A tacit knowledge example is when an experienced administrator can tell at first glance that a case is incomplete, risky or urgent, even though that judgement is not fully set out in any guide. Another tacit knowledge example is when a project manager knows from experience which coordination with which specialist department is needed early on, even though the official process does not explicitly describe this.
It is precisely this kind of experience-based knowledge, which is hard to put into words, that matters to companies when they want processes to become more reliable, knowledge to stop depending on individual people, and AI to work with company knowledge in a controlled way. If you are preparing initiatives of this kind, you will find relevant starting points in Building AI governance in the company and on the topic of Planning an AI assistant for companies sensibly.
What tacit knowledge means in a company
Tacit knowledge is experience-based knowledge that people use continuously in their day-to-day work, but often do not fully document or cannot spontaneously turn into clear rules. It develops through practice, repetition and dealing with both typical and atypical cases.
A simple comparison helps to distinguish it:
- Explicit knowledge is found in manuals, process descriptions, checklists or databases.
- Tacit knowledge shows itself in routines, judgements and tried-and-tested ways of working.
This matters for organisations because many processes do not work only because of formal procedures. They also work because employees recognise exceptions, set priorities correctly and interpret connections that only appear partly in a written description.
Tacit knowledge is especially common in fast decisions, in assessing incomplete information, in having a feel for communication and in choosing a practical order for work steps.
If you are planning, replacing or extending software, you should therefore not only capture functions, but also look at the knowledge that currently exists in people’s heads, emails or habits. For a structured way to collect requirements, a requirements specification template as a basis can help.
AI in particular makes this clear: support only becomes truly useful once it is clear what knowledge exists, where it sits and which parts of it can be turned into structured information, rules or knowledge sources. For an initial assessment of such an initiative, a project outline with follow-up questions can be useful.
Tacit knowledge: examples from day-to-day work
In everyday work, tacit knowledge often appears wherever data on its own is not enough. In a web platform for local authorities covering sewer networks, condition and planned measures, there is therefore more than just data storage. It also contains specialist knowledge about how conditions are assessed, measures are prioritised and information is brought together in a meaningful way. Projects like these typically fall under bespoke software and operations services.
It is similar in sales and customer work. In a CRM for an insurance broker, tacit knowledge shows itself in which customer cues matter in a conversation, when a case should be followed up and which piece of information really matters for the next action.
Another example comes from project control. In an extension that connects Azure DevOps with planning software, experience-based knowledge often lies in how specialist planning and development work are properly aligned so that no media discontinuities arise. Azure DevOps is a platform for planning, tracking and delivering software work. To turn requirements into a workable implementation plan, it often helps to look at a functional specification as the next step.
This is also easy to spot in day-to-day office work:
- An experienced assistant can immediately see which enquiry needs to be escalated straight away and which should first go through a standard loop.
- Two proposals may look formally similar, but an experienced person can tell from the wording, follow-up questions or familiar patterns which one will later involve more coordination.
- An IT manager knows from experience which change should first be tested on a small scale because unexpected effects may arise elsewhere.
These examples show that companies need to do more than just gather information. They should also make the reasons behind decisions, exceptions and everyday terminology visible. If you want to prepare requirements in a more structured way, you can work with a functional specification template to download.
Why tacit knowledge is crucial for processes, software and AI
As soon as important experience-based knowledge exists only with individual employees, avoidable friction arises in day-to-day work. Cover becomes uncertain, onboarding takes longer, handovers remain incomplete, and later changes to processes or software are based on assumptions rather than traceable knowledge.
This has direct consequences for software projects. If only the official target process is digitised, it is often exactly the professionally important intermediate steps, exceptions and reasons for decisions that are missing, even though these are what make the process work in practice. Anyone preparing requirements should therefore not just collect functions, but also look at roles, deviations and responsibilities. A clear view of stakeholders and interests often helps here.
The situation is similar with AI projects. A search across company knowledge, an assistant or document extraction only delivers useful results if terms, sources, approvals and typical exceptions have been clarified beforehand. Azure OpenAI is a service in Microsoft Azure that allows companies to integrate AI functions into their own applications and processes. The knowledge can remain in the Microsoft Cloud within the company’s own Azure environment in the EU. This technical basis supports controlled integration, but it does not replace the specialist preparation.
What matters, then, is not only the distinction between tacit and explicit knowledge. What matters is how experience-based knowledge becomes a reliable basis for software, search, assistants and process support. If you want to clarify the organisational governance for this, you will find a suitable next step in the article on AI governance in the company.
