The expensive search for information
Have you ever wondered how much time is lost searching for information every day? Studies show that professionals often spend several hours a day looking for data — time that is missing for productive tasks. This inefficiency not only causes high costs, but also affects product development and quality assurance. In this blog post, we look at the hidden costs of unstructured data management and show how efficient data management can help reduce these costs through automated data integration and AI-based analytics.
Where is my relevant data?
Whether it's research & development (R&D), process engineering or quality assurance — the problem is omnipresent: finding important data and information costs valuable time every day. Instead of working efficiently, you navigate through countless tables and confusing folder structures.
Every department, every team—and sometimes even every single team member—uses their own methods for naming and storing files. The result? They click through countless folders and subfolders without a uniform structure or standardized naming conventions. This inefficient search slows down processes and causes unnecessary costs.
But that's not all — in addition to local folder structures, relevant data is often distributed across various business systems: CRM, ERP, or specialized databases. Each of these systems uses its own user interface, individual search functions and logins. The result? An increased workload, more complexity and an even more inefficient data search. In the end, important information often remains undetectable — or it only appears after a time-consuming search. This inefficient data collection interrupts the flow of work, causes frustration within the team and slows down important processes.
In addition, found data is often outdated or incomplete, as central data management is missing and systems cannot be updated regularly. To ensure that information is correct and up to date, it is therefore often only necessary to exchange information directly with colleagues. But that means: countless emails, chat messages or additional meetings — a great deal of time that slows down work processes and extends decision-making processes.
The loss of time in figures
According to a study by International Data Corporation (IDC) Employees in Western Europe spend an average of up to 2.5 hours a day searching for information, data and documents — or finding the right contact person. This inefficient use of working time results in a significant loss of productivity and drives up operating costs. The result in detail:
— Extended response times for customer complaints: Delayed responses to complaints mean that customers have to ask multiple questions, which increases workload and resource expenditure. Slow response times put a strain on the customer relationship, require more complex processing and drive up costs.
— Delayed release of production batches: Production delays cause high costs: Production interruptions reduce machine utilization and reduce efficiency. Additional contractual penalties for late deliveries put a strain on customer relationships and can lead to lost orders. Dissatisfied customers switch to competitors in the long term, which can result in lost sales.
— Extended development cycles for new products: Long development times drive up R&D costs as more working hours and additional resources are required. Delayed market launch can result in lost sales and thus cost market share. At the same time, there is an increasing risk that the product will no longer meet current requirements when it enters the market — which requires costly adjustments. Extended development cycles weaken innovative capacity and reduce competitiveness.
— Loss of knowledge when employees leave: Two key factors drive these costs: the loss of specific know-how and long training periods for new employees. The knowledge of experienced specialists is often not documented, meaning that their departure leads to loss of efficiency and an increased potential for errors. New employees need time to train, which lowers productivity. At the same time, experienced team members must take on training, which limits their own work performance.
Reasons for unused data
Critical knowledge and important data therefore often remain unused in the company because they:
- are not uniformly coded or tagged
- can be stored in hard-to-reach places
- available in different file formats or languages
- are “implicit” — i.e. exist only in the minds of experienced employees
- are spread across various departments and locations
- are neither actively searched nor shared for various reasons
Artificial intelligence as a savior — for R&D, process engineering and quality assurance
According to a McKinsey study Generative AI could change collaboration and the use of expertise in particular — especially in areas that could barely be automated up to now. The integration of AI into engineering and R&D processes opens up new opportunities for knowledge management and well-founded decisions. This is essential for the development of new materials, the optimization of existing products and shorter time to market.
AI accelerates solution finding, promotes co-creation and links formal and informal knowledge flows. In addition, it enables hyper-specialized learning, complements project teams in a targeted manner and supports flexible problem-solving approaches. Personalized AI models optimize knowledge acquisition, while predictive AI methods improve experiments and accelerate decision-making processes.
AI — from theory to practice
But there is one key challenge: In order to exploit the full potential of a digital assistant, a solid database is needed.
Only with structured and well-organized data can AI quickly identify relevant information and retrieve it efficiently.
A modern database centralizes data from various sources, integrates it into a uniform structure and creates a reliable basis for data-based decisions — the basis for an efficient, AI-based document search.
Why efficient information search saves costs
Just imagine it: A digital assistant accesses a centralized database and recognizes all relevant information, relationships and correlations in seconds. Instead of wasting time searching, the assistant immediately delivers the required data — in a structured, precise and reliable way. As a result, the often underestimated costs of inefficient information management are drastically reduced — from delayed production approvals to duplication of work and loss of valuable expertise.
The most important savings potential at a glance:
Gathering information faster:
Thanks to structured coding and intelligent tagging, all relevant data can be immediately retrieved in a modern database. This saves valuable time that would otherwise be lost in the time-consuming search and enables you to concentrate on productive tasks.
Increasing efficiency:
An intelligent assistant that understands natural language and seamlessly consolidates data from various sources not only makes knowledge accessible but also directly usable. As a result, customer complaints can be processed more quickly, production batches approved more efficiently and development processes for new products can be accelerated.
Securing knowledge:
Knowledge remains structured and accessible instead of being hidden in silos or being lost when employees leave. This shortens the training period for new team members, preserves valuable know-how and ensures greater efficiency and lower costs.
Shorter development cycles:
Efficient access to relevant data and well-founded decision-making bases shortens development times, accelerates market launches and makes it possible to proactively meet market requirements.
An AI-supported assistant thus serves as an intelligent interface to the database and simplifies the entire processing of information. This not only reduces costs, but also increases flexibility and innovative strength. Faster decisions and more well-founded information open up long-term savings potential and strengthen the company's competitiveness.
The digital assistant — even in your laboratory
The efficient use of data is not a promise for the future, but is already a reality. AI-powered assistants help you quickly find scattered information, optimize processes and make data-based decisions. This is exactly where LabV comes in: Would you like to see how an intelligent assistant can transform your work in R&D, process engineering and quality assurance?