

• Cost efficiency: Where manual work was previously necessary, processes now run autonomously in the background.
• Scalability: The processing quality remains high even with increasing data volumes - without additional personnel.
• Error prevention: Automated classifications follow a clearly defined set of rules and reduce human error.

Especially for smaller companies with lean teams, AI agents can make the difference between reaction mode and proactive knowledge utilization.

3. generative AI as a knowledge multiplier

In addition to pure information retrieval and process automation, a third area of application is becoming increasingly relevant: the intelligent transformation of knowledge. Generative AI models such as GPT-4 can restructure, summarize, translate or reformulate content for specific target groups - with a quality that was unthinkable just a few years ago.

What this looks like in practice:
A managing director wants an overview of all project statuses from the last four weeks - without having to read every single meeting minute. The AI automatically creates a compact one-pager briefing with traffic light status, action required and comments - even in the appropriate corporate wording if desired.
Or: A new employee receives an individually compiled learning path based on her area of responsibility, consisting of automatically generated learning modules, summarized process documents and interactive tests.

This means in practice:

• Better basis for decision-making: Information is not only retrieved, but prepared in such a way that it is immediately understandable and usable.
• Shorter training periods: New employees are provided with the relevant knowledge in a targeted manner - without overload.
• Internationalization made easy: With automated translation and context adaptation, content can be prepared for different markets without duplicating editorial work.

Where are the limits?

As with any technological innovation, the same applies to the use of artificial intelligence in knowledge management: the benefits are only as great as the care taken in implementing them. It would be naïve to only emphasize the opportunities without also realistically naming the challenges. Three central areas of risk occur particularly frequently in practice - but they can be countered in a targeted manner.

A first and particularly sensitive area concerns data protection and the protection of intellectual property. Many generative AI systems - especially public platforms such as ChatGPT - process input content on servers outside the company or even outside Switzerland. This poses the risk of confidential information being leaked via so-called prompts (user input). This is a no-go for companies with increased security requirements. The solution lies in the use of private or sovereign LLM instances, supplemented by encrypted data rooms that offer both hosting in the desired jurisdiction and full access control.

Another risk is incorrect or hallucinated AI answers. If a language model encounters unstructured or contradictory data, there is a risk that it will generate incorrect information - often with convincing linguistic certainty, but without a factual basis. This is particularly problematic in regulated industries or when making business-critical decisions. This is where the use of Retrieval Augmented Generation (RAG) is recommended, where answers come directly from verified company sources. In combination with human review processes, this ensures the highest level of information quality.

The third area of risk concerns the traceability and logging of knowledge processes. Without audit-proof logs, audits can fail and there is a lack of transparency as to who created, changed or used which information and when. To prevent this, advanced systems rely on unalterable, seamless logs - for example using blockchain time stamps that document and secure every user action.

AI-supported data rooms "Made in Switzerland"

AI-supported data rooms with encrypted access and auditable traceability are a promising approach for trustworthy knowledge management. There are also providers in Switzerland that specialize in the application of secure knowledge management and harness the benefits of AI in a protected space. One example of this is the Swiss provider OriginStamp.

Practical example: OriginVault

‍OriginStamp combines the security of blockchain-based time stamps with the power of generative AI. It is based on a certified, encrypted data archive into which companies upload their documents and information. Based on this, users can search through large amounts of data in a matter of seconds - using a full-text search or interactive chatbot. The AI extracts relevant information in a targeted manner, even from complex or scanned documents (including OCR recognition). All user actions and AI responses are logged in an audit-proof manner and provided with a blockchain timestamp. This not only protects the data room, but also ensures that all knowledge access is transparent and traceable.

Why this is relevant for Swiss SMEs:

• Hosting in Switzerland - no outsourcing to US clouds
• Auditable logs - traceability of knowledge processes
• Flexible integration - API connection or white label option

Tip: In our CorpInSight podcast with OriginStamp, you can find out why the combination of AI & blockchain is becoming a key technology for modern companies.

Whether an international platform is sufficient or a data-sovereign solution such as OriginStamp is more suitable depends on various factors such as budget, performance requirements, security needs or integration requirements - the right choice is always individual.

Recommendations for action: How SMEs can take a structured approach

A structured approach is needed to ensure that AI-based knowledge management becomes not just an IT project, but a genuine strategic lever. The following six steps form a tried-and-tested framework:

1. Inventory: What data sources are available? What is their quality? Where are the access barriers?
2. Define pilot project: A specific use case (e.g. service documentation) as a test field.
3. Establish data hygiene: Structured metadata, clear authorizations, consistent archiving guidelines.
4. Make technology choices: Selection of suitable tools such as private LLMs, secure data rooms, interfaces.
5. Change management & training: Involve employees at an early stage, promote a culture of knowledge instead of a fetish for tools.
6. KPIs & roll-out planning: Key performance indicators such as search time reduction, acceptance rates, audit capability.

Conclusion: Knowledge is working capital - put it to good use

The digital future does not call for "more data", but for more usable knowledge. Those who manage to make existing knowledge accessible, trustworthy and efficiently usable will not only strengthen their ability to innovate, but also their resilience to change.

AI is not an end in itself, but a tool - one that, when used correctly, already delivers concrete added value today. For Swiss SMEs that proceed with a clear strategy, secure technologies and partnership-based support such as that provided by CorpIn, entry into Knowledge Management 4.0 is not a dream of the future - it is a reality.