How to make tacit knowledge visible and usable
To make tacit knowledge usable, concrete questions help more than abstract conversations about ways of working. Instead of simply asking, “How do you work?”, you will get to the crucial experience-based knowledge more quickly with more precise questions: How do employees recognise a problematic case? Which exceptions occur regularly? Which information is repeatedly missing in practice?
It is just as important to compare the documented process with day-to-day reality. Differences often do not appear in fundamental issues, but in details:
- in approvals
- in the actual order of steps
- in typical follow-up questions
- in differing priorities
The next step is to translate the knowledge into suitable forms. Some of it belongs in checklists, some in data fields, some in decision aids, some in role permissions or knowledge articles. If you want to gather requirements properly, a clear structure from the outset helps.
Language is a common stumbling block. If departments use different terms for the same matter, search, analysis and automation often fail at the level of terminology alone. This is especially important for AI functions. If you want a better sense of the practical side of this, you will find a suitable overview in Prompt engineering in companies.
Also work with real cases rather than only theoretical process diagrams. Example cases, typical special cases and unclear documents usually show more quickly where decision-making knowledge really sits. Even so, not everything has to be fully formalised. Some experience remains a matter of human judgement. The aim is not to replace every intuition, but to make recurring patterns understandable.
For handover, further development and reducing workload, specialist and technical documentation that describes architecture, interfaces and operations in a traceable way is ultimately helpful. Interfaces are the defined connections between systems.
How to tell when a project is worth doing now
A good time is often when a system is to be replaced, extended or connected with other applications, but the crucial business logic still exists in people’s heads, emails or spreadsheets.
Other typical signs are:
- New employees take a long time to handle cases confidently, even though processes are formally documented.
- Specialist departments want search across company knowledge, AI assistants or document extraction, but sources, terms and responsibilities are still unclear.
- Misunderstandings repeatedly arise between business departments, IT and external parties about what is a normal case and what is a special case.
In that situation, what usually helps is not a big AI promise, but first a proper assessment of knowledge, processes, systems and roles. In projects like these, ballmüller solutions combines concept, design, development and operations in one place, working with modern .NET, SQL databases, Microsoft Azure, Microsoft 365 and Entra ID. Entra ID is Microsoft’s identity service for sign-in and rights management.
For an initial rough estimate of project size, the cost calculator is suitable.
Frequently asked questions about tacit knowledge in companies
What is a simple tacit knowledge example?
A simple example is when an experienced person can tell from a short email that a case will later lead to follow-up questions, delays or coordination. This judgement is often not written down in any work instruction, but it is crucial to how the process runs.
Why is tacit knowledge important for AI in a company?
AI can only work with what has been professionally prepared, named and made accessible. If important terms, exceptions and reasons for decisions exist only in people’s heads, the results of a search across company knowledge, an assistant or document extraction often remain too general. That is why, before the technology, it is usually necessary to clarify which sources are reliable and how knowledge is meant to be used in your own processes. If you would like to assess this for your own project, you can make use of a no-obligation initial consultation.
How does tacit knowledge differ from explicit knowledge?
Explicit knowledge is documented, for example in policies, checklists or a requirements specification. Tacit knowledge, by contrast, shows itself in experience, judgement and practical sequence. The two belong together: one is written down, the other is applied in day-to-day work. If you want to collect requirements for this in a more structured way, you will find a good basis in the article on what a requirements specification should contain.
How can tacit knowledge be captured without disrupting day-to-day work?
The best way is not through long theoretical workshops, but through short conversations about real cases. Questions like these are helpful, for example:
- How do you recognise a special case?
- Which information is missing most often?
- Which follow-up question do you almost always ask first?
Short meetings with clear examples usually produce more than abstract process sessions. If you would like to start a discussion straight away, you can book an appointment online.
What role does software play in making tacit knowledge usable?
Software does not automatically make experience-based knowledge visible, but it can turn it into a usable form: as data fields, search logic, prompts, role permissions or documented workflows. This translation becomes especially important when applications are being extended or operated in the Microsoft Cloud. You can find an overview in the section on setup, migration and operations.
Conclusion: turning experience into usable company knowledge
A tacit knowledge example is not just an interesting observation. It often points to specialist knowledge that is critical and must be taken into account in processes, software and AI initiatives.
The practical benefit comes when companies make this knowledge systematically visible without artificially oversimplifying real practice. If you would like to assess similar initiatives, you can also look at examples from practice or, if you already have software in place, plan a look at maintainability and quality.
If you would like to structure such an initiative, a project outline based on your description or a requirements specification can provide a more reliable basis for the next steps. For classifying operating models, the article on SaaS in the business context may also be helpful.
