What happens when your experienced service desk employee is off sick or on holiday? Colleagues often must search for answers themselves. Questions go unanswered, and customers wait longer, even though the information they need is often already stored somewhere in the organisation, for example in a CRM-system. The problem is that not everyone knows where to find it.
Wondering whether this applies to your organisation? Look at how often the quality of your work, the service you provide, and your internal processes depend on who happens to be available. If the answer is “too often”, it may be time to take a closer look at how your organisation manages its knowledge infrastructure.
Six signs your knowledge infrastructure needs improvement
You don't need to conduct a full audit to see whether relying on individual employees for knowledge poses a risk to your organisation. The signs often become apparent in day-to-day operations. Do any of the following situations sound familiar? If so, there may be room for improvement.
Service desk tickets keep covering the same questions: Employees raise tickets because they can't find the answers they need or can't find them quickly enough. These are often straightforward questions, and the answers already exist somewhere in the organisation. The problem is making that knowledge easy to find.
New employees take weeks to become fully productive: If new employees spend most of their onboarding time learning how to navigate different knowledge sources, that's a clear sign that your knowledge infrastructure isn't working as effectively as it could.
Employees rarely use knowledge systems: If employees make little use of the systems that contain information relevant to their work, it's a sign that this knowledge isn't being accessed or put to good use.
Problems keep arising that could easily be resolved: If many issues could be resolved quickly by an employee who has the right information, this may point to problems with how accessible and discoverable that information is.
Knowledge is recreated instead of reused: Is new documentation created for every project? Your organisation may be spending a significant amount of time duplicating work. When project documentation is stored in a structured way and is easy for everyone to find, teams can build on existing knowledge and move forward much faster.
Employees don't trust the answers provided by AI agents: The problem doesn't always lie with the AI model itself. Often, it comes down to the information the agent uses to generate its answers. If that information is outdated or scattered across different systems, the agent can't provide reliable answers. Just like employees, AI needs access to the right information, in a place where it can be found.
Why discoverability is often overlooked in growing organisations
In many organisations, the knowledge landscape has evolved over time. Documents may once have been stored on a shared drive, but teams now work with a range of systems, including SharePoint, Confluence, CRM platforms, ticketing systems and HR software. Each system has its own purpose and does its job well. However, little attention has been paid to how the knowledge in these systems fits together, leaving information scattered across the organisation.
On paper, all the information is there. In practice, employees often rely on their own folders, individual documents or colleagues who know where to find something. This works if everyone is available and knows how the organisation operates. But as teams grow or employees leave, it becomes harder to find and transfer that knowledge.
What's missing is discoverability: the ability to find, understand and use knowledge across different systems. This applies not only to employees, but also to customers and AI assistants. It may sound straightforward, but discoverability is often overlooked as organisations grow.
The figures show just how much time is lost. According to the Gartner Digital Worker Experience Survey, 47% of digital workers struggle to find the information they need to do their jobs effectively. The Microsoft Work Trend Index also shows that employees spend an average of 27% of their working day searching for information. This doesn't just waste time. It also affects an organisation's capacity, quality of work and potential for growth.
At the same time, more organisations are deliberately adopting a composable architecture. They combine specialised tools to remain flexible and build an IT landscape that can scale with their needs. It's a logical choice, but it comes with a trade-off. Every system has its own way of storing and searching information, as well as managing access. The more systems an organisation uses, the more knowledge it has available, but the harder it becomes to bring that knowledge together and put it to use.
That's the knowledge paradox: organisations have more information at their disposal than ever, but finding and using that information doesn't automatically become easier.
How enterprise AI search solves the problem at its source
The good news is that you don't need to replace all your existing systems to address this problem. That's where Enterprise AI Search comes in. Most organisations already have an IT infrastructure in which applications, databases and legacy systems are connected through APIs and event streams. You can keep that foundation in place. Enterprise AI Search builds on it, making information from different systems searchable from a single access point.
Think of it as Google for your organisation. Instead of searching through multiple systems and documents yourself, you can find relevant information in one place, even when it's spread across different sources.
How does it work? Two technologies play an important role:
Embedding models convert questions and information into mathematical representations of meaning. This allows the system to search based on what someone means, rather than relying solely on exact keywords. It can therefore find relevant information even when a question is phrased differently from the original document.
RAG (Retrieval-Augmented Generation) connects AI to an organisation's own knowledge sources, such as documents, databases and business systems. The AI retrieves relevant information from these sources and uses it to formulate an answer. This means the answer is grounded in the organisation's own information, rather than relying solely on what the model learned during training.
Three features that make enterprise AI search reliable
For employees to trust AI, they need to be confident that its answers are accurate. That means the information AI relies on must be reliable, and employees need to be able to check where an answer comes from. Three features are particularly important:
Accuracy: Hybrid retrieval combines two search methods: semantic search and keyword search. This helps the system find relevant information even when employees phrase their questions in different ways.
Access control: AI agents must respect each employee's access permissions. This ensures that people only see information they're authorised to access. The connected source systems and their access controls determine which information is available. The exact implementation depends on the integrations and access control mechanisms in place.
Source citations: Employees can see which sources an answer is based on. This allows them to verify the information, consult the original document or ask more targeted follow-up questions. It also helps build trust in the AI agent.
The result? Employees find the information they need faster and have more time for work that adds value. New colleagues can work independently sooner, without constantly relying on experienced employees. And because everyone works with the same up-to-date information, service delivery becomes less dependent on who's available at any given moment.
That means fewer unnecessary handovers, a better customer experience and greater control over compliance with rules and procedures.
Getting started with enterprise AI search doesn't have to mean a major transformation programme
The six signs in this article can help you assess how well your organisation's knowledge infrastructure is working. Do several of them sound familiar, and would you like to address them? Start by mapping where knowledge is stored across your organisation and how easily employees can find it.
Which information is stored in which systems? Who has access to it? And where are employees losing unnecessary time searching for and verifying information? Once you have a clear picture, you can identify the biggest opportunities, determine what governance is needed and work towards a solution that can scale with your organisation, one step at a time.
Curious about where your organisation stands?
We'd be happy to assess your current situation together in a focused session. You'll gain insight into the opportunities available and the next steps you can take, without committing to anything upfront.